{"id": "bmbench-b74496c1e48662869171", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAncestral sequence reconstruction (ASR) offers a revolutionary approach to resurrect functional proteins, yet its potential in transporter engineering remains underexplored. Here, we pioneered the application of ASR to reconstructing ancestral xylose transporters, addressing the persistent challenge of glucose-mediated inhibition of xylose uptake in Saccharomyces cerevisiae during xylose co-fermentation. Through rigorous ASR analysis, we reconstructed ancestral xylose transporters (Xt) and selected two candidates—Xt3 (approximately 140 million years old) and Xt7 (approximately 40 million years old)—based on their phylogenetic positioning, degree of sequence divergence from extant homologs, and predicted structural integrity. Functional characterization demonstrated that both Xt3 and Xt7 significantly enhance xylose uptake efficiency and mitigate glucose-induced repression. In fermentation experiments with mixed sugars (40 g/L xylose and 40 g/L glucose) within 72 h, recombinant S. cerevisiae expressing Xt3 achieved 22.75 g/L xylose consumption, surpassing the benchmark N326F Xltr1p (16.22 g/L) by 40.27% and outperforming Xt7 (21.36 g/L) by 6.51%, highlighting Xt3 as the most efficient transporter. Molecular docking suggested a potentially more favorable binding mode for xylose in the ancestral transporters (binding affinity: −3.68 kcal/mol for Xt3 vs. −3.15 kcal/mol for N326F Xltr1p ). Molecular dynamics simulations further demonstrated that the ancestral transporters formed complexes with xylose that exhibited faster convergence to a stable state and maintained significantly greater conformational stability throughout the simulation compared to the N326F Xltr1p complex. These computational insights provide a plausible structural basis for their enhanced performance. This work contributes to the advancement of lignocellulosic biorefinery technology and provides a practical reference for resurrecting other valuable proteins using ASR’. The online version contains supplementary material available at 10.1186/s40643-025-00995-1. Keywords: Ancestral sequence reconstruction, Xylose transporter, Glucose/xylose co-utilization, Saccharomyces cerevisiae\n\nCandidates:\nA. Ancestral sequence reconstruction (ASR) offers a revolutionary approach to resurrect functional proteins, yet its potential in transporter engineering remains underexplored.\nB. Functional characterization demonstrated that both Xt3 and Xt7 significantly enhance xylose uptake efficiency and mitigate glucose-induced repression.\nC. Through rigorous ASR analysis, we reconstructed ancestral xylose transporters (Xt) and selected two candidates—Xt4 (approximately 140 million years old) and Xt7 (approximately 40 million years old)—based on their phylogenetic positioning, degree of sequence divergence from extant homologs, and predicted structural integrity.\nD. Functional characterization demonstrated that both Xt4 and Xt7 significantly enhance xylose uptake efficiency and mitigate glucose-induced repression.\nE. The evidence does not state that ancestral sequence reconstruction (ASR) offers a revolutionary approach to resurrect functional proteins, yet its potential in transporter engineering remains underexplored.\nF. The evidence does not state that here, we pioneered the application of ASR to reconstructing ancestral xylose transporters, addressing the persistent challenge of glucose-mediated inhibition of xylose uptake in Saccharomyces cerevisiae during xylose co-fermentation.\nG. Here, we pioneered the application of ASR to reconstructing ancestral xylose transporters, addressing the persistent challenge of glucose-mediated inhibition of xylose uptake in Saccharomyces cerevisiae during xylose co-fermentation.\nH. Through rigorous ASR analysis, we reconstructed ancestral xylose transporters (Xt) and selected two candidates—Xt3 (approximately 140 million years old) and Xt7 (approximately 40 million years old)—based on their phylogenetic positioning, degree of sequence divergence from extant homologs, and predicted structural integrity.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12770209", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12770209/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f91baaf498652635c7f3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nNatural products (NPs) and their analogues have long underpinned therapies in humans, animals, and plants health, yet, discovering truly novel scaffolds remains a formidable challenge, even with the enormous diversity offered. Over the last two decades, breakthroughs in bioinformatics, cheminformatics, advanced analytical methods, synthetic biology toolkits, and optimized microbial culture have surmounted many of the bottlenecks that stalled NP research in the 1990s and 2000s. Researchers now deploy innovative extraction and purification protocols alongside high-throughput dereplication tools to fish trace metabolites out of complex matrices. These combined approaches not only enable the discovery and rigorous characterization of biosynthesized metabolites, bio-transformed analogues and new chemical entities but also allow precise tuning of biosynthetic gene clusters (BGCs) and culture conditions- modulation and optimization, dramatically improving yield, scalability, and cost-efficiency. Several of these newly unearthed compounds exhibit unique bioactivities that directly inspire drug-development programs against metabolic disorders, cancer drug resistance, and infectious diseases. In this review, we present an up-to-date, concise roadmap of natural product discovery (NPD), majorly covering strategies for awakening silent BGCs, genome mining, and late-stage diversification systems, and we discuss the current limitations and perspectives of rational NPD. Keywords: Natural products, Culturing modulation, Unexplored reservoirs, Genome mining, Natural product diversification\n\nCandidates:\nA. The evidence does not state that natural products (NPs) and their analogues have long underpinned therapies in humans, animals, and plants health, yet, discovering truly novel scaffolds remains a formidable challenge, even with the enormous diversity offered.\nB. The evidence does not state that these combined approaches not only enable the discovery and rigorous characterization of biosynthesized metabolites, bio-transformed analogues and new chemical entities but also allow precise tuning of biosynthetic gene clusters (BGCs) and culture conditions- modulation and optimization, dramatically improving yield, scalability, and cost-efficiency.\nC. The evidence does not state that researchers now deploy innovative extraction and purification protocols alongside high-throughput dereplication tools to fish trace metabolites out of complex matrices.\nD. Over the last two decades, breakthroughs in bioinformatics, cheminformatics, advanced analytical methods, synthetic biology toolkits, and optimized microbial culture have surmounted many of the bottlenecks that stalled NP research in the 1990s and 2000s.\nE. Over the last two decades, breakthroughs in bioinformatics, cheminformatics, advanced analytical methods, synthetic biology toolkits, and optimized microbial culture have surmounted many of the bottlenecks that stalled NP research in the 1991s and 2000s.\nF. Researchers now deploy innovative extraction and purification protocols alongside high-throughput dereplication tools to fish trace metabolites out of complex matrices.\nG. Natural products (NPs) and their analogues have long underpinned therapies in humans, animals, and plants health, yet, discovering truly novel scaffolds remains a formidable challenge, even with the enormous diversity offered.\nH. These combined approaches not only enable the discovery and rigorous characterization of biosynthesized metabolites, bio-transformed analogues and new chemical entities but also allow precise tuning of biosynthetic gene clusters (BGCs) and culture conditions- modulation and optimization, dramatically improving yield, scalability, and cost-efficiency.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12775259", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12775259/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-02bbe5a333e106985729", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nHigh-yield influenza virus production is essential for efficient vaccine manufacturing to support global demands. Using Madin-Darby canine kidney (MDCK) cells to produce influenza viruses is an attractive alternative to the conventional method of manufacturing vaccines using embryonated eggs. MDCK cells exhibit heterogeneity which can impact viral yields. However, the factors driving the variation between MDCK cells are not fully understood. Utilizing an untargeted liquid chromatography-mass spectrometry lipidomic approach, we investigated two proprietary MDCK clones (C59 and C113) provided by Sartorius (Germany) that differ in biochemical and viral production properties and examined their lipid profiles and dynamics upon influenza A virus (IAV) infection between 24 and 72 h. C113, a high-yield clone, displayed elevated levels across all lipid classes, aside from ether lipids compared to C59, a clone with superior growth properties. IAV infection in clone C59 and C113 displayed key differences, specifically triacylglycerols. Analysis of progeny virions from C59 and C113 clones revealed subtle differences with a positive correlation in lipid profile ( R 2 = 0.77), suggesting similar lipid raft domains between clones. Overall, these findings highlight specific cellular lipid signatures associated with high-yield production and demonstrate the value of integrating lipidomics methods into biomanufacturing pipelines, providing complimentary quality assurance markers. The online version contains supplementary material available at 10.1038/s41598-025-33499-1. Subject terms: Biochemistry, Biological techniques, Biotechnology, Microbiology", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12783198", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12783198/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-20997908fce0f0160eee", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\n2-Hydroxy-4-Methoxybenzaldehyde (2H4MB) is a valuable aromatic compound with applications in flavour, fragrance, and pharmaceuticals. Because of its endangered status and root-specific accumulation, its production in native plants is restricted. In order to increase 2H4MB yield, this study emphasises recent developments in metabolic engineering, synthetic biology, in vitro culture methods, and AI-assisted route prediction. This review discussed about how CRISPR-based genome editing can be used to modify important biosynthetic genes and regulatory components, as well as how predictive machine learning techniques can be used to improve production conditions. Inadequate genetic resources, poorly understood biosynthetic pathways, and a dearth of reliable transformation systems are among the present constraints. The work highlights the importance of using integrative plant biotechnology techniques to fully realise the industrial and medicinal potential of this underutilised chemical.\n\nCandidates:\nA. Because of its endangered status and root-specific accumulation, its production in native plants is restricted.\nB. This review discussed about how CRISPR-based genome editing cannot be used to modify important biosynthetic genes and regulatory components, as well as how predictive machine learning techniques can be used to improve production conditions.\nC. In order to increase 3H4MB yield, this study emphasises recent developments in metabolic engineering, synthetic biology, in vitro culture methods, and AI-assisted route prediction.\nD. 3-Hydroxy-4-Methoxybenzaldehyde (2H4MB) is a valuable aromatic compound with applications in flavour, fragrance, and pharmaceuticals.\nE. 2-Hydroxy-4-Methoxybenzaldehyde (2H4MB) is a valuable aromatic compound with applications in flavour, fragrance, and pharmaceuticals.\nF. This review discussed about how CRISPR-based genome editing can be used to modify important biosynthetic genes and regulatory components, as well as how predictive machine learning techniques can be used to improve production conditions.\nG. In order to increase 2H4MB yield, this study emphasises recent developments in metabolic engineering, synthetic biology, in vitro culture methods, and AI-assisted route prediction.\nH. Because of its endangered status and root-specific accumulation, its production in native plants is not restricted.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12786920", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12786920/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8168b762b1aed439cb25", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe exploration and multiscale manufacturing in outer space hold vital significance Chemical and biological nano/micro/meso-scale manufacturing offer strategies to address challenges Emerging advances encompass novel manufacturing technologies and resource utilization strategies across orbital space stations, the Moon, Mars, and asteroids Emerging technologies like synthetic biology and artificial intelligence are discussed Key innovations, cross-disciplinary applications, and limitations are highlighted Keywords: In-space manufacturing, Biomanufacturing, Chemical manufacturing, Long-term space mission, In-situ resource utilization\n\nCandidates:\nA. The exploration and multiscale manufacturing in outer space hold vital significance Chemical and biological nano/micro/meso-scale manufacturing offer strategies to address challenges Emerging advances encompass novel manufacturing technologies and resource utilization strategies across orbital space stations, the Moon, Mars, and asteroids Emerging technologies like synthetic biology and artificial intelligence are not discussed Key innovations, cross-disciplinary applications, and limitations are highlighted Keywords: In-space manufacturing, Biomanufacturing, Chemical manufacturing, Long-term space mission, In-situ resource utilization\nB. The exploration and multiscale manufacturing in outer space hold vital significance Chemical and biological nano/micro/meso-scale manufacturing offer strategies to address challenges Emerging advances encompass novel manufacturing technologies and resource utilization strategies across orbital space stations, the Moon, Mars, and asteroids Emerging technologies like synthetic biology and artificial intelligence are discussed Key innovations, cross-disciplinary applications, and limitations are highlighted Keywords: In-space manufacturing, Biomanufacturing, Chemical manufacturing, Long-term space mission, In-situ resource utilization", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12791114", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12791114/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9d9eb8303a54f42b38e4", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBone fractures represent a significant global healthcare burden. Although fractures typically heal on their own, some fail to regenerate properly, leading to nonunion, a condition that causes prolonged disability, morbidity, and mortality. The challenge of treating nonunion fractures is further complicated in patients with underlying bone disorders where systemic and local factors impair bone healing. Traditional treatment approaches, including autografts, allografts, xenografts, and synthetic biomaterials, face limitations such as donor site pain, immune rejection, and insufficient mechanical strength, underscoring the need for alternative strategies. Biologic therapies have emerged as promising tools to enhance bone regeneration by leveraging the body’s natural healing processes. This review explores the critical role of conventional and emerging biologics in fracture healing. We categorize biologic therapies into protein-based treatments, gene and transcript therapies, small molecules, peptides, and cell-based therapies, highlighting their mechanisms of action, advantages, and clinical relevance. Finally, we examine the potential applications of biologics in treating fractures associated with bone disorders such as osteoporosis, osteogenesis imperfecta, rickets, osteomalacia, Paget’s disease, and bone tumors. By integrating biologic therapies with existing biomaterial-based strategies, these innovative approaches have the potential to transform clinical management and improve outcomes for patients with difficult-to-heal fractures. Subject terms: Bone, Pathogenesis\n\nCandidates:\nA. Traditional treatment approaches, including autografts, allografts, xenografts, and synthetic biomaterials, face limitations such as donor site pain, immune rejection, and insufficient mechanical strength, underscoring the need for alternative strategies.\nB. The evidence does not state that traditional treatment approaches, including autografts, allografts, xenografts, and synthetic biomaterials, face limitations such as donor site pain, immune rejection, and insufficient mechanical strength, underscoring the need for alternative strategies.\nC. The evidence does not state that although fractures typically heal on their own, some fail to regenerate properly, leading to nonunion, a condition that causes prolonged disability, morbidity, and mortality.\nD. The challenge of treating nonunion fractures is not further complicated in patients with underlying bone disorders where systemic and local factors impair bone healing.\nE. Bone fractures represent a significant global healthcare burden.\nF. The challenge of treating nonunion fractures is further complicated in patients with underlying bone disorders where systemic and local factors impair bone healing.\nG. The evidence does not state that bone fractures represent a significant global healthcare burden.\nH. Although fractures typically heal on their own, some fail to regenerate properly, leading to nonunion, a condition that causes prolonged disability, morbidity, and mortality.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12791149", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12791149/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fea6d66dfc85c3ad3bcb", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nThe production of odd-chain fatty acids (OCFAs) is gaining increasing importance due to their diverse applications in food, chemical, and biofuel industries. These fatty acids, which are relatively rare in nature, can be produced from renewable carbon sources through microbial fermentation processes. This review covers the significance of OCFAs in the market and their occurrence, followed by a detailed exploration of their production in mixed and single strain cultures. Specifically, the anaerobic fermentation (AF) conditions and feedstocks used to produce short OCFAs (SOCFAs), such as propionic, valeric, and heptanoic acids are discussed. Additionally, the production of long OCFAs (LOCFAs) by single strains is focusing on yeast, bacteria, and microalgae. Novel approaches for LOCFAs generation from waste carbon sources are also reviewed. This work delves both into the manipulation of microbial communities covering bioaugmentation and process optimization for bioenrichment in open mixed cultures and genetic manipulation in single-strain systems. Finally, the potential for scalable and sustainable production of OCFAs through microbial processes is discussed, as well as the technological advances needed to optimize these pathways.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12799634", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12799634/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fb6084f65e53cc3d0f45", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"31%\", \"66%\", \"39%\", \"59%\", \"30%\", \"67%\", \"40%\", \"58%\"]\n\nEvidence:\nPalm oil is the world’s most widely used vegetable oil, with a sizeable impact on the environment. As an alternative, microalgae are considered oil producers since they produce a variety of fatty acids (FA) depending on growth conditions. A collection of ten microalgae strains naturally producing oils similar in composition to palm oil was selected, and the effects of cultivation regime and varying light intensity on their growth and FA production and composition were analysed. To achieve high biomass density as well as total fatty acid (TFA) content, the optimum irradiance of 400 µmol photons m −2 s −1 in a photoautotrophic regime was determined for most of the strains. The growth rates of Scenedesmus and Desmodesmus strains in general were approximately twice as high as Chlamydomonas . The highest TFA content was found in S. obliquus CCALA 455 and D. subspicatus CCALA 467, grown photoautotrophically, reaching the values of about 66% and 58% of their dry weight, respectively. Moreover, the content of palmitic (PA), oleic (OA) and linoleic acid (LA) of about 39%, 30% and 14% of TFA, respectively, determined in D. subspicatus CCALA 467 was closest to that in palm oil (44% of PA, 39% of OA and 10% of LA). Eight of the ten microalgae strains were capable of heterotrophic growth, although their production under this regime has not been considered suitable in terms of TFA and individual FA content. • The optimum irradiance of 400 µmol photons m −2 s −1 was determined • CCALA 467 produces selected FAs in amounts close to those in palm oil • TFA content (% of dry weight) in CCALA 467 is 1.6-fold higher than in the palm The online version contains supplementary material available at 10.1007/s00253-025-13682-0. Keywords: Microalga, Biomass, Photoautotrophic and heterotrophic cultivation, Fatty acid, Palm oil", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12799715", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12799715/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2adf5dcd977f3f5f20c2", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nToxoplasmosis is a widespread zoonotic disease that poses risks to pregnant women and immunocompromised individuals. Despite considerable efforts, no licensed vaccines are currently available for humans or animals. Rational vaccine design increasingly relies on immunoinformatics approaches to identify immunodominant epitopes and key immunological features. This study aimed to characterise the Toxoplasma gondii ‐secreted protein with an altered thrombospondin repeat (TgSPATR) using immunoinformatics tools to evaluate its suitability as a vaccine candidate. A comprehensive panel of bioinformatics servers was used to predict allergenicity, solubility, antigenicity, secondary and tertiary structures, post‐translational modification (PTM) regions, and B‐ and T‐cell epitopes, followed by in silico immune simulation. TgSPATR consists of 534 amino acids with an estimated molecular weight of ∼57 kDa. The aliphatic index (65.71) and GRAVY score (–0.507) indicate moderate thermostability and an overall hydrophilic nature. Additionally, a total of 120 PTM sites were predicted, including phosphorylation, O ‐ and N ‐glycosylation, and palmitoylation sites. Secondary structure analysis (GOR IV, SOPMA, and NetSurfP‐3.0) revealed a predominance of random coils. Moreover, multiple servers (BcePred, SVMTriP, ABCpred, IEDB, ElliPro, and CTLpred) identified several high‐scoring B‐ and T‐cell epitopes capable of binding MHC class I and II molecules. According to SAVES v6.1, 74.1% and 93.8% of the residues of the initial and refined 3D models were located in favoured regions, indicating improved structural quality after refinement. In line with this, the ERRAT score also increased from 89.557 to 95.046. TgSPATR was predicted to be immunogenic and non‐allergenic. Finally, virtual immune simulation using the C‐ImmSim server showed that TgSPATR can elicit both humoral and cellular immune responses following three injections. This study provides foundational evidence that TgSPATR possesses key immunogenic properties and may serve as a promising vaccine candidate against acute and chronic toxoplasmosis. Nonetheless, wet‐lab experiments are required to validate these computational findings. Keywords: bioinformatics, in silico, secreted protein with an altered thrombospondin repeat (SPATR), Toxoplasma gondii , vaccine\n\nCandidates:\nA. Toxoplasmosis is a widespread zoonotic disease that poses risks to pregnant women and immunocompromised individuals.\nB. Despite considerable efforts, no licensed vaccines are currently available for humans or animals.\nC. Toxoplasmosis is not a widespread zoonotic disease that poses risks to pregnant women and immunocompromised individuals.\nD. Despite considerable efforts, no licensed vaccines are not currently available for humans or animals.\nE. Rational vaccine design increasingly relies on immunoinformatics approaches to identify immunodominant epitopes and key immunological features.\nF. The evidence does not state that this study aimed to characterise the Toxoplasma gondii ‐secreted protein with an altered thrombospondin repeat (TgSPATR) using immunoinformatics tools to evaluate its suitability as a vaccine candidate.\nG. This study aimed to characterise the Toxoplasma gondii ‐secreted protein with an altered thrombospondin repeat (TgSPATR) using immunoinformatics tools to evaluate its suitability as a vaccine candidate.\nH. The evidence does not state that rational vaccine design increasingly relies on immunoinformatics approaches to identify immunodominant epitopes and key immunological features.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12800914", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12800914/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-928bca7b4d485aa18d2a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTo guarantee consistent quality of therapeutic proteins, the relationship between manufacturing process parameters and glycosylation profiles must be investigated and understood. The most important manufacturing step to investigate is the cell culture unit operation, where glycoprotein structure is highly dependent on raw materials, cell line genetics, and process control ranges. Because of the critical role glycosylation plays in certain drug mechanisms of action, the relationship between specific process inputs and glycosylation have been documented extensively. However, despite the extensive body of published work, general relationships between different cell culture conditions and glycosylation profiles remain fragmented across diverse studies, hindering systematic analysis and data-driven decision-making. To better elucidate these general relationships from published research, we introduce an innovative framework that leverages text mining and knowledge graph technologies to automatically extract, integrate, and visualize complex relationships from scientific literature, enabling actionable insights for biopharmaceutical process (bioprocess) development. Our methodology centers on the design and development of a specialized text-mining pipeline to extract and quantify relationships between cell culture conditions (raw materials, cell line genetics, and process control ranges) and glycosylation profiles from unstructured scientific literature. To enhance precision, we implement a dual normalization strategy: 1) dictionary-based concept standardization to reconcile term variants, and 2) ontological classification to organize entities into hierarchically structured categories. These curated relationships are then systematically integrated into a knowledge graph, which not only captures direct parameter-outcome associations but also reveals higher-order indirect connection through graph, providing a comprehensive view of bioprocess interactions. We present an intuitive web-based interface that enables researchers to dynamically explore and visualize complex bioprocess relationships through interactive queries. The system demonstrates robust performance with an 88% F1-score in relation extraction, effectively revealing hidden relationships between process parameters and glycan attributes. By combining scalable knowledge graph technology with interpretable analytics, our solution empowers pharmaceutical researchers to optimize therapeutic glycan profiles and accelerate manufacturing process development. This advancement represents a significant step forward in data-driven bioprocess optimization.\n\nCandidates:\nA. The evidence does not state that because of the critical role glycosylation plays in certain drug mechanisms of action, the relationship between specific process inputs and glycosylation have been documented extensively.\nB. The most important manufacturing step to investigate is the cell culture unit operation, where glycoprotein structure is highly dependent on raw materials, cell line genetics, and process control ranges.\nC. The evidence does not state that however, despite the extensive body of published work, general relationships between different cell culture conditions and glycosylation profiles remain fragmented across diverse studies, hindering systematic analysis and data-driven decision-making.\nD. The evidence does not state that to guarantee consistent quality of therapeutic proteins, the relationship between manufacturing process parameters and glycosylation profiles must be investigated and understood.\nE. To guarantee consistent quality of therapeutic proteins, the relationship between manufacturing process parameters and glycosylation profiles must be investigated and understood.\nF. The most important manufacturing step to investigate is not the cell culture unit operation, where glycoprotein structure is highly dependent on raw materials, cell line genetics, and process control ranges.\nG. Because of the critical role glycosylation plays in certain drug mechanisms of action, the relationship between specific process inputs and glycosylation have been documented extensively.\nH. However, despite the extensive body of published work, general relationships between different cell culture conditions and glycosylation profiles remain fragmented across diverse studies, hindering systematic analysis and data-driven decision-making.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12803468", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12803468/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-56869e138b108110fa55", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSecretory protein production by microbial hosts simplifies product recovery and is therefore preferred over intracellular production. Efficient secretion of heterologous proteins by bacteria requires the identification of optimal signal peptides (SPs), a step that often limits process development. Using Corynebacterium glutamicum as a model host, we established a modular cloning system enabling rapid assembly of expression plasmids for secretory protein production. Screening a library of 30 individually cloned endogenous SPs with a fungal cutinase as target protein demonstrated that several native SPs achieved substantially higher secretion levels than the widely used Bacillus subtilis NprE reference SP. To accelerate SP discovery, we developed a one‐pot approach in which C. glutamicum was directly transformed with a single modular cloning mixture containing all 30 SPs. Combined with the AutoBioTech high‐throughput platform for cultivation, harvesting, and protein quantification, this strategy enabled screening of several hundred clones in parallel. Superior SPs were rapidly identified not only for cutinase but also for four polyethylene terephthalate hydrolases (PETases). This streamlined workflow significantly reduces time and cost for selecting effective SPs and provides a versatile platform for advancing secretory protein production in C. glutamicum .\n\nCandidates:\nA. Efficient secretion of heterologous proteins by bacteria requires the identification of optimal signal peptides (SPs), a step that often limits process development.\nB. Screening a library of 30 individually cloned endogenous SPs with a fungal cutinase as target protein demonstrated that several native SPs achieved substantially higher secretion levels than the widely used Bacillus subtilis NprE reference SP.\nC. Screening a library of 31 individually cloned endogenous SPs with a fungal cutinase as target protein demonstrated that several native SPs achieved substantially higher secretion levels than the widely used Bacillus subtilis NprE reference SP.\nD. Using Corynebacterium glutamicum as a model host, we established a modular cloning system enabling rapid assembly of expression plasmids for secretory protein production.\nE. The evidence does not state that efficient secretion of heterologous proteins by bacteria requires the identification of optimal signal peptides (SPs), a step that often limits process development.\nF. The evidence does not state that using Corynebacterium glutamicum as a model host, we established a modular cloning system enabling rapid assembly of expression plasmids for secretory protein production.\nG. Secretory protein production by microbial hosts simplifies product recovery and is therefore preferred over intracellular production.\nH. Secretory protein production by microbial hosts simplifies product recovery and is not therefore preferred over intracellular production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12810402", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12810402/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fdd82a39d20e76e7eeb1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAntimicrobial resistance is a growing global health crisis, responsible for nearly five million deaths annually and projected to double by 2050 as conventional antibiotics fail against multidrug-resistant pathogens. AMR is aggravated by antibiotic misuse, weak regulations, inadequate prevention, high treatment costs, and the limited discovery of new antimicrobials. In this context, antimicrobial peptides, including natural, synthetic, and computationally designed variants, have emerged as promising alternatives. AMPs display broad-spectrum antibacterial, antifungal, antiviral, antiparasitic, antibiofilm, and immunomodulatory activities, with a lower tendency to induce resistance. Their mechanisms include membrane disruption, intracellular targeting, immune modulation, and selective binding to negatively charged microbial membranes. Structural features such as α-helices, β-sheets, cyclic motifs, and post-translational modifications enhance potency and specificity. Recent advances in chemical modification, recombinant expression systems, nanotechnology, and AI-driven computational approaches have improved AMP stability, bioavailability, and therapeutic efficacy. Synthetic derivatives like innate defense regulators and conjugated AMPs further enhance immunomodulatory properties and reduce toxicity, while combination therapies increase effectiveness. Challenges remain, including degradation, short half-life, production costs, and microbial defenses such as biofilms and efflux pumps. Nevertheless, high-throughput sequencing and screening, structural biology, and structure–activity relationship studies continue to accelerate AMP development, positioning them as vital next-generation therapeutics against AMR.\n\nCandidates:\nA. Antimicrobial resistance is a growing global health crisis, responsible for nearly five million deaths annually and projected to double by 2051 as conventional antibiotics fail against multidrug-resistant pathogens.\nB. AMR is aggravated by antibiotic misuse, weak regulations, inadequate prevention, high treatment costs, and the limited discovery of new antimicrobials.\nC. The evidence does not state that in this context, antimicrobial peptides, including natural, synthetic, and computationally designed variants, have emerged as promising alternatives.\nD. In this context, antimicrobial peptides, including natural, synthetic, and computationally designed variants, have emerged as promising alternatives.\nE. The evidence does not state that aMPs display broad-spectrum antibacterial, antifungal, antiviral, antiparasitic, antibiofilm, and immunomodulatory activities, with a lower tendency to induce resistance.\nF. AMR is not aggravated by antibiotic misuse, weak regulations, inadequate prevention, high treatment costs, and the limited discovery of new antimicrobials.\nG. AMPs display broad-spectrum antibacterial, antifungal, antiviral, antiparasitic, antibiofilm, and immunomodulatory activities, with a lower tendency to induce resistance.\nH. Antimicrobial resistance is a growing global health crisis, responsible for nearly five million deaths annually and projected to double by 2050 as conventional antibiotics fail against multidrug-resistant pathogens.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12813028", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12813028/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-98bb2f850e8c5dd56836", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nHemophilia is an inherited disorder characterized by impaired blood clotting caused by mutations in the genes responsible for producing coagulation factor (F) VIII (hemophilia A, HA) or FIX (hemophilia B, HB). Current treatment primarily relies on replacement therapy, involving frequent and costly infusions of FVIII or FIX concentrates. While effective, these treatments come with the risk of developing neutralizing antibodies (inhibitors) against the infused factor. In recent years, non‐factor replacement therapies have emerged as innovative treatment options, offering enhanced efficacy especially for patients with inhibitors. Despite their advantages, these approaches still fall short of providing a definitive, long‐term cure. Since hemophilia is a monogenic disease, it presents an excellent opportunity for cell and gene therapy approaches aimed at achieving durable treatment and potentially a cure. Over the past three decades, remarkable advancements have been made in hemophilia gene therapy, culminating in the approval of Valoctocogene roxaparvovec (ROCTAVIAN, AAV‐FVIII) and Etranacogene dezaparvovec (HEMGENIX, AAV‐FIX) for patients with severe HA and HB, respectively. Nevertheless, gene therapy poses questions regarding its long‐term efficacy and safety. This review synthesizes findings from clinical trials, addresses persistent challenges in hemophilia gene therapy, and underscores the biological constraints and limitations inherent to viral vector‐based approaches.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12813738", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12813738/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-71e83d2bf4bd3d05ca11", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe A-Cell Case Study published by the “Alliance of Regenerative Medicine” illustrates how Quality-by Design can be applied to the manufacturing of Advanced Therapeutical Medicinal Products (ATMPs), using Chimeric Antigen Receptor (CAR)-T cell therapy as a ‘model’ process. However, no emphasis is given to different degrees of automation in this study. CAR-T cell therapies have been developed for various forms of leukemia, such as Acute Lymphoblastic Leukemia (ALL) or Non Hodgkin-Lymphoma (NHL). As more CAR-T cell therapies reach market approval and are being considered as first- or second line treatments, the economic efficiency and scalability of the chosen production modality become increasingly critical. Currently, academic and industrial manufacturers employ a range of approaches, from fully manual and open processing to closed and automated systems. New technologies, investments and cleanroom space requirements must be considered to assess economic and spatial efficiency in cell therapy manufacturing. This study analyses the costs and space requirements of different production modalities for autologous CAR-T cell production. The analysis shows that a higher degree of automation can reduce manufacturing costs by lowering personnel costs, cleanroom grade requirements and spatial footprint. It emphasizes the importance of maximizing cleanroom efficiency to support the scalable production of cell therapies as clinical demand grows. These results underscore the need for both industry and academia to consider automated production as a strategic approach to optimize resource use in CAR-T cell manufacturing.\n\nCandidates:\nA. The evidence does not state that cAR-T cell therapies have been developed for various forms of leukemia, such as Acute Lymphoblastic Leukemia (ALL) or Non Hodgkin-Lymphoma (NHL).\nB. However, no emphasis is not given to different degrees of automation in this study.\nC. As more CAR-T cell therapies reach market approval and are being considered as first- or second line treatments, the economic efficiency and scalability of the chosen production modality become increasingly critical.\nD. CAR-T cell therapies have been developed for various forms of leukemia, such as Acute Lymphoblastic Leukemia (ALL) or Non Hodgkin-Lymphoma (NHL).\nE. As more CAR-T cell therapies reach market approval and are not being considered as first- or second line treatments, the economic efficiency and scalability of the chosen production modality become increasingly critical.\nF. However, no emphasis is given to different degrees of automation in this study.\nG. The A-Cell Case Study published by the “Alliance of Regenerative Medicine” illustrates how Quality-by Design cannot be applied to the manufacturing of Advanced Therapeutical Medicinal Products (ATMPs), using Chimeric Antigen Receptor (CAR)-T cell therapy as a ‘model’ process.\nH. The A-Cell Case Study published by the “Alliance of Regenerative Medicine” illustrates how Quality-by Design can be applied to the manufacturing of Advanced Therapeutical Medicinal Products (ATMPs), using Chimeric Antigen Receptor (CAR)-T cell therapy as a ‘model’ process.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12816348", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12816348/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-df40c21e6d873d7a8788", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nEscherichia coli ( E. coli ) has long served as a versatile workhorse for recombinant protein production. As synthetic biology expands the demand for coordinated expression of multiple genes, co-expression systems in E. coli have evolved from basic dual-gene constructs to programmable, polygenic expression platforms. This review critically examines the major strategies enabling multigene co-expression in E. coli, including internal ribosome entry sites (IRES), 2A self-cleaving peptides, dual-promoter cassettes, multicistronic operons, and multi-plasmid configurations. We highlight the mechanistic principles, design trade-offs, and regulatory bottlenecks associated with each approach, such as translational imbalance, inclusion body formation, and plasmid compatibility. Real-world applications in metabolic engineering, complex protein assembly, and biomanufacturing are analyzed to demonstrate the functional advantages of these systems. Finally, we explore emerging programmable toolkits that integrate modular architecture, expression modeling, and AI-assisted design, paving the way for next-generation synthetic expression control in microbial chassis. This review offers a comprehensive and strategic roadmap for researchers engineering multi-gene systems in E. coli and beyond.\n\nCandidates:\nA. This review critically examines the major strategies enabling multigene co-expression in E.\nB. coli ) has long served as a versatile workhorse for recombinant protein production.\nC. The evidence does not state that coli have evolved from basic dual-gene constructs to programmable, polygenic expression platforms.\nD. The evidence does not state that as synthetic biology expands the demand for coordinated expression of multiple genes, co-expression systems in E.\nE. As synthetic biology expands the demand for coordinated expression of multiple genes, co-expression systems in E.\nF. The evidence does not state that this review critically examines the major strategies enabling multigene co-expression in E.\nG. coli have evolved from basic dual-gene constructs to programmable, polygenic expression platforms.\nH. The evidence does not state that coli ) has long served as a versatile workhorse for recombinant protein production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12819055", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12819055/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-96f406f5ec5e2f1cf288", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nWhey, a by‐product of the cheese manufacturing industry, represents one of the most abundant and polluting effluents in the global food industry. Despite traditionally being underutilized and often discarded, its rich nutrient profile, particularly protein and lactose, has increasingly sparked an interest in its value within biotechnological processes. This review analyses the potential of whey as a sustainable substrate for the microbial production of value‐added bioproducts, focussing on L‐threonine production as a strategic case study, while addressing the environmental impact of inadequate disposal and current utilization strategies. A comparative analysis with other agroindustrial waste demonstrates whey’s competitive advantages in terms of composition, cost‐effectiveness and sustainability metrics. Furthermore, L‐threonine biological and industrial importance, and the most relevant advances in metabolic engineering, optimized fermentation and emerging tools such as optogenetics and machine learning are discussed, as they facilitate enhanced L‐threonine yields through the creation of robust, high‐producing strains. Technoeconomic analysis at pilot scale (33.8 tons/year) indicates that whey‐based production offers a comparative cost advantage of 7.4% over glucose‐based processes (20.55 USD/kg vs. 22.20 USD/kg). While absolute costs at pilot scale exceed current industrial market prices (1.31–1.66 USD/kg)—reflecting typical scale effects—the demonstrated comparative advantage and substantial environmental benefits (waste valorization, elimination of disposal costs and circular economy alignment) position whey‐based L‐threonine production as a strategic biorefinery opportunity with significant potential for industrial‐scale implementation. This cost benefit is primarily driven by the lower market price of whey compared to commercial glucose substrates, which compensates for the slightly higher downstream processing costs (5.90 vs. 5.40 USD/kg) required for complex matrices. Downstream processing considerations, including recovery, purity requirements and economic viability, are comprehensively addressed. This review concludes that whey, far from being merely a pollutant, has the characteristics required to become an asset for biotechnology. Utilizing whey as a culture medium for L‐threonine production by E. coli in bioreactors not only offers a solution to mitigate a significant environmental issue but also opens a path for the cost‐effective, sustainable production of a globally high‐demand amino acid. Whey represents a strategic biorefinery platform with potential for industrial‐scale implementation. Continued research and development in this area are fundamental to fully realizing this potential.\n\nCandidates:\nA. The evidence does not state that despite traditionally being underutilized and often discarded, its rich nutrient profile, particularly protein and lactose, has increasingly sparked an interest in its value within biotechnological processes.\nB. Whey, a by‐product of the cheese manufacturing industry, represents one of the most abundant and polluting effluents in the global food industry.\nC. The evidence does not state that a comparative analysis with other agroindustrial waste demonstrates whey’s competitive advantages in terms of composition, cost‐effectiveness and sustainability metrics.\nD. Despite traditionally being underutilized and often discarded, its rich nutrient profile, particularly protein and lactose, has increasingly sparked an interest in its value within biotechnological processes.\nE. The evidence does not state that whey, a by‐product of the cheese manufacturing industry, represents one of the most abundant and polluting effluents in the global food industry.\nF. The evidence does not state that this review analyses the potential of whey as a sustainable substrate for the microbial production of value‐added bioproducts, focussing on L‐threonine production as a strategic case study, while addressing the environmental impact of inadequate disposal and current utilization strategies.\nG. This review analyses the potential of whey as a sustainable substrate for the microbial production of value‐added bioproducts, focussing on L‐threonine production as a strategic case study, while addressing the environmental impact of inadequate disposal and current utilization strategies.\nH. A comparative analysis with other agroindustrial waste demonstrates whey’s competitive advantages in terms of composition, cost‐effectiveness and sustainability metrics.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12828667", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12828667/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e498966d8f7eda8adbbd", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nYarrowia lipolytica, a versatile and oleaginous yeast, has garnered significant attention as a promising microbial chassis for producing a vast array of important metabolic products, including trace minerals, vitamins, amino acids, protein and peptides, carbohydrates, and single-cell oil (SCO), primarily in the form of saturated high-value lipids like cocoa-butter equivalents and mono-unsaturated fatty acids (MUFAs). The US FDA has designated Y. lipolytica as a “safe-to-use organism” and given it GRAS (generally regarded as safe) classification for the synthesis of EPA, citric acid, and erythritol. Co-culturing multiple interspecies microorganisms together has proven to be a feasible and comparatively more efficient strategy than monoculture to target the degradation of waste components as a substrate and boost the production of significant metabolites. In recent years, a great deal of research has been devoted to exploring the potential of this host for the biosynthesis of valuable compounds from a wide variety of strategies. Despite ongoing efforts to improve our understanding of xylose metabolism in this yeast, there has been a notable lack of research focused specifically on the biosynthesis of natural products using xylose as a precursor, which is the second most abundant sugar in lignocellulosic biomass. This review also explores recent advances in the genetic modification of Y. lipolytica to enhance its ability to assimilate xylose and produce various secondary metabolites by using xylose as a substrate.\n\nCandidates:\nA. The evidence does not state that lipolytica as a “safe-to-use organism” and given it GRAS (generally regarded as safe) classification for the synthesis of EPA, citric acid, and erythritol.\nB. Yarrowia lipolytica, a versatile and oleaginous yeast, has garnered significant attention as a promising microbial chassis for producing a vast array of important metabolic products, including trace minerals, vitamins, amino acids, protein and peptides, carbohydrates, and single-cell oil (SCO), primarily in the form of saturated high-value lipids like cocoa-butter equivalents and mono-unsaturated fatty acids (MUFAs).\nC. lipolytica as a “safe-to-use organism” and given it GRAS (generally regarded as safe) classification for the synthesis of EPA, citric acid, and erythritol.\nD. Co-culturing multiple interspecies microorganisms together has proven to be a feasible and comparatively more efficient strategy than monoculture to target the degradation of waste components as a substrate and boost the production of significant metabolites.\nE. The evidence does not state that co-culturing multiple interspecies microorganisms together has proven to be a feasible and comparatively more efficient strategy than monoculture to target the degradation of waste components as a substrate and boost the production of significant metabolites.\nF. In recent years, a great deal of research has been devoted to exploring the potential of this host for the biosynthesis of valuable compounds from a wide variety of strategies.\nG. The evidence does not state that yarrowia lipolytica, a versatile and oleaginous yeast, has garnered significant attention as a promising microbial chassis for producing a vast array of important metabolic products, including trace minerals, vitamins, amino acids, protein and peptides, carbohydrates, and single-cell oil (SCO), primarily in the form of saturated high-value lipids like cocoa-butter equivalents and mono-unsaturated fatty acids (MUFAs).\nH. The evidence does not state that in recent years, a great deal of research has been devoted to exploring the potential of this host for the biosynthesis of valuable compounds from a wide variety of strategies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12833036", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12833036/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-678390d32b3d9612c57b", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"2 L\", \"91%\", \"3 L\", \"251 mL\", \"90%\", \"250 mL\"]\n\nEvidence:\nThe emergence of allogeneic, universal chimeric antigen receptor (CAR) T cell therapies requires intensified and scalable manufacturing workflows supported by representative scale-down models (SDMs) to enable efficient process development and future large-scale production of off-the-shelf therapies. Here, we present a 7-day CAR-T cell expansion process intensified via perfusion of serum-free medium in a 2 L Univessel® Single-Use stirred-tank bioreactor (STR), consistently achieving 30 × 10 6 cells/mL, corresponding to 113 ± 7 anti-CD19 CAR-T doses per batch. Parallel runs in 250 mL Ambr® 250 STRs conducted at equivalent volumetric power input ( P/V ) of ∼8.78 W/m 3 demonstrated comparable process performance and final product quality, with univariate and multivariate analyses of cell growth, phenotype, cytotoxicity, and cytokine secretion validating the Ambr® 250 as a predictive SDM for the 2 L process. Integrating capacitance sensing in the 2 L STR enabled robust monitoring of viable cell concentrations in real-time, with strong correlation to offline measurements (R 2 = 0.98). For downstream processing, the Ksep® 400 was used to automate CAR-T cell harvesting, concentration, and washing at the 2 L scale, achieving >90% product recovery and nine-fold volume reduction without impacting product quality attributes compared to manual methods. This study establishes a scalable CAR-T manufacturing workflow supported by a predictive SDM, providing an efficient platform for process development and scale-up to enable future large-scale production of allogeneic CAR-T cell therapies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12833271", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12833271/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3904973e009d6584d6f0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicroalgae and cyanobacteria are emerging as sustainable alternatives to chemical fertilizers and pesticides, offering nutrient recycling, stress mitigation, and environmental restoration within the framework of circular bioeconomy. This review synthesizes recent advances in the utilization of cyanobacteria and green microalgae as biofertilizers, biostimulants, and biopesticides, emphasizing their physiological mechanisms and agronomic potential. Microalgae and cyanobacteria can fix atmospheric nitrogen, solubilize phosphorus, and supply essential micronutrients through exopolysaccharides, organic acids, and siderophores, thereby improving soil fertility and structure. Their metabolites, including phytohormones, amino acids, and antioxidants, stimulate seed germination, root growth, nutrient uptake, and tolerance to abiotic stresses such as drought and salinity. Moreover, allelochemicals and antimicrobial compounds from microalgae can suppress plant pathogens and reduce pesticide dependence. Integrating microalgae cultivation with wastewater and flue gas utilization promotes nutrient recycling and CO 2 sequestration, further enhancing environmental sustainability. However, large-scale application remains limited by biomass production costs, inconsistent performance under field conditions, and regulatory uncertainty. Overall, microalgae-based fertilizers and biostimulants hold great promise for sustainable crop production and soil health improvement. Future research should focus on low-cost cultivation and harvesting technologies, field scale validation, and standardized product formulations to accelerate the transition toward climate smart and resource sustainable agriculture.\n\nCandidates:\nA. Microalgae and cyanobacteria cannot fix atmospheric nitrogen, solubilize phosphorus, and supply essential micronutrients through exopolysaccharides, organic acids, and siderophores, thereby improving soil fertility and structure.\nB. Microalgae and cyanobacteria can fix atmospheric nitrogen, solubilize phosphorus, and supply essential micronutrients through exopolysaccharides, organic acids, and siderophores, thereby improving soil fertility and structure.\nC. Their metabolites, including phytohormones, amino acids, and antioxidants, stimulate seed germination, root growth, nutrient uptake, and tolerance to abiotic stresses such as drought and salinity.\nD. The evidence does not state that their metabolites, including phytohormones, amino acids, and antioxidants, stimulate seed germination, root growth, nutrient uptake, and tolerance to abiotic stresses such as drought and salinity.\nE. The evidence does not state that this review synthesizes recent advances in the utilization of cyanobacteria and green microalgae as biofertilizers, biostimulants, and biopesticides, emphasizing their physiological mechanisms and agronomic potential.\nF. Microalgae and cyanobacteria are emerging as sustainable alternatives to chemical fertilizers and pesticides, offering nutrient recycling, stress mitigation, and environmental restoration within the framework of circular bioeconomy.\nG. Microalgae and cyanobacteria are not emerging as sustainable alternatives to chemical fertilizers and pesticides, offering nutrient recycling, stress mitigation, and environmental restoration within the framework of circular bioeconomy.\nH. This review synthesizes recent advances in the utilization of cyanobacteria and green microalgae as biofertilizers, biostimulants, and biopesticides, emphasizing their physiological mechanisms and agronomic potential.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12833470", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12833470/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fbbe5b343c1c9cecfdc6", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nL-( +)-Tartaric acid is a valuable organic acid with broad applications in the food, pharmaceutical, and chemical industries. Its eco-friendly synthesis typically relies on the enzymatic hydrolysis of cis -epoxysuccinate (CES) catalyzed by cis -epoxysuccinate hydrolases (CESHs), but conventional single-batch processes suffer from low space–time yields and poor continuity. To address these challenges, we devised two complementary fed-batch strategies to simplify the enzyme–product separation by exploiting differences in their solubilities. Strategy A employs carrier-free cross-linking immobilization of whole cells using 0.02% glutaraldehyde and 0.1% polyethylenimine. In this system, both the substrate sodium cis -epoxysuccinate (CESNa) and the product sodium L-( +)-tartrate remain soluble, while the enzyme is retained in the insoluble cell matrix. Under fed-batch operation, this configuration achieves a space–time yield of 150 g L −1 h −1 . Strategy B uses cell-free extract of CESH to hydrolyze calcium cis -epoxysuccinate (CESCa) with inherently low solubility. Here, the enzyme is fully soluble but the L-( +)-tartrate formed precipitates as an insoluble calcium salt, allowing easy separation of the product from the reaction mixture. This approach overcomes potential substrate inhibition and minimizes sodium-ion discharge, delivering a space–time yield of 136 g L −1 h −1 and a specific productivity of 484 g product /g catalyst . Both the soluble-product/insoluble-enzyme system (A) and the insoluble-product/soluble-enzyme system (B) represent effective strategies to streamline downstream processing and markedly enhance productivity. Together, they offer a viable route to scalable and cost-effective industrial production of L-( +)-tartaric acid.\n\nCandidates:\nA. To address these challenges, we devised two complementary fed-batch strategies to simplify the enzyme–product separation by exploiting differences in their solubilities.\nB. Strategy A employs carrier-free cross-linking immobilization of whole cells using 1.02% glutaraldehyde and 0.1% polyethylenimine.\nC. L-( +)-Tartaric acid is not a valuable organic acid with broad applications in the food, pharmaceutical, and chemical industries.\nD. The evidence does not state that its eco-friendly synthesis typically relies on the enzymatic hydrolysis of cis -epoxysuccinate (CES) catalyzed by cis -epoxysuccinate hydrolases (CESHs), but conventional single-batch processes suffer from low space–time yields and poor continuity.\nE. L-( +)-Tartaric acid is a valuable organic acid with broad applications in the food, pharmaceutical, and chemical industries.\nF. Its eco-friendly synthesis typically relies on the enzymatic hydrolysis of cis -epoxysuccinate (CES) catalyzed by cis -epoxysuccinate hydrolases (CESHs), but conventional single-batch processes suffer from low space–time yields and poor continuity.\nG. Strategy A employs carrier-free cross-linking immobilization of whole cells using 0.02% glutaraldehyde and 0.1% polyethylenimine.\nH. The evidence does not state that to address these challenges, we devised two complementary fed-batch strategies to simplify the enzyme–product separation by exploiting differences in their solubilities.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12834850", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12834850/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-69cc3c53405c0aa5496c", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"2.18 g/L\", \"3.18 g/L\"]\n\nEvidence:\nD-Pantothenic acid (DPA), also known as vitamin B 5 , is a water-soluble organic acid, widely applied in foods, feeds, cosmetics, and medicines. Although numerous and rapidly developing cell factories have been established for DPA biosynthesis, there has been no report of any attempts to engineer Yarrowia lipolytica to synthesize DPA. To explore further possibilities in DPA biosynthesis, we tried to employ systematic metabolic engineering strategies to identify and break the potential bottlenecks in DPA biosynthesis by Y. lipolytica . By improving the rate-limiting steps of the DPA biosynthesis pathway, weakening the strongly competitive pathways, and enhancing the multiple cofactor supplies, a robust Y. lipolytica cell factory for DPA biosynthesis was successfully constructed. Consequently, the resulting strain DPA34 produced 2.18 g/L DPA in a 5-L bioreactor, representing the first report of DPA production to date in Y. lipolytica. This work is believed to facilitate the development of Y. lipolytica for sustainable manufacturing of vitamin B 5 and its derivatives. The online version contains supplementary material available at 10.1186/s40643-026-01009-4.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12834876", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12834876/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-51291192a5ddd0837f19", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHeparin, mainly used as an anticoagulant, has also shown potential in the treatment of diseases such as inflammation and cancer. Currently, heparin is mainly extracted from the intestinal mucosa of pigs. However, due to concerns about disease transmission and contamination associated with animal-derived products, biomanufacturing techniques have been explored as alternative production methods. Through enzyme engineering, metabolic engineering, and synthetic biology approaches, the heparin biosynthetic pathways have been systematically optimized. The main biomanufacturing techniques include in vivo/in vitro combination strategy (microbial heparosan fermentation followed by chemoenzymatic modification) and de novo biosynthesis. This article comprehensively discusses the latest advancements, challenges, and future perspectives of these heparin biomanufacturing techniques. Keywords: Bioengineered heparin, Heparosan fermentation, Chemoenzymatic modification, De novo biosynthesis, Synthetic biology\n\nCandidates:\nA. Currently, heparin is mainly extracted from the intestinal mucosa of pigs.\nB. The evidence does not state that however, due to concerns about disease transmission and contamination associated with animal-derived products, biomanufacturing techniques have been explored as alternative production methods.\nC. The evidence does not state that through enzyme engineering, metabolic engineering, and synthetic biology approaches, the heparin biosynthetic pathways have been systematically optimized.\nD. The evidence does not state that heparin, mainly used as an anticoagulant, has also shown potential in the treatment of diseases such as inflammation and cancer.\nE. Through enzyme engineering, metabolic engineering, and synthetic biology approaches, the heparin biosynthetic pathways have been systematically optimized.\nF. Heparin, mainly used as an anticoagulant, has also shown potential in the treatment of diseases such as inflammation and cancer.\nG. Currently, heparin is not mainly extracted from the intestinal mucosa of pigs.\nH. However, due to concerns about disease transmission and contamination associated with animal-derived products, biomanufacturing techniques have been explored as alternative production methods.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12834895", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12834895/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cd3043ce26fd4e8e816c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nUmbilical cord blood (UCB) is an attractive source of natural killer (NK) cells for the development of allogeneic ‘off-the-shelf’ cancer immunotherapies. This is due to the relatively high proportion of highly proliferative NK cells compared to adult peripheral blood (APB), a low risk of graft-versus-host disease and ease of procurement. However, due to the limited starting volume of UCB and naïve phenotype of isolated cells, ex vivo NK cell expansion and activation is essential to generate clinically relevant doses of cells with potent anti-tumor activity. Furthermore, intrinsic variability in both in vitro and clinical performance of NK cells from different UCB units (CBUs) has been reported. To better characterize this variability, we measured UCB NK cell ex vivo fold expansion, phenotype and cytotoxic potential using a basic expansion system. We then used these results to identify characteristics related to superior performance, enabling the optimization, selection and processing of CBUs for the manufacture of NK cells as therapies at a larger scale. Our results revealed that despite wide inter-donor variability in performance between CBUs, a priori selection could be used to identify units likely to show high expansion and/or cytotoxicity. We observed that decreased time between UCB collection and CD3 - UCB mononuclear cell (CBMC) isolation was associated with significantly higher NK fold expansion (n=13; p<0.05). Furthermore, a cryopreservation step following early isolation and prior to expansion, significantly increased the expansion potential of the isolated NK cells (p<0.05), thus providing an opportunity for pre-selection and parallel culture of multiple optimal units. Finally, the NK cells from CBUs collected from caesarean sections had statistically significantly increased proliferative potential compared to those from vaginal deliveries (n=13; p<0.05). In conclusion, early isolation and cryopreservation of CD3 - CBMCs from caesarean section CBUs offer an optimal starting material for use in UCB-derived NK cell immunotherapies, providing superior ex vivo performance and enabling batch testing to selectively expand cells from CBUs with the greatest potential.\n\nCandidates:\nA. The evidence does not state that furthermore, intrinsic variability in both in vitro and clinical performance of NK cells from different UCB units (CBUs) has been reported.\nB. This is due to the relatively high proportion of highly proliferative NK cells compared to adult peripheral blood (APB), a low risk of graft-versus-host disease and ease of procurement.\nC. Furthermore, intrinsic variability in both in vitro and clinical performance of NK cells from different UCB units (CBUs) has been reported.\nD. However, due to the limited starting volume of UCB and naïve phenotype of isolated cells, ex vivo NK cell expansion and activation is not essential to generate clinically relevant doses of cells with potent anti-tumor activity.\nE. This is not due to the relatively high proportion of highly proliferative NK cells compared to adult peripheral blood (APB), a low risk of graft-versus-host disease and ease of procurement.\nF. Umbilical cord blood (UCB) is not an attractive source of natural killer (NK) cells for the development of allogeneic ‘off-the-shelf’ cancer immunotherapies.\nG. However, due to the limited starting volume of UCB and naïve phenotype of isolated cells, ex vivo NK cell expansion and activation is essential to generate clinically relevant doses of cells with potent anti-tumor activity.\nH. Umbilical cord blood (UCB) is an attractive source of natural killer (NK) cells for the development of allogeneic ‘off-the-shelf’ cancer immunotherapies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12835214", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12835214/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2a9a73c619b8ef78bebc", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCodon optimization is widely used to improve heterologous gene expression in Escherichia coli . However, many existing methods focus primarily on maximizing the codon adaptation index (CAI) and neglect broader aspects of biological context. In this study, we present ColiFormer, a transformer-based codon optimization framework fine-tuned on 3676 high-expression E. coli genes curated from the NCBI database. Built on the CodonTransformer BigBird architecture, ColiFormer employs self-attention mechanisms and a mathematical optimization method (the augmented Lagrangian approach) to balance multiple biological objectives simultaneously, including CAI, GC content, tRNA adaptation index (tAI), RNA stability, and minimization of negative cis-regulatory elements. Based on in silico evaluations on 37,053 native E. coli genes and 80 recombinant protein targets commonly used in industrial studies, ColiFormer demonstrated significant improvements in CAI and tAI values, maintained GC content within biologically optimal ranges, and reduced inhibitory cis-regulatory motifs compared with established codon optimization approaches, while maintaining competitive runtime performance. These results represent computational predictions derived from standard in silico metrics; future experimental work is anticipated to validate these computational predictions in vivo. ColiFormer has been released as an open-source tool alongside the benchmark datasets used in this study.\n\nCandidates:\nA. coli genes curated from the NCBI database.\nB. Codon optimization is widely used to improve heterologous gene expression in Escherichia coli .\nC. In this study, we present ColiFormer, a transformer-based codon optimization framework fine-tuned on 3676 high-expression E.\nD. In this study, we present ColiFormer, a transformer-based codon optimization framework fine-tuned on 3677 high-expression E.\nE. However, many existing methods focus primarily on maximizing the codon adaptation index (CAI) and neglect broader aspects of biological context.\nF. Codon optimization is not widely used to improve heterologous gene expression in Escherichia coli .\nG. The evidence does not state that however, many existing methods focus primarily on maximizing the codon adaptation index (CAI) and neglect broader aspects of biological context.\nH. The evidence does not state that coli genes curated from the NCBI database.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12838208", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12838208/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aca794ba44ec754f73de", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSyringic acid (SA) is a natural derivative of syringaldehyde (SD), derived from lignin depolymerization. Its application in the food industry focuses on the properties of natural functional ingredients; it is mainly used as a food antioxidant and food preservative, but can also be used as an ingredient to enhance food flavor and functional foods. This compound exhibits a remarkable spectrum of biological activities, including potent antioxidant, anti-inflammatory, neuroprotective, hypoglycemic, detoxifying, and anti-cancer effects, positioning it as a highly promising candidate for pharmaceutical and nutraceutical applications. In this study, suitable sites were first screened through homologous sequence alignment, and a variant of aryl-alcohol oxidase (CgAAO) with high efficiency in catalyzing the conversion of SD to SA was obtained via site-directed mutagenesis. A deep eutectic solvent (DES) system based on choline chloride/urea (ChCl/UR) in water was developed to enhance SA production. Additionally, key parameters of the biological reaction were optimized, including temperature, pH, metal ions, as well as the type and dosage of DES. The optimal performance was achieved using recombinant E. coli pRSFDuet-CgAAO-Y335F whole-cell biocatalysts, yielding 75% and producing 0.75 g/L SA in 100 mM KPB buffer (pH 7.0) containing 5 wt% ChCl/UR and 1 mM Fe 3+ . This study established a novel biosynthetic pathway for SA that was efficient, mild, green, and environmentally friendly.\n\nCandidates:\nA. Its application in the food industry focuses on the properties of natural functional ingredients; it is mainly used as a food antioxidant and food preservative, but can also be used as an ingredient to enhance food flavor and functional foods.\nB. In this study, suitable sites were first screened through homologous sequence alignment, and a variant of aryl-alcohol oxidase (CgAAO) with high efficiency in catalyzing the conversion of SD to SA was obtained via site-directed mutagenesis.\nC. Syringic acid (SA) is not a natural derivative of syringaldehyde (SD), derived from lignin depolymerization.\nD. This compound exhibits a remarkable spectrum of biological activities, including potent antioxidant, anti-inflammatory, neuroprotective, hypoglycemic, detoxifying, and anti-cancer effects, positioning it as a highly promising candidate for pharmaceutical and nutraceutical applications.\nE. In this study, suitable sites were first screened through homologous sequence alignment, and a variant of aryl-alcohol oxidase (CgAAO) with high efficiency in catalyzing the conversion of SD to SA was not obtained via site-directed mutagenesis.\nF. Its application in the food industry focuses on the properties of natural functional ingredients; it is not mainly used as a food antioxidant and food preservative, but can also be used as an ingredient to enhance food flavor and functional foods.\nG. Syringic acid (SA) is a natural derivative of syringaldehyde (SD), derived from lignin depolymerization.\nH. The evidence does not state that this compound exhibits a remarkable spectrum of biological activities, including potent antioxidant, anti-inflammatory, neuroprotective, hypoglycemic, detoxifying, and anti-cancer effects, positioning it as a highly promising candidate for pharmaceutical and nutraceutical applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12839953", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12839953/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-33aaf2bc37e57d1d6e44", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nEnzyme technology, characterized by high efficiency, environmental compatibility, and precise controllability, has become a pivotal biocatalytic approach for quality enhancement and nutritional improvement in modern food industries. This review summarizes recent advances and underlying mechanisms of enzyme applications in food processing optimization, nutritional enhancement, and functional food development. In terms of process optimization, enzymes such as transglutaminase, laccase, and peroxidase enhance protein crosslinking, thereby markedly improving the texture and stability of dairy products, meat products, and plant-based protein systems. Proteases and lipases play essential roles in flavor development, maturation, and modulation of sensory attributes. From a nutritional perspective, enzymatic hydrolysis significantly improves the bioavailability of proteins, minerals, and dietary fibers, while simultaneously degrading antinutritional factors and harmful compounds, including phytic acid, tannins, food allergens, and acrylamide, thus contributing to improved food safety and nutritional balance. With respect to functional innovation, enzyme-directed production of bioactive peptides has demonstrated notable antihypertensive, antioxidant, and immunomodulatory activities. In addition, enzymatic synthesis of functional oligosaccharides and rare sugars, glycosylation-based modification of polyphenols, and enzyme-assisted extraction of plant bioactive compounds provide novel strategies and technological support for the development of functional foods. Owing to their high specificity and eco-friendly nature, enzyme technologies are driving food and nutrition sciences toward more precise, personalized, and sustainable development pathways. Despite these advances, critical research gaps remain, particularly in the limited mechanistic understanding of enzyme behavior in complex food matrices, the insufficient integration of multi-omics data with enzymatic process design, and the challenges associated with translating laboratory-scale enzymatic strategies into robust, data-driven, and scalable industrial applications.\n\nCandidates:\nA. In terms of process optimization, enzymes such as transglutaminase, laccase, and peroxidase enhance protein crosslinking, thereby markedly improving the texture and stability of dairy products, meat products, and plant-based protein systems.\nB. Enzyme technology, characterized by high efficiency, environmental compatibility, and precise controllability, has become a pivotal biocatalytic approach for quality enhancement and nutritional improvement in modern food industries.\nC. The evidence does not state that enzyme technology, characterized by high efficiency, environmental compatibility, and precise controllability, has become a pivotal biocatalytic approach for quality enhancement and nutritional improvement in modern food industries.\nD. Proteases and lipases play essential roles in flavor development, maturation, and modulation of sensory attributes.\nE. The evidence does not state that this review summarizes recent advances and underlying mechanisms of enzyme applications in food processing optimization, nutritional enhancement, and functional food development.\nF. This review summarizes recent advances and underlying mechanisms of enzyme applications in food processing optimization, nutritional enhancement, and functional food development.\nG. The evidence does not state that proteases and lipases play essential roles in flavor development, maturation, and modulation of sensory attributes.\nH. The evidence does not state that in terms of process optimization, enzymes such as transglutaminase, laccase, and peroxidase enhance protein crosslinking, thereby markedly improving the texture and stability of dairy products, meat products, and plant-based protein systems.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12841018", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12841018/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e25af4799facfc069b46", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe metal-binding periplasmic protein CusF has been proposed as a bifunctional tag that enhances the solubility of recombinant proteins and enables purification using Cu affinity chromatography. However, evidence for its performance remains limited to a few model proteins. Here, we evaluated CusF as a solubility tag for two heterologous proteins: a putative poly(A)-polymerase from Enterococcus faecalis (Efa PAP) and the red fluorescent protein mCherry. The proteins were fused to CusF, expressed in E. coli BL21 (DE3) pLysS and Rosetta 2 (DE3) strains, and assessed for solubility and IMAC binding. Native Efa PAP was completely insoluble under all tested conditions, and fusion to CusF did not improve its solubility. Similarly, CusF–mCherry accumulated predominantly in the insoluble fraction, with only trace amounts detectable in soluble lysates. Soluble CusF–mCherry did not bind Cu 2+ -charged IMAC resin, while moderate binding to Ni 2+ -charged resin was attributable to the vector-encoded His tag rather than CusF. These results indicate that CusF does not universally enhance protein solubility and may not consistently bind Cu-based IMAC resin. Our findings expand empirical knowledge of solubility tag performance and emphasize the necessity of testing multiple tags to identify optimal strategies for recombinant protein production.\n\nCandidates:\nA. The evidence does not state that here, we evaluated CusF as a solubility tag for two heterologous proteins: a putative poly(A)-polymerase from Enterococcus faecalis (Efa PAP) and the red fluorescent protein mCherry.\nB. The evidence does not state that the metal-binding periplasmic protein CusF has been proposed as a bifunctional tag that enhances the solubility of recombinant proteins and enables purification using Cu affinity chromatography.\nC. The evidence does not state that however, evidence for its performance remains limited to a few model proteins.\nD. However, evidence for its performance remains limited to a few model proteins.\nE. The metal-binding periplasmic protein CusF has been proposed as a bifunctional tag that enhances the solubility of recombinant proteins and enables purification using Cu affinity chromatography.\nF. The proteins were fused to CusF, expressed in E.\nG. The proteins were not fused to CusF, expressed in E.\nH. Here, we evaluated CusF as a solubility tag for two heterologous proteins: a putative poly(A)-polymerase from Enterococcus faecalis (Efa PAP) and the red fluorescent protein mCherry.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12842202", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12842202/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dff8580a2c9e81f1a11c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPhycobiliproteins are recognized as potential bioactive compounds and described as highly valued natural products for industrial and biotechnological applications. Moreover, they have been observed to possess antioxidant, anticancer/antineoplastic, and anti-inflammatory activities. Therefore, the search for new methods of their extraction and isolation is still ongoing. Foam fractionation, a bubble separation technique that allows amphiphilic molecules to be separated from their aqueous solutions, is a promising but understudied method. The process may be carried out both under mild conditions that are suitable for proteins and also for diluted solutions. This paper presents the results of applying the foam fractionation process to concentrate and separate phycobiliproteins. Allo- and C-phycocyanin from a thermophilic Synechococcus PCC 6715 strain were used in extract form after biomass cultivation and disintegration. Two ways of running the process were investigated: batch mode and continuous mode, the latter of which has not been reported in the literature previously. The results indicate that the method can be applied on a larger scale, as the outcomes of the continuous mode processes were comparable to those of the batch mode. Moreover, the results indicate that the process provides, to a certain extent, the opportunity of separating phycobiliproteins from each other.\n\nCandidates:\nA. Phycobiliproteins are recognized as potential bioactive compounds and described as highly valued natural products for industrial and biotechnological applications.\nB. Foam fractionation, a bubble separation technique that allows amphiphilic molecules to be separated from their aqueous solutions, is not a promising but understudied method.\nC. Foam fractionation, a bubble separation technique that allows amphiphilic molecules to be separated from their aqueous solutions, is a promising but understudied method.\nD. The evidence does not state that moreover, they have been observed to possess antioxidant, anticancer/antineoplastic, and anti-inflammatory activities.\nE. Phycobiliproteins are not recognized as potential bioactive compounds and described as highly valued natural products for industrial and biotechnological applications.\nF. Therefore, the search for new methods of their extraction and isolation is still ongoing.\nG. Moreover, they have been observed to possess antioxidant, anticancer/antineoplastic, and anti-inflammatory activities.\nH. Therefore, the search for new methods of their extraction and isolation is not still ongoing.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12842750", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12842750/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-19056f8dd2eb9647a1c1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nXylanases (EC 3.2.1.8) are value-added enzymes essential for biomass deconstruction and are widely used in the pulp and paper, food, feed, and biofuel sectors. This review provides a comprehensive analysis of the current state and future prospects of xylanase research and application. It begins by examining the structural diversity of xylan substrates and the corresponding classification of xylanase enzymes, their catalytic mechanisms, and methods for their functional study, such as inhibitor analysis. The discussion then covers the challenges and methods involved in the purification of xylanases from complex biological mixtures. While natural microbial sources (fungi and bacteria) remain important, the limitations of wild-type (WT) strains for industrial production are highlighted. The review assesses the most common recombinant production systems, including Escherichia coli , Bacillus subtilis , and Komagataella phaffii , comparing their advantages for high-yield enzyme production. Finally, the paper focuses on protein engineering strategies as powerful tools for enhancing key enzyme properties (thermostability, specific activity, and pH tolerance). By integrating fundamental knowledge with applied technological approaches, this review underscores the critical role of xylanases in industrial biotechnology and identifies future research directions for their optimization.\n\nCandidates:\nA. The evidence does not state that this review provides a comprehensive analysis of the current state and future prospects of xylanase research and application.\nB. The evidence does not state that it begins by examining the structural diversity of xylan substrates and the corresponding classification of xylanase enzymes, their catalytic mechanisms, and methods for their functional study, such as inhibitor analysis.\nC. This review provides a comprehensive analysis of the current state and future prospects of xylanase research and application.\nD. Xylanases (EC 3.2.1.8) are value-added enzymes essential for biomass deconstruction and are widely used in the pulp and paper, food, feed, and biofuel sectors.\nE. The discussion then covers the challenges and methods involved in the purification of xylanases from complex biological mixtures.\nF. The evidence does not state that the discussion then covers the challenges and methods involved in the purification of xylanases from complex biological mixtures.\nG. It begins by examining the structural diversity of xylan substrates and the corresponding classification of xylanase enzymes, their catalytic mechanisms, and methods for their functional study, such as inhibitor analysis.\nH. Xylanases (EC 4.2.1.8) are value-added enzymes essential for biomass deconstruction and are widely used in the pulp and paper, food, feed, and biofuel sectors.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12843773", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12843773/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-312e08682f8edaa2df2c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAstaxanthin, derived from Haematococcus pluvialis , is a potent antioxidant with significant therapeutic potential. However, its large-scale commercialization is hindered by the “thick-wall challenge”, a phenomenon where the stress conditions required for astaxanthin accumulation also trigger the formation of resistant secondary cell walls. This challenge complicates extraction and reduces bioaccessibility, thereby increasing production costs. Recent advancements have focused on uncoupling astaxanthin biosynthesis from cell wall reinforcement, utilizing metabolic engineering and strain selection to reduce wall formation while maintaining high yields. Furthermore, green extraction techniques, such as electrotechnologies and ionic liquids, are being explored to improve efficiency and environmental sustainability. This review synthesizes these innovations, including biorefinery systems that maximize biomass valorization, and discusses emerging clinical applications. We highlight the challenges in bridging the gap between laboratory successes and clinical translation, and suggest future directions for resolving the thick-wall challenge, advancing astaxanthin production, and expanding its therapeutic uses in nutraceuticals and pharmaceuticals.\n\nCandidates:\nA. The evidence does not state that recent advancements have focused on uncoupling astaxanthin biosynthesis from cell wall reinforcement, utilizing metabolic engineering and strain selection to reduce wall formation while maintaining high yields.\nB. However, its large-scale commercialization is hindered by the “thick-wall challenge”, a phenomenon where the stress conditions required for astaxanthin accumulation also trigger the formation of resistant secondary cell walls.\nC. However, its large-scale commercialization is not hindered by the “thick-wall challenge”, a phenomenon where the stress conditions required for astaxanthin accumulation also trigger the formation of resistant secondary cell walls.\nD. Recent advancements have focused on uncoupling astaxanthin biosynthesis from cell wall reinforcement, utilizing metabolic engineering and strain selection to reduce wall formation while maintaining high yields.\nE. This challenge complicates extraction and reduces bioaccessibility, thereby increasing production costs.\nF. Astaxanthin, derived from Haematococcus pluvialis , is not a potent antioxidant with significant therapeutic potential.\nG. The evidence does not state that this challenge complicates extraction and reduces bioaccessibility, thereby increasing production costs.\nH. Astaxanthin, derived from Haematococcus pluvialis , is a potent antioxidant with significant therapeutic potential.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12843829", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12843829/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-326944befae69713d53e", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe growing concern over plastic pollution and the widespread presence of micro- and nanoplastics has renewed interest in polyhydroxybutyrate (PHB) as a biodegradable alternative; however, its industrial deployment remains constrained by costly recovery operations with a high environmental burden. This study examines how PHB biosynthesis and intracellular organization, physicochemical properties, and the characteristics of the producing microorganism influence the performance of conventional recovery routes, including extraction with organic solvents, alkaline/oxidative chemical digestion, and enzymatic–physical schemes coupled with mechanical disruption. Based on this foundation, quantitative data are analyzed for PHB content in bacteria, mixed microbial cultures, cyanobacteria, and microalgae, along with extraction yields, polymer purity, and solvent recyclability in processes employing chlorine-free solvents, green solvents, and hydrophobic natural deep eutectic solvents (NaDESs) formulated with terpenes and organic acids. The analysis integrates mechanistic perspectives on NaDES–cell and NaDES–PHB interactions with solvent design criteria, biorefinery configurations, and preliminary evidence from technoeconomic and life cycle assessments. The findings identify NaDES as an up-and-coming platform capable of reconciling biopolymer quality with the principles of green chemistry while delineating critical gaps in recovery efficiency, viscosity management, solvent recycling, and pilot-scale validation.\n\nCandidates:\nA. This study examines how PHB biosynthesis and intracellular organization, physicochemical properties, and the characteristics of the producing microorganism influence the performance of conventional recovery routes, including extraction with organic solvents, alkaline/oxidative chemical digestion, and enzymatic–physical schemes coupled with mechanical disruption.\nB. Based on this foundation, quantitative data are not analyzed for PHB content in bacteria, mixed microbial cultures, cyanobacteria, and microalgae, along with extraction yields, polymer purity, and solvent recyclability in processes employing chlorine-free solvents, green solvents, and hydrophobic natural deep eutectic solvents (NaDESs) formulated with terpenes and organic acids.\nC. The evidence does not state that the analysis integrates mechanistic perspectives on NaDES–cell and NaDES–PHB interactions with solvent design criteria, biorefinery configurations, and preliminary evidence from technoeconomic and life cycle assessments.\nD. The analysis integrates mechanistic perspectives on NaDES–cell and NaDES–PHB interactions with solvent design criteria, biorefinery configurations, and preliminary evidence from technoeconomic and life cycle assessments.\nE. The evidence does not state that this study examines how PHB biosynthesis and intracellular organization, physicochemical properties, and the characteristics of the producing microorganism influence the performance of conventional recovery routes, including extraction with organic solvents, alkaline/oxidative chemical digestion, and enzymatic–physical schemes coupled with mechanical disruption.\nF. The growing concern over plastic pollution and the widespread presence of micro- and nanoplastics has renewed interest in polyhydroxybutyrate (PHB) as a biodegradable alternative; however, its industrial deployment remains constrained by costly recovery operations with a high environmental burden.\nG. Based on this foundation, quantitative data are analyzed for PHB content in bacteria, mixed microbial cultures, cyanobacteria, and microalgae, along with extraction yields, polymer purity, and solvent recyclability in processes employing chlorine-free solvents, green solvents, and hydrophobic natural deep eutectic solvents (NaDESs) formulated with terpenes and organic acids.\nH. The evidence does not state that the growing concern over plastic pollution and the widespread presence of micro- and nanoplastics has renewed interest in polyhydroxybutyrate (PHB) as a biodegradable alternative; however, its industrial deployment remains constrained by costly recovery operations with a high environmental burden.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12845502", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12845502/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0743f91747d13b92e7d4", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nToday, PHB and its copolymers—potential plastic substitutes—are produced by fermenting sugar, which is not scalable to the volumes of plastic consumption. PHB from CH 4 can offer a sustainable process route, with CH 4 potentially produced from a variety of waste biomass streams through anaerobic digestion, gasification, and methanation. The high molar mass (M w ) of PHB is a key determinant of its mechanical properties, and strain, culture conditions and downstream processing influence it. In this work, the strain Methylocystis sp. GB 25 (DSMZ 7674) was grown on natural gas as the sole carbon and energy source and air (1:1) in a loop reactor with 350 L active fermentation volume, at 35 °C and ambient pressure. After two days of continuous growth, the bacteria were limited in P and N for 1, 2, and 2.5 days to determine the optimal conditions for PHB accumulation and the highest Mw as the target. The biomass was then centrifuged and spray-dried. For downstream processing, chloroform solvent extraction and selected enzymatic treatment were deployed, yielding ~40% PHB from the biomass. The PHB obtained by solvent extraction exhibited high average weight molar masses of M w ~1.1–1.5 × 10 6 g mol −1 . The highest M w was obtained after one day of limitation, whereas enzyme treatment resulted in partially degraded PHB. Cold chloroform maceration, interesting due to energy savings, did not achieve sufficient extraction efficiency because it was unable to extract high-molar-mass PHB fractions. The extracted PHB has a high molar mass, more than double that of standard commercial PHB, and was characterized by DSC, which showed a high degree of crystallinity of up to 70% with a melting temperature of close to 180 °C. Mechanical tensile properties measurements, as well as dynamic mechanical thermal analysis (DMTA), were performed. Degradation of the PHB by enzymes was also determined. Methanotrophic PHB is a promising bioplastics material. The high M w can limit and delay polymer degradation in practical processing steps, making the material more versatile and robust.\n\nCandidates:\nA. The high molar mass (M w ) of PHB is not a key determinant of its mechanical properties, and strain, culture conditions and downstream processing influence it.\nB. Today, PHB and its copolymers—potential plastic substitutes—are produced by fermenting sugar, which is not scalable to the volumes of plastic consumption.\nC. In this work, the strain Methylocystis sp.\nD. PHB from CH 5 can offer a sustainable process route, with CH 4 potentially produced from a variety of waste biomass streams through anaerobic digestion, gasification, and methanation.\nE. PHB from CH 4 can offer a sustainable process route, with CH 4 potentially produced from a variety of waste biomass streams through anaerobic digestion, gasification, and methanation.\nF. The high molar mass (M w ) of PHB is a key determinant of its mechanical properties, and strain, culture conditions and downstream processing influence it.\nG. Today, PHB and its copolymers—potential plastic substitutes—are produced by fermenting sugar, which is not not scalable to the volumes of plastic consumption.\nH. The evidence does not state that in this work, the strain Methylocystis sp.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12846098", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12846098/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9517ebc4390221df288b", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"5H\", \"5 L\", \"4H\", \"6 L\"]\n\nEvidence:\nScalable moss bioreactors enable the production of high-quality recombinant prolyl-hydroxylated human collagen without heterologous P4H expression, offering a sustainable and vegan alternative to conventional collagens derived from animals. Collagens are structural proteins of the extracellular matrix essential for skin elasticity and integrity. They are widely used in dietary supplements and cosmetics. Conventional collagens of animal origin raise concerns regarding ethics, safety, and sustainability. As a vegan alternative, we report on the production of a 30 kDa prolyl-hydroxylated human collagen polypeptide from Physcomitrella moss plants. For secretion-based production and formulation compatibility, a hydrophilic region encompassing 334 amino acids from human type III collagen was selected, which includes four protein domains involved in cell adhesion, collagen binding, integrin recognition and wound healing. Transgenic moss lines were generated via protoplast transformation. Immunodetection identified collagen-producing lines, and mass spectrometry validated the product and detected prolyl-hydroxylation on 23 sites. The presence of this important post-translational modification underscores the high biomimetic quality of the product. To enable industrial-scale production, the transformants were quantitatively analysed at the genomic, transcript, and protein levels. The most productive lines were forwarded to process development, where culture conditions, including CO 2 supplementation, pH, and light intensity, were optimized. Upscaling to 5 L photobioreactors established a robust, light- and biomass-dependent production regime that yielded nearly 1 mg/L of secreted collagen polypeptide in the culture supernatant after 11 days of cultivation. Taken together, this study presents the first scalable moss-based production of a post-translationally modified human collagen and offers a sustainable and vegan alternative to conventional collagens for cosmetic formulations. This highlights the versatility of Physcomitrella as a production host for high-quality proteins with industrial applicability that also meet consumer requirements. The online version contains supplementary material available at 10.1007/s00299-026-03727-7.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12852254", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12852254/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-549a6b4df422067f5661", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCarotenoids and apocarotenoids constitute a structurally and functionally sundry class of isoprenoids whose significance extends from photosynthetic light capture and photoprotection to phytohormone signaling, flavor and aroma formation, and emerging biomedical applications. While recent appraisals have emphasized quantitative advances in microbial production, this mini-review adopts a pathway module-centric perspective. We examine each biosynthetic stage from precursor supply, condensation to geranylgeranyl diphosphate (GGPP), phytoene synthesis, desaturation/isomerization, cyclization, hydroxylation, ketolation, epoxidation, and oxidative cleavage, highlighting novel enzymatic variants, mutagenesis studies, fusion strategies, and compartmentalization approaches that impart metabolic control. Special emphasis is placed on recently discovered and engineered enzymes, as well as synthetic biology tools. This review integrates diverse enzyme sources, host ranges across plants, fungi, algae, yeasts, and bacteria, as well as pathway modularity, to provide an updated review of recent literature. We conclude by outlining future directions that highlight gaps and potential areas for future work. This focused synthesis aims to equip researchers with a hierarchical understanding of the pathways and strategies to advance carotenoid and apocarotenoid biosynthesis.\n\nCandidates:\nA. The evidence does not state that we examine each biosynthetic stage from precursor supply, condensation to geranylgeranyl diphosphate (GGPP), phytoene synthesis, desaturation/isomerization, cyclization, hydroxylation, ketolation, epoxidation, and oxidative cleavage, highlighting novel enzymatic variants, mutagenesis studies, fusion strategies, and compartmentalization approaches that impart metabolic control.\nB. While recent appraisals have emphasized quantitative advances in microbial production, this mini-review adopts a pathway module-centric perspective.\nC. We examine each biosynthetic stage from precursor supply, condensation to geranylgeranyl diphosphate (GGPP), phytoene synthesis, desaturation/isomerization, cyclization, hydroxylation, ketolation, epoxidation, and oxidative cleavage, highlighting novel enzymatic variants, mutagenesis studies, fusion strategies, and compartmentalization approaches that impart metabolic control.\nD. Carotenoids and apocarotenoids constitute a structurally and functionally sundry class of isoprenoids whose significance extends from photosynthetic light capture and photoprotection to phytohormone signaling, flavor and aroma formation, and emerging biomedical applications.\nE. The evidence does not state that carotenoids and apocarotenoids constitute a structurally and functionally sundry class of isoprenoids whose significance extends from photosynthetic light capture and photoprotection to phytohormone signaling, flavor and aroma formation, and emerging biomedical applications.\nF. The evidence does not state that while recent appraisals have emphasized quantitative advances in microbial production, this mini-review adopts a pathway module-centric perspective.\nG. Special emphasis is not placed on recently discovered and engineered enzymes, as well as synthetic biology tools.\nH. Special emphasis is placed on recently discovered and engineered enzymes, as well as synthetic biology tools.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12852478", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12852478/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-04ceb58e84e6d47d74f1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDifferent yeast species, including Ogataea polymorpha , are often used as hosts for recombinant protein production. One of the most important factors limiting such applications is yeast-specific modifications of glycoside chains attached to secretory proteins. This problem can potentially be solved by the identification and inactivation of genes responsible for these modifications. Previously we demonstrated that the exceptional resistance of O. polymorpha to vanadate depends on the ABV1 gene responsible for the mannosylphosphorylation of protein glycoside chain in the Golgi apparatus. Here we show that mutations altering protein glycosylation in the secretory pathway can be selected in the abv1Δ mutant by screening for vanadate resistance. For one such mutant, we identified the responsible gene, which encodes a putative α-1,2-mannosyltransferase. To ensure the absence of phosphomannosylation, both O. polymorpha genes, ABV1 and MNN4 , which encode mannosylphosphate transferase homologs, were inactivated. Some vanadate resistant mutants generated in this strain showed defects in N -glycosylation of a recombinant glycoprotein. This demonstrates that the effects of N -glycosylation on vanadate resistance in O. polymorpha are not mediated by phosphomannosylation per se and that identification of certain genes responsible for N -glycosylation in this yeast can be performed via selection of vanadate resistant clones.\n\nCandidates:\nA. One of the most important factors limiting such applications is not yeast-specific modifications of glycoside chains attached to secretory proteins.\nB. The evidence does not state that previously we demonstrated that the exceptional resistance of O.\nC. Different yeast species, including Ogataea polymorpha , are often used as hosts for recombinant protein production.\nD. This problem can potentially be solved by the identification and inactivation of genes responsible for these modifications.\nE. Previously we demonstrated that the exceptional resistance of O.\nF. This problem cannot potentially be solved by the identification and inactivation of genes responsible for these modifications.\nG. One of the most important factors limiting such applications is yeast-specific modifications of glycoside chains attached to secretory proteins.\nH. Different yeast species, including Ogataea polymorpha , are not often used as hosts for recombinant protein production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12855064", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12855064/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fb11bd8c9e3745b4b223", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"31%\", \"30%\", \"87%\", \"81%\", \"88%\", \"7%\", \"6%\", \"80%\"]\n\nEvidence:\nCAR-T cell therapy is leading the way in the field of cancer cell immunotherapies due to its high success rates. However, the manufacturing of CAR-T cells remains complex and expensive. T-cell enrichment from patient apheresis starting material is a key step in the manufacture but cellular impurities interfere with the ex vivo transduction of T-cells and their proliferation. Current enrichment methods including magnetic bead selection suffer from various limitations. We report here a bead-less T-cell enrichment process through a two-stage procedure based on inertial microfluidics. Using apheresis like starting material samples from healthy donors, the dual-stage process showed an efficient 87% (SD ± 6%) enrichment and 80% (SD ± 30%) recovery of T-cells. Validation of the process with ovarian cancer samples resulted in a T-cell purity 70% (SD ± 10%) from a starting purity of 48% (SD ± 6%) at a 64% (SD ± 4%) T-cell recovery. The two-stage inertial microfluidic process was also shown to have no detectable effect on the proliferation of the cells.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12855359", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12855359/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-70b52e61c17461f7d449", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nRecombinant protein expression can be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications. We introduce RP3Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli . RP3Net utilizes the most recent protein and genomic foundational models. A curated dataset of internal experimental results from AstraZeneca and publicly available data from the Structural Genomics Consortium was used for training, validation and testing of RP3Net. RP3Net achieves an increase in area under the receiver operator curve (AUROC) of 0.15, compared to a baseline model. When experimentally validated on an independent, prospective, manually selected set of 97 constructs, RP3Net outperformed currently available models, with an AUROC of 0.83, delivering accurate predictions in 77% of the cases, and correctly identifying successfully expressing constructs in 92% of cases. The model, along with installation and running instructions, is available under an MIT licence at https://github.com/RP3Net/RP3Net , DOI 10.5281/zenodo.17243498.\n\nCandidates:\nA. A curated dataset of internal experimental results from AstraZeneca and publicly available data from the Structural Genomics Consortium was used for training, validation and testing of RP4Net.\nB. RP4Net utilizes the most recent protein and genomic foundational models.\nC. RP3Net utilizes the most recent protein and genomic foundational models.\nD. Recombinant protein expression cannot be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications.\nE. Recombinant protein expression can be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications.\nF. We introduce RP4Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli .\nG. A curated dataset of internal experimental results from AstraZeneca and publicly available data from the Structural Genomics Consortium was used for training, validation and testing of RP3Net.\nH. We introduce RP3Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli .", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12857573", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12857573/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-54f73b17efef9519767b", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"3.5 µg/mL\", \"2.5 µg/mL\"]\n\nEvidence:\nSaccharomyces cerevisiae is an established production host for therapeutic proteins; many of those are small proteins such as insulin or glucagon-like peptide-1 (GLP-1) analogs. Contrastingly, proteins of higher molecular weight, foremost antibodies, did not reach the market due, among other factors, to limiting productivity. Here we addressed the loss of product to protein degradation through a combination of genetic engineering of the host and medium optimization. We screened target genes that either directly or indirectly can lead to proteolytic degradation. We identified four deletions that are beneficial for expression: PEP1 and VPS30 , which both can channel proteins to the vacuole for degradation; MON2 , which can lead to the re-uptake of secreted proteins; and ALG3 , which can affect the permeability of the cell wall. In parallel, we developed a small-scale fed-batch cultivation system for 24-well deep well plate cultivations and using an amino acid-rich medium. To stabilize secreted proteins, we screened chemical chaperones and osmolytes. We fortified the medium with arginine, 4-phenylbutyrate (4-PBA), and Tween-20. Using the engineered yeast strain, which features VPS30 , PEP1 , and ALG3 deletions, and the small-scale fed-batch system, we obtained 2.5 µg/mL of a secreted chimeric fusion of a nanobody to the crystallizable fragment (Fc) of a human immunoglobulin. Instrumental to the increase in the final titer were the reduced losses. This was achieved by a combination of complementary measures: improving diffusion through the cell wall, achieved through genetic engineering, and reducing losses to proteolytic degradation through medium optimization and genetic engineering. Moreover, we showed that the engineered strain and cultivation set-up are suitable for the production of different antibodies. • Chemical chaperones and amino acid-rich medium increased secreted protein titers. • Medium and host engineering are instrumental for improving productivity. • Small-scale cultivation system enables production levels suitable for characterization. The online version contains supplementary material available at 10.1007/s00253-025-13700-1. Keywords: Saccharomyces cerevisiae , Chemical chaperones, Small-scale production system, Medium optimization, Recombinant antibody production, Chimeric nanobody-Fc fusion protein", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12858493", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12858493/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-64d8c3cbe5a7c017aa59", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGas fermentation offers a sustainable alternative for valorizing climate-active gases and industrial off-gases. Currently, these gases require energy-intensive purification steps before they can be used in chemical processes such as Fischer–Tropsch synthesis. In gas fermentation, anaerobic bacteria produce acetate from industrial off-gases. Compared to chemical processes, the anaerobic bacteria offer greater tolerance to varying gas concentrations and impurities. One major product of these anaerobic valorization processes is acetate, which can be used as a co-substrate in a variety of biological processes. This study evaluates Corynebacterium glutamicum and Ustilago maydis in benchtop cultivations using 10–20% (v/v) sterile-filtered acetate-rich supernatants from Acetobacterium woodii fermentation to produce L-lysine and triglycerides. Partial substitution of glucose with these supernatants supported robust growth and required no additional purification beyond sterile filtration. C. glutamicum achieved a L-lysine concentration of 3.5 ± 0.27 g∙L −1 and exhibited a diauxic growth pattern on glucose and acetate. In U. maydis , supernatant addition shortened the lag phase by approximately 2 h but reduced triglyceride yields modestly due to higher nitrogen availability. Optimizing the nitrogen-to-carbon ratio in benchtop fermentations resulted in a triglyceride concentration of 12.75 ± 1.17 g∙L −1 , demonstrating the feasibility of this approach. Collectively, the results demonstrate a viable method for replacing a portion of refined glucose with acetate-rich supernatants, thereby enabling a cost-efficient integration of anaerobic gas valorization with aerobic biomanufacturing. The online version contains supplementary material available at 10.1186/s13068-025-02732-4.\n\nCandidates:\nA. The evidence does not state that in gas fermentation, anaerobic bacteria produce acetate from industrial off-gases.\nB. Gas fermentation offers a sustainable alternative for valorizing climate-active gases and industrial off-gases.\nC. The evidence does not state that compared to chemical processes, the anaerobic bacteria offer greater tolerance to varying gas concentrations and impurities.\nD. In gas fermentation, anaerobic bacteria produce acetate from industrial off-gases.\nE. Compared to chemical processes, the anaerobic bacteria offer greater tolerance to varying gas concentrations and impurities.\nF. The evidence does not state that gas fermentation offers a sustainable alternative for valorizing climate-active gases and industrial off-gases.\nG. Currently, these gases require energy-intensive purification steps before they can be used in chemical processes such as Fischer–Tropsch synthesis.\nH. Currently, these gases require energy-intensive purification steps before they cannot be used in chemical processes such as Fischer–Tropsch synthesis.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12859875", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12859875/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-46359fa3f70e22d525b2", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nKomagataella pastoris is extensively used as a microbial cell factory for the production of recombinant proteins and high‐value compounds. However, tightly controlled promoter systems responsive to safe and economical inducers are required for precise metabolic and pathway engineering in this yeast species. Cumate‐inducible promoters are an ideal choice due to the safety and low cost of cumate. In this study, we systematically optimised the insertion sites of the CuO operator sequence within the strong promoter P GCW14 to isolate a high‐activity variant that we designated as P GCWCuO03 . To fine‐tune the expression of the repressor protein CymR, we developed a truncated promoter of P GAP , designated as P GAP200 . Based on the optimal promoter P GCWCuO03 and the CymR expression unit, we constructed a robust CymR/CuO‐mediated cumate‐inducible promoter, designated as P gc , in K. pastoris . P gc demonstrated outstanding induction properties, resulting in an approximately 11‐fold increase in target protein production following induction. Promoter substitution assays validated the effectiveness of P gc in temporal gene expression control, highlighting the significant potential of this promoter for both basic research and industrial bioprocessing applications in synthetic biology and biotechnology in K. pastoris .\n\nCandidates:\nA. However, tightly controlled promoter systems responsive to safe and economical inducers are required for precise metabolic and pathway engineering in this yeast species.\nB. Cumate‐inducible promoters are not an ideal choice due to the safety and low cost of cumate.\nC. Komagataella pastoris is not extensively used as a microbial cell factory for the production of recombinant proteins and high‐value compounds.\nD. Cumate‐inducible promoters are an ideal choice due to the safety and low cost of cumate.\nE. Komagataella pastoris is extensively used as a microbial cell factory for the production of recombinant proteins and high‐value compounds.\nF. However, tightly controlled promoter systems responsive to safe and economical inducers are not required for precise metabolic and pathway engineering in this yeast species.\nG. In this study, we systematically optimised the insertion sites of the CuO operator sequence within the strong promoter P GCW14 to isolate a high‐activity variant that we designated as P GCWCuO03 .\nH. In this study, we systematically optimised the insertion sites of the CuO operator sequence within the strong promoter P GCW15 to isolate a high‐activity variant that we designated as P GCWCuO03 .", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12868391", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12868391/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-16c38ac99e1e4b2ab1a0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPrecise quantification of recombinant proteins is essential for assessing and comparing expression efficiency and optimizing production processes. Fluorescent proteins have emerged as powerful tools for real-time monitoring of gene expression and protein tracking. However, standardized and validated methods for their quantification, particularly for the widely used green fluorescent protein, remain limited. To date, no universally adopted protocol has emerged. This study presents a high-throughput method for the quantification of recombinantly produced Emerald Green Fluorescent Protein (EmGFP) based on direct fluorescence measurements of the cell suspension while quantifying and integrating potential effects of signal attenuation. The workflow uses solely standard laboratory equipment, ensuring broad accessibility and easy implementation. Moreover, in-house EmGFP standard preparation and quantification is described. The method was validated according to FDA guidelines “Analytical Procedures and Methods Validation for Drugs and Biologics,” addressing the requirements of linearity, limit of detection (LOD), limit of quantification (LOQ), precision, accuracy, and recovery rate. Investigation was conducted using Escherichia coli BL21 cells expressing EmGFP, widely available sodium fluorescein as a chemical standard, commercial GFP, and an in-house EmGFP standard. A robust correlation (linear fitting, R 2 0.96) of the EmGFP concentration and relative fluorescence units (RFU) was established, enabling efficient and high-throughput fluorescence quantification using a standardized workflow in a microtiter-based format suitable for the application in comparative studies across different expression constructs, conditions, and scales. By enabling absolute quantification of fluorescent proteins, this method supports both real-time bioprocess optimization and broader applications in protein production research. The online version contains supplementary material available at 10.1007/s00253-026-13734-z.\n\nCandidates:\nA. Precise quantification of recombinant proteins is essential for assessing and comparing expression efficiency and optimizing production processes.\nB. To date, no universally adopted protocol has emerged.\nC. The evidence does not state that to date, no universally adopted protocol has emerged.\nD. The evidence does not state that however, standardized and validated methods for their quantification, particularly for the widely used green fluorescent protein, remain limited.\nE. Precise quantification of recombinant proteins is not essential for assessing and comparing expression efficiency and optimizing production processes.\nF. Fluorescent proteins have emerged as powerful tools for real-time monitoring of gene expression and protein tracking.\nG. The evidence does not state that fluorescent proteins have emerged as powerful tools for real-time monitoring of gene expression and protein tracking.\nH. However, standardized and validated methods for their quantification, particularly for the widely used green fluorescent protein, remain limited.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12876113", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12876113/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8c5b06a53868cfb0e7d6", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicrobial fermentation is an established technology that is becoming increasingly used to produce key food components. Among the various microorganisms used, yeasts play crucial roles due to their efficiency in synthesizing a wide range of industrially important compounds. The growing demand for sustainable, locally sourced, and animal-free food ingredients has increased the focus on yeast biomass and its derivatives. These yeast-based products, such as food emulsifiers, are a promising next-generation of food components, offering advantages like a low risk of allergenicity. Yeast biomass-based fractions have been effectively used as emulsifiers in various food products including in dairy, meat, bakery, meat alternatives, mayonnaises and salad dressing, with effective properties demonstrated in a range of oil-in-water, water-in-oil, and Pickering emulsion models. Both whole cell biomass and yeast cell fractions such as the yeast cell wall, mannoproteins, glucans, exopolysaccharides and other yeast-derived compounds have been demonstrated to function as effective emulsifiers. An increasingly large number of yeasts, beyond just Saccharomyces cerevisiae , have been studied as potential sources of these emulsifiers with the extraction and purification methods employed depending on the specific emulsifier targeted, the required purity, and the intended application. Efficient, cost-effective, and sustainable processes are key to enabling industrial-scale production of these emulsifiers, as such this article reviews the potential yeast-derived food emulsifiers, lists the various yeast species investigated to date, examines the extraction and purification methods, and highlights the potential food applications of these yeast-derived emulsifiers.\n\nCandidates:\nA. Microbial fermentation is an established technology that is becoming increasingly used to produce key food components.\nB. The evidence does not state that among the various microorganisms used, yeasts play crucial roles due to their efficiency in synthesizing a wide range of industrially important compounds.\nC. Among the various microorganisms used, yeasts play crucial roles due to their efficiency in synthesizing a wide range of industrially important compounds.\nD. These yeast-based products, such as food emulsifiers, are a promising next-generation of food components, offering advantages like a low risk of allergenicity.\nE. The evidence does not state that the growing demand for sustainable, locally sourced, and animal-free food ingredients has increased the focus on yeast biomass and its derivatives.\nF. The growing demand for sustainable, locally sourced, and animal-free food ingredients has increased the focus on yeast biomass and its derivatives.\nG. Microbial fermentation is not an established technology that is becoming increasingly used to produce key food components.\nH. These yeast-based products, such as food emulsifiers, are not a promising next-generation of food components, offering advantages like a low risk of allergenicity.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12876142", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12876142/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fababd41f08c6452e050", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBillions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics. We introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications. The framework is validated on 37,191 ECG images with 1596 collected at Akershus University Hospital, where the algorithm obtains a mean signal-to-noise ratio of 19.65 dB on scanned papers with common artifacts. It is further evaluated on the Emory Paper Digitization ECG Dataset, comprising 35,595 images, including images with perspective distortion, wrinkles, and stains. The model improves on the state-of-the-art in all subcategories. The full software is released as open-source, promoting reproducibility and further development. We hope the software will contribute to unlocking retrospective ECG archives and democratize access to AI-driven diagnostics.\n\nCandidates:\nA. It is further evaluated on the Emory Paper Digitization ECG Dataset, comprising 36,595 images, including images with perspective distortion, wrinkles, and stains.\nB. Billions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics.\nC. The evidence does not state that billions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics.\nD. The framework is validated on 38,191 ECG images with 1596 collected at Akershus University Hospital, where the algorithm obtains a mean signal-to-noise ratio of 19.65 dB on scanned papers with common artifacts.\nE. It is further evaluated on the Emory Paper Digitization ECG Dataset, comprising 35,595 images, including images with perspective distortion, wrinkles, and stains.\nF. We introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications.\nG. The framework is validated on 37,191 ECG images with 1596 collected at Akershus University Hospital, where the algorithm obtains a mean signal-to-noise ratio of 19.65 dB on scanned papers with common artifacts.\nH. The evidence does not state that we introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12891466", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12891466/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-08aeb92768f9d6d17f37", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBovine mastitis is a major disease affecting dairy cow health and milk production, and current treatment options still have important limitations. Previous studies have shown that a protein called cyclophilin A is closely associated with inflammatory responses during mastitis, but further research requires a stable and reliable source of this protein. In this study, we established a method for the stable expression of bovine cyclophilin A in mammalian cells and demonstrated that these cells can continuously express the target protein. This work provides a solid technical foundation for future studies on the role of this protein in bovine mastitis and for the development of related diagnostic or intervention approaches, and it may contribute to improving the prevention and control of bovine mastitis.\n\nCandidates:\nA. Previous studies have shown that a protein called cyclophilin A is closely associated with inflammatory responses during mastitis, but further research requires a stable and reliable source of this protein.\nB. Bovine mastitis is a major disease affecting dairy cow health and milk production, and current treatment options still have important limitations.\nC. In this study, we established a method for the stable expression of bovine cyclophilin A in mammalian cells and demonstrated that these cells can continuously express the target protein.\nD. The evidence does not state that this work provides a solid technical foundation for future studies on the role of this protein in bovine mastitis and for the development of related diagnostic or intervention approaches, and it may contribute to improving the prevention and control of bovine mastitis.\nE. Bovine mastitis is not a major disease affecting dairy cow health and milk production, and current treatment options still have important limitations.\nF. Previous studies have shown that a protein called cyclophilin A is not closely associated with inflammatory responses during mastitis, but further research requires a stable and reliable source of this protein.\nG. This work provides a solid technical foundation for future studies on the role of this protein in bovine mastitis and for the development of related diagnostic or intervention approaches, and it may contribute to improving the prevention and control of bovine mastitis.\nH. In this study, we established a method for the stable expression of bovine cyclophilin A in mammalian cells and demonstrated that these cells cannot continuously express the target protein.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12896659", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12896659/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ac8d75870f41bc7faf8a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicrobial biosurfactants have emerged as natural and sustainable alternatives to synthetic surfactants used in the food industry, due to the growing demand for biodegradable and safe ingredients. Produced by bacteria, fungi, and yeasts, these compounds exhibit important physicochemical properties, such as emulsifying capacity, surface tension reduction, foam stabilization, and favorable interaction with different food matrices. In addition to their technological function, they exhibit relevant biological activities, including antioxidant and antimicrobial action, which contribute to the control of lipid oxidation and microbiological deterioration. These characteristics make biosurfactants attractive for applications in emulsions, fermented beverages, aerated products, probiotic systems, and bioactive packaging. The objective of this work is to provide a narrative literature review that integrates recent advances in the production, functionality, safety, sustainability, and application perspectives of biosurfactants in the food sector. In the field of production, biotechnological advances have made it possible to overcome historical limitations such as high cost and low yield. Strategies such as the use of agro-industrial waste, metabolic engineering, microbial co-cultures, continuous fermentations, and in situ removal techniques have increased efficiency and reduced environmental impacts. Despite the advances, significant challenges remain. Future prospects and advances tend to facilitate industrial adoption and consolidate biosurfactants as strategic ingredients for the development of more sustainable, functional, and technologically advanced foods.\n\nCandidates:\nA. In addition to their technological function, they exhibit relevant biological activities, including antioxidant and antimicrobial action, which contribute to the control of lipid oxidation and microbiological deterioration.\nB. The evidence does not state that in addition to their technological function, they exhibit relevant biological activities, including antioxidant and antimicrobial action, which contribute to the control of lipid oxidation and microbiological deterioration.\nC. Microbial biosurfactants have emerged as natural and sustainable alternatives to synthetic surfactants used in the food industry, due to the growing demand for biodegradable and safe ingredients.\nD. The evidence does not state that these characteristics make biosurfactants attractive for applications in emulsions, fermented beverages, aerated products, probiotic systems, and bioactive packaging.\nE. Produced by bacteria, fungi, and yeasts, these compounds exhibit important physicochemical properties, such as emulsifying capacity, surface tension reduction, foam stabilization, and favorable interaction with different food matrices.\nF. The evidence does not state that produced by bacteria, fungi, and yeasts, these compounds exhibit important physicochemical properties, such as emulsifying capacity, surface tension reduction, foam stabilization, and favorable interaction with different food matrices.\nG. The evidence does not state that microbial biosurfactants have emerged as natural and sustainable alternatives to synthetic surfactants used in the food industry, due to the growing demand for biodegradable and safe ingredients.\nH. These characteristics make biosurfactants attractive for applications in emulsions, fermented beverages, aerated products, probiotic systems, and bioactive packaging.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12896828", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12896828/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0a24d72db6498901bff9", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCentella asiatica has emerged as a strategic biomass for the sustainable production of high-value biochemicals at the interface of traditional medicine and modern biotechnology. This review consolidates the current knowledge on its phytochemical diversity, emphasizing triterpenoid saponins—asiaticoside, madecassoside, asiatic acid, and madecassic acid—as core bioactive molecules relevant to pharmaceutical, dermatological, nutraceutical, and functional-ingredient applications. Advances in green extraction technologies, including ultrasound-assisted, microwave-assisted, ohmic-heating, and supercritical CO 2 systems, have demonstrated superior efficiency in recovering high-purity biochemicals while significantly reducing solvent use, energy demand, and environmental impact compared with conventional methods. Complementary analytical and standardization platforms, such as HPLC, UPLC, and GC–MS, enable rigorous quality control across the entire value chain, supporting the development of reproducible and regulatory-compliant biochemical extracts. From a biomass valorization and biorefinery perspective, C. asiatica offers multiple metabolite streams that align with circular economy and field-to-market sustainability principles. Key challenges remain, including agronomic variability, scaling up green extraction, and supply chain resilience. However, emerging solutions, such as Good Agricultural and Collection Practices (GACP) guided cultivation, plant tissue culture, metabolic engineering, and integrated biorefinery frameworks, show strong potential for establishing a reliable and environmentally responsible production system. Collectively, C. asiatica represents a model species for sustainable biochemical production, combining scientific efficacy with industrial, economic, and ecological relevance.\n\nCandidates:\nA. Centella asiatica has emerged as a strategic biomass for the sustainable production of high-value biochemicals at the interface of traditional medicine and modern biotechnology.\nB. Complementary analytical and standardization platforms, such as HPLC, UPLC, and GC–MS, enable rigorous quality control across the entire value chain, supporting the development of reproducible and regulatory-compliant biochemical extracts.\nC. The evidence does not state that this review consolidates the current knowledge on its phytochemical diversity, emphasizing triterpenoid saponins—asiaticoside, madecassoside, asiatic acid, and madecassic acid—as core bioactive molecules relevant to pharmaceutical, dermatological, nutraceutical, and functional-ingredient applications.\nD. The evidence does not state that centella asiatica has emerged as a strategic biomass for the sustainable production of high-value biochemicals at the interface of traditional medicine and modern biotechnology.\nE. This review consolidates the current knowledge on its phytochemical diversity, emphasizing triterpenoid saponins—asiaticoside, madecassoside, asiatic acid, and madecassic acid—as core bioactive molecules relevant to pharmaceutical, dermatological, nutraceutical, and functional-ingredient applications.\nF. The evidence does not state that complementary analytical and standardization platforms, such as HPLC, UPLC, and GC–MS, enable rigorous quality control across the entire value chain, supporting the development of reproducible and regulatory-compliant biochemical extracts.\nG. Advances in green extraction technologies, including ultrasound-assisted, microwave-assisted, ohmic-heating, and supercritical CO 3 systems, have demonstrated superior efficiency in recovering high-purity biochemicals while significantly reducing solvent use, energy demand, and environmental impact compared with conventional methods.\nH. Advances in green extraction technologies, including ultrasound-assisted, microwave-assisted, ohmic-heating, and supercritical CO 2 systems, have demonstrated superior efficiency in recovering high-purity biochemicals while significantly reducing solvent use, energy demand, and environmental impact compared with conventional methods.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12899466", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12899466/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-753f448224976559fa44", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMonoacylglycerols (MAGs) are significant intermediate byproducts in the hydrolysis of oils and fats. The accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes. Therefore, the development of efficient biocatalysts for the specific MAG conversion is of great industrial importance. The lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application. In this study, a combinatorial protein engineering strategy was employed to overcome these limitations. We integrated fusion protein technology with rational design to enhance both the functional expression and catalytic efficiency of AOL. Firstly, the superfolder green fluorescent protein (sfGFP) was fused to the N-terminus of AOL. The results indicated that the sfGFP fusion tag significantly improved the solubility and stability of the enzyme, preventing the formation of inclusion bodies. The fusion protein sfGFP-AOL exhibited a MAG conversion rate of approximately 65%, confirming the positive impact of the fusion tag on enzyme developability. To further boost catalytic performance, site-directed mutagenesis was performed based on structural analysis. Among the variants, the mutant sfGFP-Y92Q emerged as the most potent candidate. In the MAG conversion, sfGFP-Y92Q achieved a conversion rate of 98%, which was not only significantly higher than that of sfGFP-AOL but also outperformed the widely used commercial immobilized lipase, Novozym 435 (~54%). Structural modeling and docking analysis revealed that the Y92Q mutation optimized the geometry of the active site. The substitution of Tyrosine with Glutamine at position 92 likely enlarged the substrate-binding pocket and altered the local electrostatic environment, thereby relieving steric hindrance and facilitating the access of the bulky MAG substrate to the catalytic center. In conclusion, this work demonstrates that the synergistic application of sfGFP fusion and rational point mutation (Y92Q) can dramatically transform the catalytic properties of AOL. The engineered sfGFP-Y92Q variant serves as a robust and highly efficient biocatalyst for MAG degradation. Its superior performance compared to commercial standards suggests immense potential for cost-effective applications in the bio-manufacturing of high-purity fatty acids and biodiesel, offering a greener alternative to traditional chemical processes.\n\nCandidates:\nA. Therefore, the development of efficient biocatalysts for the specific MAG conversion is not of great industrial importance.\nB. The evidence does not state that the lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application.\nC. Therefore, the development of efficient biocatalysts for the specific MAG conversion is of great industrial importance.\nD. Monoacylglycerols (MAGs) are significant intermediate byproducts in the hydrolysis of oils and fats.\nE. The evidence does not state that the accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes.\nF. Monoacylglycerols (MAGs) are not significant intermediate byproducts in the hydrolysis of oils and fats.\nG. The lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application.\nH. The accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12899640", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12899640/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e4e4393f4a3a5e136bca", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDetergent-compatible lipases are increasingly valued for their ability to remove stains under low-temperature and environmentally friendly washing conditions. Their industrial applicability depends on achieving high enzyme production, cost-effective purification, and stability within detergent formulations. Here, we report the purification and characterization of a highly active extracellular lipase from Streptomyces sp. AU-153 (1543 U/mL, p -NPP assay). A simplified aqueous two-phase system (ATPS) of poly­(ethylene glycol) and sodium chloride achieved 8-fold purification with a recovery of 272.7%. The purified enzyme exhibited optimal activity at pH 8.0 and 40 °C, maintained stability across pH 7–11, and retained substantial activity up to 60 °C. Activity was enhanced by Ca 2+ , Mg 2+ , and β-mercaptoethanol, whereas PMSF inhibited activity. The lipase remained stable in various commercial detergents and in the presence of surfactants, oxidizing agents, and boron compounds. It also showed affinity toward sunflower and thermally degraded olive oils. Low-temperature washing assays confirmed its effectiveness in oil stain removal. To our knowledge, ATPS-based purification and washing performance of Streptomyces lipases have each been reported only once, and this study is the first to integrate both approaches for the same enzyme. Moreover, Streptomyces sp. AU-153 displayed one of the highest native extracellular lipase activities documented for the genus, while the ATPS protocol achieved one of the highest recoveries reported for microbial lipases. These findings establish strain AU-153 as a promising natural source of detergent-compatible lipases and highlight its potential for enzyme-based washing applications.\n\nCandidates:\nA. Here, we report the purification and characterization of a highly active extracellular lipase from Streptomyces sp.\nB. A simplified aqueous two-phase system (ATPS) of poly­(ethylene glycol) and sodium chloride achieved 8-fold purification with a recovery of 272.7%.\nC. Detergent-compatible lipases are increasingly valued for their ability to remove stains under low-temperature and environmentally friendly washing conditions.\nD. Their industrial applicability depends on achieving high enzyme production, cost-effective purification, and stability within detergent formulations.\nE. The evidence does not state that here, we report the purification and characterization of a highly active extracellular lipase from Streptomyces sp.\nF. A simplified aqueous two-phase system (ATPS) of poly­(ethylene glycol) and sodium chloride achieved 9-fold purification with a recovery of 272.7%.\nG. The evidence does not state that their industrial applicability depends on achieving high enzyme production, cost-effective purification, and stability within detergent formulations.\nH. Detergent-compatible lipases are not increasingly valued for their ability to remove stains under low-temperature and environmentally friendly washing conditions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12902849", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12902849/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-77dd252352d328c94919", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nExtracellular vesicles (EVs) are heterogeneous, lipid bilayer-enclosed vesicles secreted by cells. Research on EVs dates back to the 1940s, and the term “exosomes” - a major subtype of EVs - was coined in 1981 to describe small membrane vesicles shed from cells. However, it is only in the past two decades that research in this area has expanded rapidly. By transferring functional biomolecules, EVs play a pivotal role in intercellular communication and regulate a wide range of cellular functions under both physiological and pathological conditions. Owing to their high biocompatibility, capacity to protect encapsulated cargo from degradation, and ability to cross biological barriers, EVs also show great promise as biomarkers and drug-delivery systems. Following the first, albeit unintentional, isolation of EVs in 1946, the 80th anniversary of EV research is now approaching. In this review, we trace the history of EV research and summarize key advances in the field. We also discuss current challenges and future prospects in this rapidly evolving area.\n\nCandidates:\nA. By transferring functional biomolecules, EVs play a pivotal role in intercellular communication and regulate a wide range of cellular functions under both physiological and pathological conditions.\nB. Extracellular vesicles (EVs) are heterogeneous, lipid bilayer-enclosed vesicles secreted by cells.\nC. Extracellular vesicles (EVs) are not heterogeneous, lipid bilayer-enclosed vesicles secreted by cells.\nD. However, it is not only in the past two decades that research in this area has expanded rapidly.\nE. Research on EVs dates back to the 1941s, and the term “exosomes” - a major subtype of EVs - was coined in 1981 to describe small membrane vesicles shed from cells.\nF. However, it is only in the past two decades that research in this area has expanded rapidly.\nG. The evidence does not state that by transferring functional biomolecules, EVs play a pivotal role in intercellular communication and regulate a wide range of cellular functions under both physiological and pathological conditions.\nH. Research on EVs dates back to the 1940s, and the term “exosomes” - a major subtype of EVs - was coined in 1981 to describe small membrane vesicles shed from cells.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12902917", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12902917/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a742b660496c76469f93", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"4.97 g/L\", \"2.28 g/L\", \"9.26 g/L\", \"3.28 g/L\", \"5.96 g/L\", \"3.97 g/L\", \"10.26 g/L\", \"4.96 g/L\"]\n\nEvidence:\nCaldimonas thermodepolymerans DSM 15344, a moderately thermophilic bacterium, has emerged as a promising candidate for next-generation industrial biotechnology (NGIB) due to its ability to utilize lignocellulose-derived sugars for polyhydroxyalkanoate (PHA) production. This study assesses its metabolic potential by evaluating the utilization of various plant-derived sugars and their mixtures, with a focus on xylose, glucose, and cellobiose. The results indicate that C. thermodepolymerans exhibits a strong preference for xylose (3.97 g/L PHB) over glucose (2.28 g/L PHB) but demonstrates even greater efficiency in metabolizing cellobiose (4.96 g/L PHB). However, extracellular hydrolysis of cellobiose leads to glucose accumulation, which constrains overall productivity. Our findings suggest that the primary limitation in glucose metabolism is inefficient glucose transport rather than intracellular catabolism. To address this bottleneck, the glf glucose facilitator gene from the mesophilic bacterium Zymomonas mobilis was introduced into C. thermodepolymerans , enhancing its glucose utilization capacity. The engineered strain (Cald_GLF3) exhibited significantly improved PHA productivity, particularly when cultivated on sugar mixtures containing cellobiose. Despite being grown at suboptimal temperatures due to the thermal instability of Glf from Z. mobilis , Cald_GLF3 outperformed the wild-type strain, achieving notably high PHA yields when cultivated with cellobiose as the sole carbon source (9.26 g/L PHB). These findings highlight the critical role of glucose transport in the metabolism of C. thermodepolymerans and suggest that targeted engineering can further enhance its biotechnological potential. This study establishes C. thermodepolymerans as a promising thermophilic chassis for PHA production from lignocellulosic sugars, contributing to sustainable biopolymer synthesis. C. thermodepolymerans DSM 15344 produces PHA from lignocellulose-derived sugars Xylose and cellobiose are preferred substrates, while glucose is poorly utilized Deficient glucose transport in DSM 15344 restored by Zymomonas mobilis glf gene Keywords: Caldimonas thermodepolymerans , Polyhydroxyalkanoates, Thermophiles, Sugar metabolism, Glucose transporters, Lignocelluloses", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12906555", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12906555/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-638a2d462d78c2a399b6", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPseudomonas aeruginosa is a major human opportunistic pathogen associated with a high incidence of multi-drug resistance. The antibody-based blockade of P. aeruginosa virulence factors represents a promising alternative strategy to mitigate its infectivity. In this study, we employed single B cell sorting from cystic fibrosis patients to isolate human monoclonal antibodies (mAbs) targeting proteins from the P. aeruginosa Type 3 Secretion System (T3SS) and characterized a panel of mAbs directed at PscF and PcrV. Among those, two mAbs, P5B3 and P3D6, that bind to the injectisome tip protein PcrV, exhibited T3SS blocking activity. We solved the crystal structure of the P3D6 Fab-PcrV complex, which revealed that the Ab binds to the C-terminal region of PcrV. In addition, we compared the T3SS-blocking activity of three PcrV-targeting mAbs, including two from previous independent studies, using two distinct assays to evaluate pore formation and toxin injection. We conducted a mechanistic and structural analysis of their modes of action through modeling based on the known structure of a functional homolog, SipD from Salmonella typhimurium . The analysis suggests that anti-PcrV mAbs may act through different mechanisms, ranging from preventing PcrV oligomerization to disrupting PcrV’s scaffolding function, thereby inhibiting the assembly and function of the translocon pore. Our findings provide additional evidence that T3SS-targeting Abs, some capable of inhibiting virulence, are elicited in P. aeruginosa -infected patients. The results offer deeper insights into PcrV recognition by mAbs and their associated mechanisms of action, helping to identify which Abs are more likely to be therapeutically useful based on their mode of action and potency. This paves the way for the development of effective alternatives to traditional antibiotics in the fight against this resilient pathogen.\n\nCandidates:\nA. The evidence does not state that aeruginosa virulence factors represents a promising alternative strategy to mitigate its infectivity.\nB. Pseudomonas aeruginosa is not a major human opportunistic pathogen associated with a high incidence of multi-drug resistance.\nC. Pseudomonas aeruginosa is a major human opportunistic pathogen associated with a high incidence of multi-drug resistance.\nD. aeruginosa virulence factors represents a promising alternative strategy to mitigate its infectivity.\nE. The evidence does not state that in this study, we employed single B cell sorting from cystic fibrosis patients to isolate human monoclonal antibodies (mAbs) targeting proteins from the P.\nF. aeruginosa Type 4 Secretion System (T3SS) and characterized a panel of mAbs directed at PscF and PcrV.\nG. aeruginosa Type 3 Secretion System (T3SS) and characterized a panel of mAbs directed at PscF and PcrV.\nH. In this study, we employed single B cell sorting from cystic fibrosis patients to isolate human monoclonal antibodies (mAbs) targeting proteins from the P.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12912723", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12912723/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-69743612e38313d1bfca", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nM9 minimal media and its enhanced variants (M9 + and M9++) are widely used for recombinant protein expression in Escherichia coli, particularly for isotopic labeling required in structural biology techniques such as NMR spectroscopy. This study investigates how different compositions of M9-based media (M9, M9+, and M9++) influence bacterial growth, metabolic stress, and central carbon metabolism during recombinant expression of the protein. Using 1D ¹H NMR spectroscopy and multivariate statistical analysis, we observed distinct media-dependent metabolic shifts. Standard M9 exhibited limited bacterial growth and heightened stress-related fermentation, indicated by high ethanol and acetate levels. In contrast, M9 + significantly increased biomass but promoted pronounced overflow metabolism. M9 + + presented intermediate biomass levels and markedly reduced overflow metabolites, favoring biosynthesis pathways, notably increasing valine, acetoin, and formate concentrations. These findings suggest that further optimization of glucose concentration, nitrogen sources, and phosphate buffering could significantly improve the metabolic balance of M9++, creating an enhanced medium tailored for efficient, high-quality recombinant protein expression and isotopic labeling in E. coli . Keywords: Recombinant protein expression, M9 minimal media, Metabolomics, NMR spectroscopy, Isotopic labeling\n\nCandidates:\nA. This study investigates how different compositions of M9-based media (M9, M9+, and M9++) influence bacterial growth, metabolic stress, and central carbon metabolism during recombinant expression of the protein.\nB. M10 minimal media and its enhanced variants (M9 + and M9++) are widely used for recombinant protein expression in Escherichia coli, particularly for isotopic labeling required in structural biology techniques such as NMR spectroscopy.\nC. Using 1D ¹H NMR spectroscopy and multivariate statistical analysis, we observed distinct media-dependent metabolic shifts.\nD. Using 2D ¹H NMR spectroscopy and multivariate statistical analysis, we observed distinct media-dependent metabolic shifts.\nE. Standard M9 exhibited limited bacterial growth and heightened stress-related fermentation, indicated by high ethanol and acetate levels.\nF. Standard M10 exhibited limited bacterial growth and heightened stress-related fermentation, indicated by high ethanol and acetate levels.\nG. M9 minimal media and its enhanced variants (M9 + and M9++) are widely used for recombinant protein expression in Escherichia coli, particularly for isotopic labeling required in structural biology techniques such as NMR spectroscopy.\nH. This study investigates how different compositions of M10-based media (M9, M9+, and M9++) influence bacterial growth, metabolic stress, and central carbon metabolism during recombinant expression of the protein.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12913276", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12913276/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6c700ee0432f889db623", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nRadiation enteritis is a common complication in patients undergoing abdominal radiotherapy. Current management strategies face significant limitations: clinical agents like amifostine are hindered by systemic side effects and demanding administration; direct supplementation with radioprotective metabolites such as propionate suffers from low bioavailability and transient action; and conventional probiotics lack targeted therapeutic output. To address these challenges, we engineered Escherichia coli Nissle 1917 to function as a living therapeutic that continuously produces and delivers propionate directly in the gut. This propionate-engineered probiotic achieved a production yield of 181.33 ± 4.27 mg/L in vitro. In a mouse model of abdominal irradiation, this engineered bacterium alleviated radiation-induced intestinal damage by continuously releasing propionate and enhancing intestinal epithelial barrier function. Multi-omics analysis revealed that the engineered bacterium could restore intestinal microbiota homeostasis, enhancing the abundance of advantageous bacteria with radioprotective properties (e.g., Dubosiella , Akkermansia ). Moreover, it modulated intestinal microbiota metabolism, influencing the metabolism of ascorbic acid, aldoses, and other metabolites. Additionally, it protected the intestinal mucosal barrier from radiation-induced damage, which was associated with the modulation of the SOCS1/JAK2/STAT3 signaling pathway. This study introduces a novel biological therapy to mitigate the side effects of radiotherapy and could open new avenues for preventing and treating radiation-induced intestinal injury. The online version contains supplementary material available at 10.1186/s40643-026-01020-9.\n\nCandidates:\nA. Current management strategies face significant limitations: clinical agents like amifostine are not hindered by systemic side effects and demanding administration; direct supplementation with radioprotective metabolites such as propionate suffers from low bioavailability and transient action; and conventional probiotics lack targeted therapeutic output.\nB. To address these challenges, we engineered Escherichia coli Nissle 1918 to function as a living therapeutic that continuously produces and delivers propionate directly in the gut.\nC. This propionate-engineered probiotic achieved a production yield of 182.33 ± 4.27 mg/L in vitro.\nD. This propionate-engineered probiotic achieved a production yield of 181.33 ± 4.27 mg/L in vitro.\nE. Current management strategies face significant limitations: clinical agents like amifostine are hindered by systemic side effects and demanding administration; direct supplementation with radioprotective metabolites such as propionate suffers from low bioavailability and transient action; and conventional probiotics lack targeted therapeutic output.\nF. Radiation enteritis is a common complication in patients undergoing abdominal radiotherapy.\nG. To address these challenges, we engineered Escherichia coli Nissle 1917 to function as a living therapeutic that continuously produces and delivers propionate directly in the gut.\nH. Radiation enteritis is not a common complication in patients undergoing abdominal radiotherapy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12913845", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12913845/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a2fb8455c732222feb8e", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"49 h\", \"48 h\"]\n\nEvidence:\nTyrosinase is a binuclear copper oxidase central to melanogenesis and food browning and is a major target for depigmenting and anti-browning agents. Here we evaluate Altenusin, a fungal carboxy-biphenyl polyketide, as a tyrosinase-inhibitor scaffold by combining structure-based screening, enhanced fermentation and mechanistic enzymology. Docking against the mushroom tyrosinase Agaricus bisporus PPO 3 (AbPPO 3 ) highlighted Altenusin as a presumed dicopper-site binder, and genome mining of the producer strain revealed a polyketide synthase gene cluster consistent with its biosynthesis. Fermentation optimization and bioreactor transfer increased Altenusin titers up to 0.254 ± 0.022 g L −1 . In vitro , Altenusin inhibited in a substrate-dependent manner, with IC 50 values of 0.381 ± 0.002 mM ( l -tyrosine) and 0.162 ± 0.023 mM ( l -DOPA); kinetic analysis indicated competitive monophenolase inhibition and mixed-type diphenolase inhibition. Altenusin also showed strong radical-scavenging and copper-reducing activity, moderate Cu 2+ chelation and a narrow cytotoxicity window in HepG2 cells (48 h, CC 50 : 0.093 mM). Overall, these data define Altenusin as a biotechnologically tractable starting point for fungal carboxy-biphenyl inhibitor discovery.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12917734", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12917734/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a010d5b7fdca8fe4bd6e", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nOver the past two decades, Meyerozyma caribbica has been identified as a metabolically versatile and ecologically adaptable yeast with significant relevance to biotechnology, agriculture, environmental remediation, and food applications. Since its formal description in 2005, this species has demonstrated the ability to grow on a wide range of substrates and under various stress conditions, facilitating the production of valuable bioproducts such as ethanol, xylitol, arabitol, and volatile aroma compounds. Multiple strains efficiently ferment lignocellulosic hydrolysates, tolerate inhibitory compounds, and remain active at elevated temperatures, which supports their application in integrated biorefineries. In addition to its fermentative capabilities, M. caribbica serves as an effective biocontrol agent through the production of antifungal metabolites, hydrolytic enzymes, mycoparasitism, nutrient competition, and the induction of plant defense responses. Environmental functions include the degradation of dyes, hydrocarbons, and organochlorine pesticides, as well as metal biosorption and the mitigation of oxidative stress in plants. There is also increasing interest in its potential as a probiotic and as a starter culture that can modulate sensory attributes in fermented foods. This review synthesizes 20 years of research on M. caribbica , focusing on its roles in bioproduct production, plant disease management, bioremediation, and probiotic or food-related applications. Keywords: biodegradation, non-conventional yeasts, biocontrol, probiotics, xylitol\n\nCandidates:\nA. Since its formal description in 2006, this species has demonstrated the ability to grow on a wide range of substrates and under various stress conditions, facilitating the production of valuable bioproducts such as ethanol, xylitol, arabitol, and volatile aroma compounds.\nB. Over the past two decades, Meyerozyma caribbica has been identified as a metabolically versatile and ecologically adaptable yeast with significant relevance to biotechnology, agriculture, environmental remediation, and food applications.\nC. The evidence does not state that multiple strains efficiently ferment lignocellulosic hydrolysates, tolerate inhibitory compounds, and remain active at elevated temperatures, which supports their application in integrated biorefineries.\nD. In addition to its fermentative capabilities, M.\nE. Since its formal description in 2005, this species has demonstrated the ability to grow on a wide range of substrates and under various stress conditions, facilitating the production of valuable bioproducts such as ethanol, xylitol, arabitol, and volatile aroma compounds.\nF. The evidence does not state that over the past two decades, Meyerozyma caribbica has been identified as a metabolically versatile and ecologically adaptable yeast with significant relevance to biotechnology, agriculture, environmental remediation, and food applications.\nG. Multiple strains efficiently ferment lignocellulosic hydrolysates, tolerate inhibitory compounds, and remain active at elevated temperatures, which supports their application in integrated biorefineries.\nH. The evidence does not state that in addition to its fermentative capabilities, M.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12923170", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12923170/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d3b4e3b6a0f2ea108445", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMethylococcus species utilize methane as the sole carbon and energy source, converting it into biomass and other metabolic end products. Owing to this metabolic capacity, they hold particular promise in industrial C1 biotechnology, especially for the production of protein-rich feed. However, the industrial cultivation of Methylococcus -based consortia on methane is inherently nonsterile, exposing the process to potential biological risks that may compromise the stability, duration and productivity of cultivation. One of the most critical threats is bacteriophage infection, whose triggers for rapid phage-mediated lysis and resulting economic losses remain incompletely understood. Elucidating these processes is paramount for devising strategies to mitigate or prevent detrimental outcomes. In this investigation, nine publicly accessible genomes of Methylococcus species were examined, culminating in the identification of eleven prophage sequences distributed variably among the genomes. Sequence annotations revealed that nine prophages are potentially functional and intact, whereas the rest carry incomplete gene sets indicative of nonviability. Phylogenetic analyses corroborated the substantial diversity of prophages, which formed distinct clusters related to γ-proteobacteria phages. Furthermore, comparative genomic analyses demonstrated a high degree of structural conservation despite the presence of rearrangements. The annotation of the CRISPR‒Cas systems provided insights into additional dimensions of phage‒bacteria interactions. Examination of prophage integration sites did not reveal any disruption of metabolic gene structures, thus suggesting minimal risk of deleterious phenotypic outcomes. These findings considerably advance the current understanding of the genetic diversity and biological properties of prophages infecting Methylococcus species, underscoring the importance of holistic approaches for the detection and analysis of these elements. Our findings underscore the need for routine prophage monitoring in industrial methanotrophic consortia, with the pipeline established here serving as a foundational framework for future refinement and industrial adaptation. The online version contains supplementary material available at 10.1186/s13068-026-02738-6. Keywords: Bacteriophages, Prophages, Methane, Methylococcus , Gaprin, Bioinformatics, Genomic analysis, CRISPR‒Cas, Comparative genomics, Biotechnology, Industrial bioconversion", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12924245", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12924245/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9d679f2c671b4a351ada", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHigh-throughput yeast engineering is being transformed by biofoundries that integrate automation, artificial intelligence (AI), and standardized workflows. This review examines how these facilities accelerate strain development through the Design-Build-Test-Learn (DBTL) cycle, with advances in genome editing, phenotypic screening, and predictive modelling. It highlights Australia’s involvement through the Australian Genome Foundry, Idea-BIO, and the CSIRO Biofoundiry and explores global efforts to overcome reproducibility and standardization challenges. Despite progress, key barriers remain, including protocol variability and integration of AI tools. We also highlight the opportunity for a shift toward autonomous, self-optimizing ‘self-driving labs’ that transition from DBTL to Design-Build-Deploy cycles. The future of yeast engineering depends not only on technological innovation, but also on the harmonization of international standards, data governance, and ethical safeguards. If fully realized, the convergence of robotics, AI, and synthetic biology will redefine yeast engineering, leading to step changes in strain performance for a variety of important products, thus enabling economic and sustainable biomanufacturing at scale. Keywords: biofoundry, synthetic biology, engineering biology, machine learning\n\nCandidates:\nA. Despite progress, key barriers remain, including protocol variability and integration of AI tools.\nB. High-throughput yeast engineering is being transformed by biofoundries that integrate automation, artificial intelligence (AI), and standardized workflows.\nC. This review examines how these facilities accelerate strain development through the Design-Build-Test-Learn (DBTL) cycle, with advances in genome editing, phenotypic screening, and predictive modelling.\nD. High-throughput yeast engineering is not being transformed by biofoundries that integrate automation, artificial intelligence (AI), and standardized workflows.\nE. The evidence does not state that it highlights Australia’s involvement through the Australian Genome Foundry, Idea-BIO, and the CSIRO Biofoundiry and explores global efforts to overcome reproducibility and standardization challenges.\nF. The evidence does not state that this review examines how these facilities accelerate strain development through the Design-Build-Test-Learn (DBTL) cycle, with advances in genome editing, phenotypic screening, and predictive modelling.\nG. It highlights Australia’s involvement through the Australian Genome Foundry, Idea-BIO, and the CSIRO Biofoundiry and explores global efforts to overcome reproducibility and standardization challenges.\nH. The evidence does not state that despite progress, key barriers remain, including protocol variability and integration of AI tools.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12927428", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12927428/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f3d54042ef6b12822f6a", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nRecombinant protein expression in mycobacteria faces two major challenges: limited regulatory tools for inducible expression and inefficient secretion of heterologous products. In this study, we developed plasmid-based systems that enable translationally gated secretion in Mycobacterium smegmatis , coupling riboswitch-mediated translational control with efficient extracellular export. The platform integrates the M. tuberculosis antigen 85A promoter and signal peptide for constitutive secretion combined with synthetic riboswitches for inducible translational regulation. We tested two theophylline-responsive riboswitches (riboE and riboE+) and a temperature-sensitive variant (riboU9) by using mCherry as a reporter. Fluorescence assays, RT-PCR, and Western blotting confirmed efficient secretion and strict translational control. The theophylline-inducible systems exhibited a dose-dependent response with maximal expression at 2 mM inducer, while the riboU9 construct showed a clean ON/OFF phenotype triggered by temperature shift. In all cases, transcripts were detected irrespective of induction, confirming regulation at the translational rather than transcriptional level. Secretion was highly efficient, with 10–20 fold higher protein levels in extracellular versus intracellular fractions. Induction during early- and mid-log phases yielded maximal protein, whereas late-log induction reduced output by ∼50%. Together, these results define translationally gated secretion as a new control layer in mycobacterial protein production. This modular platform expands the genetic toolkit available for Mycobacterium research, providing new opportunities for the study of antigens and virulence factors from slow-growing pathogens and offering potential applications in structural biology, vaccine development, and drug target validation.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12930493", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12930493/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2fa8416d171edd72ed39", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPlastic waste, especially from packaging, poses major recycling challenges due to the presence of mixed polymers, which often result in inconsistent blends that are unsuitable for reuse in food-grade applications. Chemical recycling, particularly alkaline hydrolysis, offers a promising solution in the case of chemically reactive polymers, such as polyesters, with poly­(ethylene terephthalate) (PET) being one of the dominant plastics suitable for both mechanical and chemical recycling. Mechanical recycling is currently used for the largest part of PET recycling, due to the fact that turning the polymer back into its monomeric building blocks requires catalysts, elevated temperatures, or prolonged reaction times. This study presents a recently developed Heated High-Ethanol Alkaline Aqueous (HHeAA) process that enables efficient, catalyst-free PET hydrolysis under milder conditions. Nearly complete hydrolysis was achieved within just 20 min at 90 °C using a loading of 0.624 g of NaOH/g of PET. The process was successfully scaled up with commercial PET bottles, achieving full hydrolysis while significantly reducing the liquid-to-solid ratio from 20 to just 5 L/kg. These results highlight the industrial potential of the HHeAA method as a more sustainable and energy-efficient alternative for PET recycling and chemical reuse and in turn reduced environmental impact.\n\nCandidates:\nA. The evidence does not state that chemical recycling, particularly alkaline hydrolysis, offers a promising solution in the case of chemically reactive polymers, such as polyesters, with poly­(ethylene terephthalate) (PET) being one of the dominant plastics suitable for both mechanical and chemical recycling.\nB. Mechanical recycling is currently used for the largest part of PET recycling, due to the fact that turning the polymer back into its monomeric building blocks requires catalysts, elevated temperatures, or prolonged reaction times.\nC. The evidence does not state that this study presents a recently developed Heated High-Ethanol Alkaline Aqueous (HHeAA) process that enables efficient, catalyst-free PET hydrolysis under milder conditions.\nD. Chemical recycling, particularly alkaline hydrolysis, offers a promising solution in the case of chemically reactive polymers, such as polyesters, with poly­(ethylene terephthalate) (PET) being one of the dominant plastics suitable for both mechanical and chemical recycling.\nE. Plastic waste, especially from packaging, poses major recycling challenges due to the presence of mixed polymers, which often result in inconsistent blends that are unsuitable for reuse in food-grade applications.\nF. This study presents a recently developed Heated High-Ethanol Alkaline Aqueous (HHeAA) process that enables efficient, catalyst-free PET hydrolysis under milder conditions.\nG. Plastic waste, especially from packaging, poses major recycling challenges due to the presence of mixed polymers, which often result in inconsistent blends that are not unsuitable for reuse in food-grade applications.\nH. Mechanical recycling is not currently used for the largest part of PET recycling, due to the fact that turning the polymer back into its monomeric building blocks requires catalysts, elevated temperatures, or prolonged reaction times.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12930499", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12930499/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ba1668ad0b18d5861589", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nThe growing demand for sustainable alternatives to fossil-based chemicals has increased interest in platform chemicals derived from renewable biomass sources, such as malic acid. This C4 dicarboxylic acid is valued for its diverse application potential in food, pharmaceuticals, and bioplastics. Sustainable platform chemicals remain commercially uncompetitive primarily due to high production costs driven by high substrate costs. Microbial production using more cost-effective feedstocks like sugar beet molasses shows promise. However, it faces challenges from high osmolality, growth inhibitors, and predetermined substrate composition during fermentation, as well as elevated pigmentation that complicates downstream processing. Moreover, the separation techniques typically used for highly polar carboxylic acids face considerable yield limitations due to the high solubility of malic acid and its salts. This study developed an all-encompassing production process for malic acid from untreated sugar beet molasses. Fermentative malic acid production with Ustilago trichophora was investigated in batch, fed-batch, and pulsed batch in shake flask scale, followed by a scale-up into 150 L pilot scale. A total of 15.7 kg malic acid was produced in a repeated pulsed batch with membrane-based cell retention with a titer of 108 g/L, a yield of 0.50 g/g, and a space–time yield of 0.66 g/L/h (max. 1.1 g/L/h). In addition, the byproduct succinic acid was detected in concentrations of up to 22.9 g/L. In the subsequent downstream processing, activated carbons were used for two-stage product capture, solvent change, and decolorization, followed by crystallization of the products malic acid and succinic acid. Based on experimental results, an Aspen Plus model was developed to estimate the overall process yields of 0.43 g malic acid (98% purity) and 0.10 g succinic acid per gram sucrose equivalent. A techno-economic analysis suggests production costs within the range of current market prices. Agricultural residue streams are often proposed as cost-effective alternatives for fermentative platform chemical production, although the challenges addressed hamper the direct transfer of process strategies from established organic acid production. By presenting a holistic approach explicitly tailored to malic acid production from untreated molasses, this work demonstrates the techno-economic feasibility of the developed process at a meaningful scale. The online version contains supplementary material available at 10.1186/s13068-026-02736-8.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12930559", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12930559/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-28b48ed24f155990dfe3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nYarrowia lipolytica is an emerging host for producing acetyl-CoA– and malonyl-CoA–derived chemicals. However, most processes rely on yeast nitrogen base (YNB), a historical formulation with poorly controlled trace metal content. This variability impairs metabolic performance, limits reproducibility, and complicates process transfer. Commercial YNB batches differed markedly, causing 1.5–2-fold variation in growth and docosahexaenoic acid (DHA) production. We developed a malonyl-CoA–responsive flaviolin reporter strain and combined it with a structured Design of Experiments (DoE) workflow to systematically re-engineer YNB mineral composition. Dissection of all 20 YNB components revealed that vitamins are dispensable under the tested conditions, whereas a small subset of salts and trace elements - particularly ZnSO 4 , FeCl 3 , KH 2 PO 4 , MgSO 4 , CaCl 2 , and CuSO 4 - dominantly shape precursor availability and product formation. One-factor-at-a-time (OFAT), factorial, steepest ascent, and central composite designs converged in an optimized synthetic mineral medium assembled entirely from individual salts and trace metals. This formulation increased flaviolin titers to 1.41 ± 0.08 g L -1 , a more-than threefold improvement over commercial YNB, while ensuring high reproducibility. Key mineral interventions also translated to complex pathways: omission of ZnSO 4 increased PUFA titers by 7.6-fold (docosapentaenoic acid, DPA) and 58-fold (eicosapentaenoic acid, EPA) and enhanced DHA formation in independent production strains. The defined formulation substantially reduces cost and eliminates batch-to-batch variability inherent to commercial YNB powders. Our results establish mineral balancing as a major yet underused lever for improving acetyl-CoA– and malonyl-CoA–derived production in Y. lipolytica and demonstrate a generalizable, model-guided workflow for creating simplified, reproducible, and cost-efficient synthetic media for non-conventional yeast cell factories. The online version contains supplementary material available at 10.1186/s12934-026-02939-6. Keywords: Yarrowia lipolytica, Medium engineering, Yeast nitrogen base, Design of experiments, Flaviolin reporter, acetyl-CoA and malonyl-CoA metabolism, Omega-3 fatty acids, Zinc and iron homeostasis\n\nCandidates:\nA. The evidence does not state that this variability impairs metabolic performance, limits reproducibility, and complicates process transfer.\nB. Commercial YNB batches differed markedly, causing 1.5–2-fold variation in growth and docosahexaenoic acid (DHA) production.\nC. Commercial YNB batches differed markedly, causing 2.5–2-fold variation in growth and docosahexaenoic acid (DHA) production.\nD. Yarrowia lipolytica is not an emerging host for producing acetyl-CoA– and malonyl-CoA–derived chemicals.\nE. The evidence does not state that however, most processes rely on yeast nitrogen base (YNB), a historical formulation with poorly controlled trace metal content.\nF. This variability impairs metabolic performance, limits reproducibility, and complicates process transfer.\nG. However, most processes rely on yeast nitrogen base (YNB), a historical formulation with poorly controlled trace metal content.\nH. Yarrowia lipolytica is an emerging host for producing acetyl-CoA– and malonyl-CoA–derived chemicals.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12930956", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12930956/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c79a89f2301ec435fc1f", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nOptimising continuous phototrophic cultivation remains a major challenge for scalable, energy-efficient cyanobacterial bioprocesses. Here, we combine controlled photophysiology, long-term continuous experimentation, multi-parameter analysis, and batch-derived Monod kinetic modelling to define a precise operational window for Synechocystis sp. PCC 6803 under flat-plate photobioreactor (FP-PBR) illumination. Using a fully calibrated FP-PBR platform, we first quantified intrinsic growth limits ( µ max = 0.081–0.118 day −1 ) across low, moderate, and high irradiance regimes, establishing the illumination-driven growth ceilings that constrain downstream continuous operation. Guided by these kinetic boundaries, continuous cultivation demonstrated that productive steady-state growth emerges only within a narrow regime governed by light intensity (500–700 µmol photons m −2 s −1 ), temperature (32–34 °C), and dilution rate (0.12–0.14 day −1 ). Single-parameter and 3D interaction analyses revealed strong coupling between photonic supply, thermal sensitivity, and hydraulic residence time, while multi-factor modelling captured these nonlinear constraints and accurately predicted washout boundaries. Translating these insights into sustainability metrics, the optimised regime supports 0.07–0.125 g L −1 day −1 of biomass productivity, equivalent to 8.4–15.0 g biomass day −1 and 176–315 kJ day −1 of chemical energy in a 120 L mini-pilot system. Stoichiometric analysis indicates this corresponds to 15.6–27.6 g CO 2 day −1 sequestered, demonstrating measurable environmental benefit even at a small scale. Together, these results provide a mechanistically grounded, kinetically constrained framework for designing inherently efficient, low-waste, and model-predictive cyanobacterial photobioprocesses aligned with green chemistry and future carbon-neutral manufacturing.\n\nCandidates:\nA. The evidence does not state that optimising continuous phototrophic cultivation remains a major challenge for scalable, energy-efficient cyanobacterial bioprocesses.\nB. Using a fully calibrated FP-PBR platform, we first quantified intrinsic growth limits ( µ max = 0.081–0.118 day −1 ) across low, moderate, and high irradiance regimes, establishing the illumination-driven growth ceilings that constrain downstream continuous operation.\nC. The evidence does not state that here, we combine controlled photophysiology, long-term continuous experimentation, multi-parameter analysis, and batch-derived Monod kinetic modelling to define a precise operational window for Synechocystis sp.\nD. Optimising continuous phototrophic cultivation remains a major challenge for scalable, energy-efficient cyanobacterial bioprocesses.\nE. Using a fully calibrated FP-PBR platform, we first quantified intrinsic growth limits ( µ max = 1.081–0.118 day −1 ) across low, moderate, and high irradiance regimes, establishing the illumination-driven growth ceilings that constrain downstream continuous operation.\nF. Here, we combine controlled photophysiology, long-term continuous experimentation, multi-parameter analysis, and batch-derived Monod kinetic modelling to define a precise operational window for Synechocystis sp.\nG. PCC 6803 under flat-plate photobioreactor (FP-PBR) illumination.\nH. PCC 6804 under flat-plate photobioreactor (FP-PBR) illumination.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12933868", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12933868/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-df3c8e122050fc3296d9", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHyaluronic acid (HA) is a glycosaminoglycan with a wide range of biological functions that depend on its molecular weight (MW). Recently, there has been an increasing interest in producing HA at particular MWs for various cosmetic and biomedical applications. HA is traditionally produced by extraction or microbial fermentation, which is then subjected to chemical or enzymatic treatments to customize the MW. On the other hand, direct microbial synthesis at desired MWs has considerable advantages over conventional techniques. The present study introduces a combinatorial approach using four critical variables which influence the molecular weight of HA (MW HA ): (1) Expression of HA synthases from different Streptococcus species ( S. parauberis , S. uberis , S. zooepidemicus , and S. pyogenes ) which intrinsically produce different MW HA ; (2) Supply of HA precursors by varying heterlogous gene expression in the HA-precursor pathways ( hasAB vs. hasABE ); (3) Re-routing of metabolic fluxes by deletion of the lactate dehydrogenase ( ldh ) gene; and (4) Varying the initial glucose concentration in batch fermentation. Recombinant Lactococcus lactis strains expressing HA synthase genes taken from diverse Streptococcal sp. were found to produce varying MW HA under otherwise identical genetic and bioreactor conditions. The HA synthases sourced from S. uberis and S. parauberis synthesized higher MW HA , whereas those from S. pyogenes produced lower MW HA . In silico analysis of the HA synthase sequences indicated that differences in the transmembrane regions among the various isoforms are the probable cause of variations in MW HA . Compared to their wild-type counterparts, ldh -knockout L. lactis strains showed a noticeable increase in MW HA due to a substantial increase in HA precursor levels. Further, the co-expression of hasE in addition to hasAB , considerably increased MW HA due to a better balance of the intracellular HA-precursor ratios. This multiplexing approach, involving simultaneous manipulation of the above factors, allowed us to produce HA with tailored MW HA over a broad range from 0.2 to 2.6 MDa. Our technology eliminates the need for enzymatic desizing or post-processing of HA to achieve the desired MW HA . In summary, this multiplexing approach enables one-pot synthesis of desired MW HA , opening up new avenues for producing customized HA. The online version contains supplementary material available at 10.1186/s12934-026-02945-8.\n\nCandidates:\nA. HA is not traditionally produced by extraction or microbial fermentation, which is then subjected to chemical or enzymatic treatments to customize the MW.\nB. On the other hand, direct microbial synthesis at desired MWs has considerable advantages over conventional techniques.\nC. Hyaluronic acid (HA) is a glycosaminoglycan with a wide range of biological functions that depend on its molecular weight (MW).\nD. The evidence does not state that on the other hand, direct microbial synthesis at desired MWs has considerable advantages over conventional techniques.\nE. HA is traditionally produced by extraction or microbial fermentation, which is then subjected to chemical or enzymatic treatments to customize the MW.\nF. The evidence does not state that recently, there has been an increasing interest in producing HA at particular MWs for various cosmetic and biomedical applications.\nG. Hyaluronic acid (HA) is not a glycosaminoglycan with a wide range of biological functions that depend on its molecular weight (MW).\nH. Recently, there has been an increasing interest in producing HA at particular MWs for various cosmetic and biomedical applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12934015", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12934015/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e310631f6b160f384bdc", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis study integrates the valorization of a lignocellulose material into poly­(3-hydroxybutyrate), P­(3HB), with biopolymer extraction from bacterial cells with the enzyme alcalase. The work focused on Burkholderia thailandensis DSM 13276 as the P­(3HB) producer and on eucalyptus bark, a byproduct from the pulp industry, as the sole feedstock for bacterial cultivation. The eucalyptus bark was hydrolyzed by a cellulolytic enzymatic cocktail following steam explosion and further subjected to ultrafiltration for enzyme recovery. The resulting hydrolysate supported good cell growth, achieving a cell dry weight of 7.67 ± 0.16 g/L within 72 h of cultivation, and high P­(3HB) content (60.0 ± 2.19 wt %) in the bacterial cells, clearly favoring biopolymer synthesis over cell growth, as demonstrated by the polymer and growth yields (0.190 g P(3HB) /g sugar and 0.026 g X /g sugar , respectively). High extraction efficiency (96%) and biopolymer purity (100 ± 3.38%) were reached by enzymatic treatment, resulting in a sample with properties aligned with those of commercial P­(3HB) in terms of molecular mass distribution, crystallinity, and thermal properties. These findings demonstrate the successful use of a sustainable feedstock together with the application of environmentally friendly technologies based on the use of enzymes for both lignocellulosic saccharification and biopolymer recovery to develop high-quality bioplastics, advancing the goals of a circular bioeconomy.\n\nCandidates:\nA. The eucalyptus bark was not hydrolyzed by a cellulolytic enzymatic cocktail following steam explosion and further subjected to ultrafiltration for enzyme recovery.\nB. The resulting hydrolysate supported good cell growth, achieving a cell dry weight of 8.67 ± 0.16 g/L within 72 h of cultivation, and high P­(3HB) content (60.0 ± 2.19 wt %) in the bacterial cells, clearly favoring biopolymer synthesis over cell growth, as demonstrated by the polymer and growth yields (0.190 g P(3HB) /g sugar and 0.026 g X /g sugar , respectively).\nC. The work focused on Burkholderia thailandensis DSM 13277 as the P­(3HB) producer and on eucalyptus bark, a byproduct from the pulp industry, as the sole feedstock for bacterial cultivation.\nD. This study integrates the valorization of a lignocellulose material into poly­(4-hydroxybutyrate), P­(3HB), with biopolymer extraction from bacterial cells with the enzyme alcalase.\nE. This study integrates the valorization of a lignocellulose material into poly­(3-hydroxybutyrate), P­(3HB), with biopolymer extraction from bacterial cells with the enzyme alcalase.\nF. The work focused on Burkholderia thailandensis DSM 13276 as the P­(3HB) producer and on eucalyptus bark, a byproduct from the pulp industry, as the sole feedstock for bacterial cultivation.\nG. The eucalyptus bark was hydrolyzed by a cellulolytic enzymatic cocktail following steam explosion and further subjected to ultrafiltration for enzyme recovery.\nH. The resulting hydrolysate supported good cell growth, achieving a cell dry weight of 7.67 ± 0.16 g/L within 72 h of cultivation, and high P­(3HB) content (60.0 ± 2.19 wt %) in the bacterial cells, clearly favoring biopolymer synthesis over cell growth, as demonstrated by the polymer and growth yields (0.190 g P(3HB) /g sugar and 0.026 g X /g sugar , respectively).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12934527", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12934527/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1b4163544988705d54ec", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSince its discovery as a pivotal enzyme in innate immunity, cyclic GMP‐AMP synthase (cGAS) has been extensively studied for its immunological significance and catalytic mechanism. However, its potential as a biocatalyst for the efficient synthesis of the second messenger 2′3′‐cyclic GMP‐AMP (2′3′‐cGAMP) remains underexplored. This review provides a comprehensive biotechnological perspective on cGAS, highlighting its enzymatic and structural features, substrate promiscuity, homologs, and engineered variants. We examined the expression systems reported in previous studies and assessed their suitability for scalable cGAS production. Furthermore, we explored reaction engineering strategies for 2′3′‐cGAMP synthesis by comparing published production and purification methods. This review aims to bridge the gap between fundamental enzymology and applied bioprocessing by positioning cGAS as a promising biocatalyst for the pharmaceutical industry, with potential applications in immunotherapy, vaccine adjuvants, and beyond. Keywords: biocatalysis, bioprocess, cGAMP, cGAS, cyclic dinucleotides\n\nCandidates:\nA. We examined the expression systems reported in previous studies and assessed their suitability for scalable cGAS production.\nB. The evidence does not state that since its discovery as a pivotal enzyme in innate immunity, cyclic GMP‐AMP synthase (cGAS) has been extensively studied for its immunological significance and catalytic mechanism.\nC. The evidence does not state that we examined the expression systems reported in previous studies and assessed their suitability for scalable cGAS production.\nD. The evidence does not state that this review provides a comprehensive biotechnological perspective on cGAS, highlighting its enzymatic and structural features, substrate promiscuity, homologs, and engineered variants.\nE. This review provides a comprehensive biotechnological perspective on cGAS, highlighting its enzymatic and structural features, substrate promiscuity, homologs, and engineered variants.\nF. However, its potential as a biocatalyst for the efficient synthesis of the second messenger 2′3′‐cyclic GMP‐AMP (2′3′‐cGAMP) remains underexplored.\nG. Since its discovery as a pivotal enzyme in innate immunity, cyclic GMP‐AMP synthase (cGAS) has been extensively studied for its immunological significance and catalytic mechanism.\nH. However, its potential as a biocatalyst for the efficient synthesis of the second messenger 3′3′‐cyclic GMP‐AMP (2′3′‐cGAMP) remains underexplored.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12934549", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12934549/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2236f42596888a996db0", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"24 h\", \"25 h\", \"48 h\", \"73 h\", \"49 h\", \"72 h\"]\n\nEvidence:\nChinese hamster ovary (CHO) cells are widely utilised in the biopharmaceutical industry to produce therapeutic proteins. Understanding the mechanisms of endoplasmic reticulum (ER) stress and its interplay with protein degradation pathways remains pivotal for improving production efficiency and product quality. In this study, we investigated the proteomic responses of CHO-K1 (non-producer), CHO DP-12 (IgG-producer), and NISTCHO (IgG-producer) cell lines under ER stress induced by a combination of the proteasome inhibitor MG132 and the glycosylation inhibitor tunicamycin. Viability, cell growth, and IgG titre were measured after 24 h, 48 h, and 72 h of treatment and the 48 h timepoint was used for the comparative analysis of the proteomic data across the three cell lines. Proteasome inhibition with MG132 intensified ER stress and altered ER-associated protein degradation (ERAD). Combined tunicamycin + MG132 treatment was associated with cell line-specific proteomic changes: NISTCHO upregulated ER translocation and glycoprotein quality control proteins (SSR4, SEC24C, UGGT1), CHO DP-12 activated redox/disulfide regulators (DNAJC10, CAPN1), while CHO-K1 showed broad proteome shifts, suggesting differences in baseline stress handling. These findings provide mechanistic insights into ER stress and protein quality control in CHO cells, offering a foundation for strategies to enhance cell line robustness and optimise biopharmaceutical production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12938224", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12938224/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0379ff39c4744ab2088d", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMicrofluidics-based preparation methods for cell-laden hydrogel microspheres are well-suited for large-scale comparative analysis of single or few cells. However, in existing studies, the preparation of cell-laden hydrogel microspheres and the cell culture process are typically separated, requiring the fabricated microspheres to be eluted and transferred from the preparation device to cell culture dishes or plates for cultivation. This transfer process can easily compromise sterility, while conventional cell culture methods consume more reagents and cause microsphere stacking, hindering single-cell observation and analysis. To address these issues, this paper presents an integrated microfluidic chip that sequentially enables droplet generation with cell encapsulation, gel droplet solidification, hydrogel microsphere trapping, and microsphere-based cell culture and analysis, facilitating the cultivation and observation of single or small numbers of cells. Integrating cell-laden microsphere preparation and 3D cell culture within a sealed chip structure reduces contamination risks associated with cell transfer, enables automation of multiple cell analysis workflows, and minimizes reagent and sample consumption. Using polydimethylsiloxane (PDMS) with good gas permeability and processability as the chip material, biocompatible fluorinated oil was selected as the oil phase for microsphere preparation. A mild sodium alginate-calcium ion gelation system was employed, where calcium ions were released under acidic conditions after droplet generation to trigger solidification, yielding uniform hydrogel microspheres. Under optimized conditions, the single-cell encapsulation efficiency for test samples of human myeloid leukemia cells (K562) was 33.8% ± 1.8%, with a size uniformity coefficient of variation (CV) reaching 3.85%. Cells encapsulated within hydrogel microspheres were cultured in 286 on-chip independent cell culture chambers, achieving >95% viability after 24 h.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12938325", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12938325/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3df5eb168ec9f2c4a8b0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nFermentation, one of the oldest biotransformation processes, has become a key element of contemporary sustainable biotechnology. In modern food systems, it enables the simultaneous resolution of environmental, nutritional, and economic challenges by converting agricultural and food residues into high-value-added products, such as bioactive compounds, organic acids, biofuels, enzymes, and proteins. Consistent with the concept of a circular bioeconomy, fermentation supports resource recycling, waste minimization, and greenhouse gas reduction, contributing to the achievement of selected United Nations Sustainable Development Goals (SDGs). The importance of fermentation extends beyond its environmental aspects—fermented foods and postbiotics support the modulation of the gut microbiome, strengthen immunity, and can act as a preventative measure against metabolic and inflammatory conditions. Simultaneously, the dynamic development of precision fermentation and synthetic biology enables the design of microorganisms that produce specific food ingredients without the use of animals or traditional agriculture, paving the way for more responsible production and consumption. This review presents the categories of organic residues valorized through fermentation, explains their role in circular food and healthcare systems, and identifies key technological and regulatory barriers limiting the scaling of this approach. Collectively, fermentation emerges as a biotechnology platform with significant transformative potential for future sustainable food systems.\n\nCandidates:\nA. Fermentation, one of the oldest biotransformation processes, has become a key element of contemporary sustainable biotechnology.\nB. In modern food systems, it enables the simultaneous resolution of environmental, nutritional, and economic challenges by converting agricultural and food residues into high-value-added products, such as bioactive compounds, organic acids, biofuels, enzymes, and proteins.\nC. The importance of fermentation extends beyond its environmental aspects—fermented foods and postbiotics support the modulation of the gut microbiome, strengthen immunity, and can act as a preventative measure against metabolic and inflammatory conditions.\nD. The evidence does not state that consistent with the concept of a circular bioeconomy, fermentation supports resource recycling, waste minimization, and greenhouse gas reduction, contributing to the achievement of selected United Nations Sustainable Development Goals (SDGs).\nE. The importance of fermentation extends beyond its environmental aspects—fermented foods and postbiotics support the modulation of the gut microbiome, strengthen immunity, and cannot act as a preventative measure against metabolic and inflammatory conditions.\nF. The evidence does not state that in modern food systems, it enables the simultaneous resolution of environmental, nutritional, and economic challenges by converting agricultural and food residues into high-value-added products, such as bioactive compounds, organic acids, biofuels, enzymes, and proteins.\nG. The evidence does not state that fermentation, one of the oldest biotransformation processes, has become a key element of contemporary sustainable biotechnology.\nH. Consistent with the concept of a circular bioeconomy, fermentation supports resource recycling, waste minimization, and greenhouse gas reduction, contributing to the achievement of selected United Nations Sustainable Development Goals (SDGs).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12939561", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12939561/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-52e4a6ce2352566492cf", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe revalorization of food processing by-products represents a critical strategy for enhancing resource efficiency and advancing circularity within the food system. This review examines the potential of three major plant-based agro-industrial by-products—fruit and vegetable residues, brewer’s spent grain, and spent coffee grounds—as sources of high-value functional ingredients. These by-products contain bioactive compounds, including dietary fibers, polyphenols, proteins, peptides, oils, and antioxidants, that can be recovered using emerging green extraction and bioprocessing technologies. Conventional extraction methods are progressively being replaced or hybridized with enzyme-assisted, ultrasound-assisted, microwave-assisted, and deep eutectic solvent techniques to improve yield, reduce solvent consumption, and preserve bioactivity. The recovered compounds have demonstrated promising applications as gelling agents (pectin), natural colorants and antioxidants, protein-enriched flours, prebiotic fibers, and bioactive extracts for functional food and nutraceutical formulations. However, challenges persist in standardizing feedstock composition, scaling continuous extraction processes, ensuring safety and regulatory compliance, and generating robust techno-economic and life-cycle assessments to validate sustainability claims. This review synthesizes biochemical composition data, processing pathways, food applications, and regulatory considerations, and identifies research priorities for developing integrated, scalable biorefinery models that valorize food by-products into market-ready functional ingredients.\n\nCandidates:\nA. Conventional extraction methods are progressively being replaced or hybridized with enzyme-assisted, ultrasound-assisted, microwave-assisted, and deep eutectic solvent techniques to improve yield, reduce solvent consumption, and preserve bioactivity.\nB. The revalorization of food processing by-products represents a critical strategy for enhancing resource efficiency and advancing circularity within the food system.\nC. The evidence does not state that the revalorization of food processing by-products represents a critical strategy for enhancing resource efficiency and advancing circularity within the food system.\nD. The evidence does not state that this review examines the potential of three major plant-based agro-industrial by-products—fruit and vegetable residues, brewer’s spent grain, and spent coffee grounds—as sources of high-value functional ingredients.\nE. These by-products contain bioactive compounds, including dietary fibers, polyphenols, proteins, peptides, oils, and antioxidants, that can be recovered using emerging green extraction and bioprocessing technologies.\nF. This review examines the potential of three major plant-based agro-industrial by-products—fruit and vegetable residues, brewer’s spent grain, and spent coffee grounds—as sources of high-value functional ingredients.\nG. Conventional extraction methods are not progressively being replaced or hybridized with enzyme-assisted, ultrasound-assisted, microwave-assisted, and deep eutectic solvent techniques to improve yield, reduce solvent consumption, and preserve bioactivity.\nH. These by-products contain bioactive compounds, including dietary fibers, polyphenols, proteins, peptides, oils, and antioxidants, that cannot be recovered using emerging green extraction and bioprocessing technologies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12943265", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12943265/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c28003122a536ffe53fc", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe prevalence of diabetes and its worldwide co-morbidities is escalating. Therefore, the number of users of therapeutic peptides including insulin analogs and glucagon-like peptide 1 receptor agonists (GLP-1RAs), will unavoidably increase in the coming years. However, access to these two antidiabetic classes remains limited in some countries due to their high cost. Even when available, their long-term therapeutic efficiency is often compromised by challenges in sustained treatment adherence, mainly resulting from their mode of administration through repeated subcutaneous injections. This repeated invasive delivery not only affects patient comfort but also complicates long-term disease management and monitoring. Therefore, there is an urgent need to improve the accessibility, affordability, and long-term patient adherence to insulin and GLP-1RAs. In this review, we highlight as promising alternatives the potential of plants and microalgae to serve as host organisms, as well as the use of their polysaccharides as drug carriers, for the production of low-cost and non-invasive antidiabetic drugs.\n\nCandidates:\nA. The evidence does not state that however, access to these two antidiabetic classes remains limited in some countries due to their high cost.\nB. However, access to these two antidiabetic classes remains limited in some countries due to their high cost.\nC. Even when available, their long-term therapeutic efficiency is not often compromised by challenges in sustained treatment adherence, mainly resulting from their mode of administration through repeated subcutaneous injections.\nD. The prevalence of diabetes and its worldwide co-morbidities is escalating.\nE. The prevalence of diabetes and its worldwide co-morbidities is not escalating.\nF. Even when available, their long-term therapeutic efficiency is often compromised by challenges in sustained treatment adherence, mainly resulting from their mode of administration through repeated subcutaneous injections.\nG. Therefore, the number of users of therapeutic peptides including insulin analogs and glucagon-like peptide 2 receptor agonists (GLP-1RAs), will unavoidably increase in the coming years.\nH. Therefore, the number of users of therapeutic peptides including insulin analogs and glucagon-like peptide 1 receptor agonists (GLP-1RAs), will unavoidably increase in the coming years.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12944287", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12944287/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-44ff1030f8faa5c9afe8", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nElastane is ubiquitous in polyester-based textiles and complicates depolymerization-based recycling because it can undergo thermal degradation and chemical bond cleavage, consuming reagents and forming low-molecular by-products that may compromise monomer quality. Here, we investigate alkaline PET depolymerization of PET/elastane blends under an intentional base-competition scenario in a laboratory kneader. Pure PET (100/0) and PET/EL blends (95/5 and 85/15, wt/wt) were processed under quasi-solid-state conditions at 140 °C for 5 min using solid NaOH dosed at 2.1 mol per mol PET repeat unit and pelletized feedstocks to ensure scale-relevant mixing and reproducible chamber filling. Torque and bulk-temperature profiles were similar across compositions, and isolated terephthalic acid yields remained in a narrow corridor (68–71%), indicating that PET depolymerization is not measurably impaired by 5–15 wt% elastane within this reaction window. Differential scanning calorimetry of water-insoluble residues revealed pronounced changes in elastane-related thermal transitions, evidencing elastane modification during treatment. Targeted 1 H NMR screening of recovered TA against a 4,4′-methylenedianiline spiked reference showed no detectable co-isolated aromatic diamines. Overall, the study demonstrates robust monomer recovery from mixed PET/EL textiles under solid-NaOH, short-residence, solvent-lean processing, while identifying residue analytics as the key bottleneck for quantifying elastane fate and closing component balances.\n\nCandidates:\nA. Here, we investigate alkaline PET depolymerization of PET/elastane blends under an intentional base-competition scenario in a laboratory kneader.\nB. Elastane is ubiquitous in polyester-based textiles and complicates depolymerization-based recycling because it can undergo thermal degradation and chemical bond cleavage, consuming reagents and forming low-molecular by-products that may compromise monomer quality.\nC. Pure PET (100/0) and PET/EL blends (95/5 and 85/15, wt/wt) were processed under quasi-solid-state conditions at 140 °C for 5 min using solid NaOH dosed at 2.1 mol per mol PET repeat unit and pelletized feedstocks to ensure scale-relevant mixing and reproducible chamber filling.\nD. Torque and bulk-temperature profiles were similar across compositions, and isolated terephthalic acid yields remained in a narrow corridor (68–71%), indicating that PET depolymerization is not measurably impaired by 5–15 wt% elastane within this reaction window.\nE. Pure PET (101/0) and PET/EL blends (95/5 and 85/15, wt/wt) were processed under quasi-solid-state conditions at 140 °C for 5 min using solid NaOH dosed at 2.1 mol per mol PET repeat unit and pelletized feedstocks to ensure scale-relevant mixing and reproducible chamber filling.\nF. Elastane is not ubiquitous in polyester-based textiles and complicates depolymerization-based recycling because it can undergo thermal degradation and chemical bond cleavage, consuming reagents and forming low-molecular by-products that may compromise monomer quality.\nG. The evidence does not state that here, we investigate alkaline PET depolymerization of PET/elastane blends under an intentional base-competition scenario in a laboratory kneader.\nH. Torque and bulk-temperature profiles were similar across compositions, and isolated terephthalic acid yields remained in a narrow corridor (69–71%), indicating that PET depolymerization is not measurably impaired by 5–15 wt% elastane within this reaction window.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12944338", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12944338/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-744b8e5ebd00604d3a7f", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis research has developed a promising edible vaccine to combat Salmonella infections in poultry, a major source of foodborne illness worldwide. Using advanced computational and immunoinformatic tools, the team designed two multi-epitope vaccine constructs targeting conserved proteins involved in bacterial adhesion and biofilm formation. The vaccines, engineered for expression in the microalga Chlorella vulgaris, incorporate immune-activating adjuvants—β-defensin-3 and lipopolysaccharide (LPS)—to stimulate robust immune responses. Structural modeling and molecular docking revealed that the LPS-based construct (Construct 2) binds strongly to Toll-like receptor 3, suggesting potent innate immune activation. Simulated immune responses showed effective IgM-to-IgG class switching and long-lasting antibody production, indicating strong protection potential. Codon optimization confirmed high expression feasibility in algae, paving the way for scalable, low-cost oral vaccine production. This innovation aligns with One Health principles, aiming to reduce antibiotic use in agriculture, enhance food safety, and mitigate antimicrobial resistance. Experimental trials are underway to validate the vaccine’s efficacy in live poultry.\n\nCandidates:\nA. Structural modeling and molecular docking revealed that the LPS-based construct (Construct 2) binds strongly to Toll-like receptor 3, suggesting potent innate immune activation.\nB. Structural modeling and molecular docking revealed that the LPS-based construct (Construct 3) binds strongly to Toll-like receptor 3, suggesting potent innate immune activation.\nC. The evidence does not state that this research has developed a promising edible vaccine to combat Salmonella infections in poultry, a major source of foodborne illness worldwide.\nD. The vaccines, engineered for expression in the microalga Chlorella vulgaris, incorporate immune-activating adjuvants—β-defensin-3 and lipopolysaccharide (LPS)—to stimulate robust immune responses.\nE. The evidence does not state that using advanced computational and immunoinformatic tools, the team designed two multi-epitope vaccine constructs targeting conserved proteins involved in bacterial adhesion and biofilm formation.\nF. This research has developed a promising edible vaccine to combat Salmonella infections in poultry, a major source of foodborne illness worldwide.\nG. The vaccines, engineered for expression in the microalga Chlorella vulgaris, incorporate immune-activating adjuvants—β-defensin-4 and lipopolysaccharide (LPS)—to stimulate robust immune responses.\nH. Using advanced computational and immunoinformatic tools, the team designed two multi-epitope vaccine constructs targeting conserved proteins involved in bacterial adhesion and biofilm formation.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12945135", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12945135/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-85c3d05b6a742587c725", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicrobial CO 2 capture coupled with biosurfactant production represents a promising strategy for greenhouse gas mitigation and sustainable biomanufacturing. This review examines the metabolic and engineering aspects of microbial carbon capture, focusing on both anaerobic and CO 2 -enriched systems within the Microbial-CCUS framework. The structural diversity, physicochemical properties, and industrial applications of microbial biosurfactants are discussed, along with emerging evidence of anaerobic biosurfactant synthesis linked to CO 2 metabolism. Advances in genetic and synthetic biology, pathway modularization, and systems-level modeling are reshaping the potential to coordinate CO 2 fixation with biosurfactant biosynthesis. Integrating artificial intelligence with metabolic engineering may further optimize productivity, scalability, and energy efficiency. Despite technical and economic challenges, the convergence of CO 2 utilization, biotechnology, and digital innovation offers a transformative route toward circular carbon systems and climate mitigation. • Microbial CO 2 capture drives biosurfactant synthesis within Microbial-CCUS systems . • Anaerobic and CO 2 -enriched cultures unlock new routes for sustainable biomanufacturing. • Synthetic biology links carbon-fixation modules to biosurfactant pathways . Keywords: Biosurfactants, Microbial CO 2 capture, Anaerobic metabolism, Microbial, CCUS, Circular economy, Bioprocess engineering\n\nCandidates:\nA. Advances in genetic and synthetic biology, pathway modularization, and systems-level modeling are reshaping the potential to coordinate CO 3 fixation with biosurfactant biosynthesis.\nB. Microbial CO 2 capture coupled with biosurfactant production represents a promising strategy for greenhouse gas mitigation and sustainable biomanufacturing.\nC. The structural diversity, physicochemical properties, and industrial applications of microbial biosurfactants are discussed, along with emerging evidence of anaerobic biosurfactant synthesis linked to CO 3 metabolism.\nD. Advances in genetic and synthetic biology, pathway modularization, and systems-level modeling are reshaping the potential to coordinate CO 2 fixation with biosurfactant biosynthesis.\nE. This review examines the metabolic and engineering aspects of microbial carbon capture, focusing on both anaerobic and CO 2 -enriched systems within the Microbial-CCUS framework.\nF. This review examines the metabolic and engineering aspects of microbial carbon capture, focusing on both anaerobic and CO 3 -enriched systems within the Microbial-CCUS framework.\nG. The structural diversity, physicochemical properties, and industrial applications of microbial biosurfactants are discussed, along with emerging evidence of anaerobic biosurfactant synthesis linked to CO 2 metabolism.\nH. Microbial CO 3 capture coupled with biosurfactant production represents a promising strategy for greenhouse gas mitigation and sustainable biomanufacturing.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12946364", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12946364/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f3fd623edc33c5744317", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAugmentation therapy is a treatment option available in the market that has been approved by the U.S. Food and Drug Administration (FDA) for alpha-1-antitrypsin (A1AT) deficient patients. The treatment requires weekly injections of purified A1AT for the patients and relies on plasma donor. The demand for A1AT is also high due to its functional role in various diseases. However, scaling up production of purified human plasma A1AT remained costly and challenging. It is therefore of great interest to generate A1AT at larger scale in ensuring a consistent supply to the market. In this paper, we evaluated the stability and productivity of ten Chinese Hamster Ovary (CHO) single cell clones over 12 weeks. This was followed by scaling up the fed-batch production of A1AT with the selected cell clone in a 10L single-use surface aerated orbital shaken bioreactor SB10-X. The cell specific productivity of the two bioreactor runs were at 9.6 and 12 pg/cell/day (pcd) respectively, which were comparable to shake flasks. While the paper focuses on the possibility to scale up A1AT production, process conditions such as feeding regime could be investigated to further prolong the culture longevity and increase productivity. The online version contains supplementary material available at 10.1038/s41598-026-37353-w. Keywords: Surface aerated bioreactor, Biologics, Scale up, Bioprocess development, Recombinant protein, Shear sensitive Subject terms: Biotechnology, Biologics\n\nCandidates:\nA. The demand for A2AT is also high due to its functional role in various diseases.\nB. The demand for A1AT is also high due to its functional role in various diseases.\nC. Augmentation therapy is a treatment option available in the market that has been approved by the U.S.\nD. The treatment requires weekly injections of purified A1AT for the patients and relies on plasma donor.\nE. Food and Drug Administration (FDA) for alpha-1-antitrypsin (A1AT) deficient patients.\nF. Food and Drug Administration (FDA) for alpha-2-antitrypsin (A1AT) deficient patients.\nG. Augmentation therapy is not a treatment option available in the market that has been approved by the U.S.\nH. The treatment requires weekly injections of purified A2AT for the patients and relies on plasma donor.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12949062", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12949062/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-37172f4f9cdf571d692a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\n(+)-Bicyclogermacrene and its derivatives, with promising antimicrobial, anticancer, and insecticidal properties, hold significant potential for applications in pharmaceuticals, agriculture, and industry. However, traditional extraction methods from plant essential oils are unsustainable. In this study, we achieved the de novo biosynthesis of (+)-bicyclogermacrene using a metabolically engineered Escherichia coli strain. The biosynthetic pathway of (+)-bicyclogermacrene was partitioned into upstream and downstream modules to enable precise regulation. This was accomplished through the genome-integrated overexpression of the endogenous methylerythritol phosphate pathway to ensure an adequate supply of terpenoid precursors, which pulled the titer from the initial 11.3 mg/L to 50.1 mg/L. Production was further enhanced to 96.9 mg/L by fusion of downstream key genes to facilitate precursor channeling, along with expression level optimization to improve pathway efficiency. Additionally, NADPH supply was fine-tuned through overexpressing dehydrogenases to improve the overall metabolic balance and this approach achieved a titer of 119 mg/L. Following site-directed of (+)-bicyclogermacrene synthase, the engineered E. coli strain M6-36 produced 565 mg/L of (+)-bicyclogermacrene in a 5-L bioreactor, an approximately 50-fold increase from the initial. To the best of our knowledge, the obtained titer in this study represents the highest level ever reported for the production of (+)-bicyclogermacrene. This study demonstrates an effective approach for the heterologous biosynthesis of sesquiterpenoids in E. coli and provides a scalable platform for the sustainable production of terpenoid-derived valuable chemicals. The online version contains supplementary material available at 10.1186/s40643-026-01017-4.\n\nCandidates:\nA. The evidence does not state that (+)-Bicyclogermacrene and its derivatives, with promising antimicrobial, anticancer, and insecticidal properties, hold significant potential for applications in pharmaceuticals, agriculture, and industry.\nB. However, traditional extraction methods from plant essential oils are not unsustainable.\nC. The evidence does not state that in this study, we achieved the de novo biosynthesis of (+)-bicyclogermacrene using a metabolically engineered Escherichia coli strain.\nD. However, traditional extraction methods from plant essential oils are unsustainable.\nE. The biosynthetic pathway of (+)-bicyclogermacrene was partitioned into upstream and downstream modules to enable precise regulation.\nF. In this study, we achieved the de novo biosynthesis of (+)-bicyclogermacrene using a metabolically engineered Escherichia coli strain.\nG. The biosynthetic pathway of (+)-bicyclogermacrene was not partitioned into upstream and downstream modules to enable precise regulation.\nH. (+)-Bicyclogermacrene and its derivatives, with promising antimicrobial, anticancer, and insecticidal properties, hold significant potential for applications in pharmaceuticals, agriculture, and industry.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12950146", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12950146/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f44908fdd4178a516a81", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"1.46 g/L\", \"2.46 g/L\"]\n\nEvidence:\nIn contrast to the extensively researched animal CYP11A1 system, the catalytic mechanism of sterol side-chain cleavage by plant-derived cytochrome P450scc enzymes remains poorly understood. Through the integration of computational structural biology and enzyme channel engineering, this study successfully elucidated the key intermediates in the stepwise hydroxylation-cleavage catalytic process of Digitalis purpurea -derived DlCYP87A enzyme. Building on this foundation, we implemented structure-guided rational design to precisely engineer the substrate channel and catalytic pocket, systematically delineating their structure-activity relationships, which ultimately overcame the critical catalytic bottleneck of low conversion efficiency in heterologous microbial systems expressing plant-derived P450scc. This study established an efficient steroid synthesis system in Saccharomyces cerevisiae through integrated systematic enzyme engineering and transcriptome-guided organelle optimization. In a 5-liter fermentation system, engineered strain P4 achieved a pregnenolone titer of 1.46 g/L. This achievement represents the first gram-scale breakthrough in de novo pregnenolone biosynthesis, laying a crucial technological foundation for scalable bio-manufacturing of steroid precursors and pioneering a new industrial production pathway. Keywords: Cytochrome P450 scc , CYP87A, Steroids, Synthetic biology, Enzyme engineering", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12955207", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12955207/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9065dbd638452c2a1da0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nFungi play a dual role as indispensable ecological engineers and as major agents of disease in humans, animals, and plants. Recent estimates highlight their substantial impact, with millions of invasive infections annually and severe agricultural losses threatening food security. At the same time, fungi underpin ecosystem services such as decomposition, soil aggregation, and carbon sequestration, while also serving as prolific sources of enzymes, metabolites, and sustainable biomaterials. Advances in single-cell and spatial omics, cryo-electron microscopy, AlphaFold-based structural predictions, and machine learning applied to biosynthetic gene clusters are transforming the study of fungal pathogenicity, symbiosis, and metabolism. These approaches are shifting fungal research from descriptive biology toward predictive, translational pipelines that connect mechanistic insights to drug discovery, resistance management, and biotechnological innovation. Nevertheless, challenges remain, including antifungal resistance, climate-driven emergence of new pathogens, limited therapeutic options, and bottlenecks in scaling fungal applications for sustainability. Addressing these requires integrated One Health strategies that bridge clinical, agricultural, and environmental perspectives. By uniting structural biology, omics, genome editing, and computational tools within a global framework, fungal biology can be harnessed not only to mitigate emerging risks but also to drive innovations in medicine, agriculture, and green technologies.\n\nCandidates:\nA. Fungi play a dual role as indispensable ecological engineers and as major agents of disease in humans, animals, and plants.\nB. Recent estimates highlight their substantial impact, with millions of invasive infections annually and severe agricultural losses threatening food security.\nC. The evidence does not state that at the same time, fungi underpin ecosystem services such as decomposition, soil aggregation, and carbon sequestration, while also serving as prolific sources of enzymes, metabolites, and sustainable biomaterials.\nD. At the same time, fungi underpin ecosystem services such as decomposition, soil aggregation, and carbon sequestration, while also serving as prolific sources of enzymes, metabolites, and sustainable biomaterials.\nE. The evidence does not state that recent estimates highlight their substantial impact, with millions of invasive infections annually and severe agricultural losses threatening food security.\nF. Advances in single-cell and spatial omics, cryo-electron microscopy, AlphaFold-based structural predictions, and machine learning applied to biosynthetic gene clusters are not transforming the study of fungal pathogenicity, symbiosis, and metabolism.\nG. Advances in single-cell and spatial omics, cryo-electron microscopy, AlphaFold-based structural predictions, and machine learning applied to biosynthetic gene clusters are transforming the study of fungal pathogenicity, symbiosis, and metabolism.\nH. The evidence does not state that fungi play a dual role as indispensable ecological engineers and as major agents of disease in humans, animals, and plants.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12957183", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12957183/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-97c918a45b1936f19f44", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMesenchymal stem cells (MSCs) are highly valuable for their potential in cell therapy and tissue engineering because of their self-renewal, multilineage differentiation, and immunomodulatory capabilities. Adipose-derived mesenchymal stem cells (AD-MSCs) are advantageous in regenerative medicine because of their accessibility and ease of isolation. However, the clinical application of MSCs faces challenges related to large-scale culture (LSC) expansion, which is required to generate enough cells for transplantation but also decreases their therapeutic properties. This review assesses the impact of LSC on MSC functionality, differentiation potential, and immunomodulatory properties, and identifies key factors, such as metabolic shifts, genetic instability, and altered secretory profiles, that can compromise their therapeutic potential. We explored how prolonged in vitro passaging decreases MSC functionality and increases the risk of genetic alterations. In addition, strategies to preserve the efficacy of MSCs during scaling are discussed. A comprehensive literature review was conducted using PubMed, focusing on in vitro and in vivo studies that evaluated the effects of LSC on MSCs. These findings provide insights into optimizing culture protocols to maintain the clinical efficacy of AD-MSCs in regenerative therapies, addressing the critical need to balance large-scale expansion and functional integrity. Keywords: Adipose-derived stem cells, Large-scale culture expansion, Mesenchymal stem cells, Regenerative medicine, Therapeutic potential, Cell functionality", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12960335", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12960335/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-60f695383c777ea038e7", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDemand for recombinant proteins is rapidly growing, driven by their use as biotherapeutics, vaccine components, industrial enzymes, and food ingredients. The growing market requires novel strategies for increasing protein production in cellular hosts. Systems-level frameworks have been used to improve production, but have had difficulty relating complex cellular pathways with protein expression. Here, we demonstrate a method for mapping relationships between gene expression signatures and carbon source-related phenotypes related to recombinant protein production. Our approach induces systematic perturbations in cultures of K. phaffii using varied co-feeds of carbon sources. The different carbon sources significantly impacted cell growth, specific productivity, and transcriptional states. With these data, we identified metagenes for both immunoglobulin G1 monoclonal antibody (IgG1) and Variable domain on a heavy chain (VHH) antibody that explained significant transcriptomic variance. These metagenes strongly associated with two phenotypes: production of recombinant protein-to-biomass ratio, and response to methanol induction. We used these results to identify and knockout 31 novel gene targets for which expression inversely correlated with productivity. Nine of these genes improved productivity of IgG1 by up to 3x and ten genes increased productivity of VHH by up to 1.7x. Many of these genes are involved in the modulation and progression of the cell cycle but interestingly, disruption had little to no impact on cell growth. This study establishes a framework for relating gene signatures to complex cellular phenotypes, providing a robust methodology for assessing production processes and identifying new targets for cellular engineering. While the identified specific metagenes depend on the complexity and structure of the recombinant protein produced, this framework is extensible across diverse proteins and potentially other host organisms. These signatures may serve as scale-independent, cellular-level metrics for traits like efficiency of production of recombinant proteins, facilitating the translation of findings across different scales and cultivation modes. Furthermore, this framework enables the identification of novel targets for genomic modifications that can improve strain performance, offering a predictive tool for the rational design of high-performing microbial cell factories. The online version contains supplementary material available at 10.1186/s12934-026-02948-5. Keywords: Pichia pastoris, Systems biology, Transcriptomics, Monoclonal antibody\n\nCandidates:\nA. The evidence does not state that here, we demonstrate a method for mapping relationships between gene expression signatures and carbon source-related phenotypes related to recombinant protein production.\nB. The evidence does not state that systems-level frameworks have been used to improve production, but have had difficulty relating complex cellular pathways with protein expression.\nC. The evidence does not state that the growing market requires novel strategies for increasing protein production in cellular hosts.\nD. Here, we demonstrate a method for mapping relationships between gene expression signatures and carbon source-related phenotypes related to recombinant protein production.\nE. Demand for recombinant proteins is rapidly growing, driven by their use as biotherapeutics, vaccine components, industrial enzymes, and food ingredients.\nF. Demand for recombinant proteins is not rapidly growing, driven by their use as biotherapeutics, vaccine components, industrial enzymes, and food ingredients.\nG. The growing market requires novel strategies for increasing protein production in cellular hosts.\nH. Systems-level frameworks have been used to improve production, but have had difficulty relating complex cellular pathways with protein expression.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12964731", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12964731/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d57c8596f6dca50b1db4", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe bioconversion of fish by-products has been evidenced as a sustainable process to convert food waste into high-value products. In the present study, protein hydrolysates were produced from fish by-products by different bioprocesses and evaluated as fertilizers in wheat ( Triticum aestivum L. ) on a nitrogen-equivalent basis. Fish by-products were processed through grinding prior to bioconversion. Enzymatic hydrolysis was performed using Alcalase at 55 °C, pH 6.5, and a 3-h reaction, while microbial conversion was assessed using a lactic culture at 40 °C, pH 6.5, and a 10-day culture. Hydrolysates obtained by enzymatic and microbial bioconversion were evaluated as fertilizers by adding 30 mg after 7 and 14 days to wheat seeds sown under controlled conditions. Protease and microbial hydrolysis generated high concentrations of α -amino groups, yielding 100 mM and 170 mM, respectively. The combined process exhibited a synergistic effect, yielding 226 mM of α -amino groups and 33% of protein recovery. Plant growth assays were conducted under controlled conditions using nitrogen-equivalent doses of each hydrolysate. Microbial and combined enzymatic-microbial hydrolysates generated average plant lengths of 52 cm and 54 cm compared to 44 cm in the control, while plant biomass reached 1.7 g and 2.3 g with microbial and combined enzymatic-microbial hydrolysates compared to 0.7 g in the control. Photosynthetic parameters remained within normal physiological ranges from 2.5 to 3.3 for performance index (PI) and from 0.78 to 0.80 for maximum quantum efficiency (Fv/Fm). The integration of enzymatic and microbial catalysis produced the most effective biostimulant activity, highlighting the value of combining enzymatic specificity with microbial metabolic versatility. These findings support fish-derived protein hydrolysates as efficient and eco-friendly fertilizers that are capable of improving plant growth while contributing to sustainable and integral utilization of natural resources.\n\nCandidates:\nA. In the present study, protein hydrolysates were produced from fish by-products by different bioprocesses and evaluated as fertilizers in wheat ( Triticum aestivum L.\nB. The bioconversion of fish by-products has been evidenced as a sustainable process to convert food waste into high-value products.\nC. Enzymatic hydrolysis was performed using Alcalase at 55 °C, pH 6.5, and a 3-h reaction, while microbial conversion was assessed using a lactic culture at 40 °C, pH 6.5, and a 10-day culture.\nD. In the present study, protein hydrolysates were not produced from fish by-products by different bioprocesses and evaluated as fertilizers in wheat ( Triticum aestivum L.\nE. Enzymatic hydrolysis was performed using Alcalase at 56 °C, pH 6.5, and a 3-h reaction, while microbial conversion was assessed using a lactic culture at 40 °C, pH 6.5, and a 10-day culture.\nF. The evidence does not state that the bioconversion of fish by-products has been evidenced as a sustainable process to convert food waste into high-value products.\nG. Fish by-products were processed through grinding prior to bioconversion.\nH. Fish by-products were not processed through grinding prior to bioconversion.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12968009", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12968009/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-85d2d4c70dd3710c26af", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nKeratinous waste, a major by-product of agriculture and animal husbandry, is produced in massive quantities and is notoriously recalcitrant to degradation. With the expansion of the poultry and livestock industries, keratinous waste accumulation (e.g., feathers, hooves, and horns) has become a pressing environmental concern. Keratin’s highly cross-linked disulfide bond structure is resistant to breakdown by common proteases. Keratinase, a specialized protease capable of specifically degrading keratin, has emerges as a pivotal tool for the valorization of keratinous waste, demonstrating significant potential in waste management and resource recovery. This review systematically summarizes the enzymatic properties, mechanisms of action, and microbial sources of keratinases. It elaborates on innovative keratinase applications in waste valorization (including biogas production, the generation of bioactive peptides and amino acid feedstocks, and bioplastic manufacturing) and green industries (including leather and textile processing), as well as in the pharmaceutical, cosmetic, and detergent sectors. This review provides an in-depth discussion of the major challenges hindering industrial-scale keratinase application, including low heterologous expression efficiency and insufficient stability under industrial conditions. Finally, it outlines future research directions, encompassing protein engineering, artificial intelligence (AI)-assisted design, and multi-enzyme synergistic catalysis systems, aiming to offer forward-looking theoretical insights for advanced keratinase development and industrial application.\n\nCandidates:\nA. Keratinase, a specialized protease capable of specifically degrading keratin, has emerges as a pivotal tool for the valorization of keratinous waste, demonstrating significant potential in waste management and resource recovery.\nB. The evidence does not state that keratinase, a specialized protease capable of specifically degrading keratin, has emerges as a pivotal tool for the valorization of keratinous waste, demonstrating significant potential in waste management and resource recovery.\nC. Keratin’s highly cross-linked disulfide bond structure is not resistant to breakdown by common proteases.\nD. Keratinous waste, a major by-product of agriculture and animal husbandry, is produced in massive quantities and is notoriously recalcitrant to degradation.\nE. The evidence does not state that with the expansion of the poultry and livestock industries, keratinous waste accumulation (e.g., feathers, hooves, and horns) has become a pressing environmental concern.\nF. Keratin’s highly cross-linked disulfide bond structure is resistant to breakdown by common proteases.\nG. With the expansion of the poultry and livestock industries, keratinous waste accumulation (e.g., feathers, hooves, and horns) has become a pressing environmental concern.\nH. Keratinous waste, a major by-product of agriculture and animal husbandry, is not produced in massive quantities and is notoriously recalcitrant to degradation.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12968019", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12968019/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d8bf0c7c32eb025b0a1b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPlastics drive twin crises: persistent pollution and greenhouse gas emissions. Bio-based approaches using enzymes and microorganisms to depolymerise plastics and valorise monomers show promise but raise societal, ethical and regulatory questions central to Responsible Research and Innovation (RRI). In this Perspective, we reflect on RRI implications of bio-based plastic degradation, informed by stakeholder discussions across the plastics value chain and public engagement. We identify broad support alongside concerns about scalability, interaction with existing recycling, governance and containment of genetically modified organisms, management of additives and contaminants, and the roles of regulation and economic incentives in enabling adoption. Subject terms: Bioremediation, Environmental biotechnology\n\nCandidates:\nA. The evidence does not state that in this Perspective, we reflect on RRI implications of bio-based plastic degradation, informed by stakeholder discussions across the plastics value chain and public engagement.\nB. The evidence does not state that bio-based approaches using enzymes and microorganisms to depolymerise plastics and valorise monomers show promise but raise societal, ethical and regulatory questions central to Responsible Research and Innovation (RRI).\nC. We identify broad support alongside concerns about scalability, interaction with existing recycling, governance and containment of genetically modified organisms, management of additives and contaminants, and the roles of regulation and economic incentives in enabling adoption.\nD. In this Perspective, we reflect on RRI implications of bio-based plastic degradation, informed by stakeholder discussions across the plastics value chain and public engagement.\nE. Plastics drive twin crises: persistent pollution and greenhouse gas emissions.\nF. The evidence does not state that we identify broad support alongside concerns about scalability, interaction with existing recycling, governance and containment of genetically modified organisms, management of additives and contaminants, and the roles of regulation and economic incentives in enabling adoption.\nG. Bio-based approaches using enzymes and microorganisms to depolymerise plastics and valorise monomers show promise but raise societal, ethical and regulatory questions central to Responsible Research and Innovation (RRI).\nH. The evidence does not state that plastics drive twin crises: persistent pollution and greenhouse gas emissions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12972147", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12972147/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ecf1726b7bb9b9452436", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nListeria monocytogenes is a foodborne pathogen of global concern, particularly for immunocompromised individuals at risk of severe disease. In mice, infection outcomes are strongly influenced by host immunity and gut microbiome composition. The Oligo-MM 12 defined microbiota mouse model, containing a simplified community of 12 bacterial strains, offers a controlled system to study L. monocytogenes pathogenesis and microbiome interactions. Defined or reduced-complexity microbiota models are increasingly used to investigate colonisation resistance and identify protective taxa. In this study, we compared Oligo-MM 12 mice with conventionally raised Specific Pathogen Free (SPF) mice to assess how microbiome complexity shapes infection. This allowed us to explore how microbiome complexity affects resistance to L. monocytogenes . We performed an in vivo infection study to assess host responses and pathogen-related outcomes, alongside an ex vivo fermentation assay that simulated the murine distal colon, to monitor microbial dynamics. Building on our earlier work, we now demonstrate that in vivo, Oligo-MM 12 mice showed significantly higher L. monocytogenes shedding in faeces during infection, whereas SPF mice progressively reduced L. monocytogenes levels. Despite this, L. monocytogenes dissemination to internal organs after three days of infection was similar in both models. Alterations to gut Prevotella , Akkermansia and Blautia species following L. monocytogenes infection were noteworthy. Ex vivo fermentation mirrored in vivo patterns, validating the Oligo-MM 12 system for mechanistic studies. Together, these results highlight the importance of microbiome complexity in modulating infection outcomes and establish a foundation for identifying protective taxa and mechanisms of colonization resistance. The online version contains supplementary material available at 10.1038/s41598-026-37294-4. Keywords: Listeria monocytogenes , SPF, Oligo-MM 12 , micro-Matrix bioreactor, Gut Microbiome Subject terms: Immunology, Microbiology\n\nCandidates:\nA. The Oligo-MM 12 defined microbiota mouse model, containing a simplified community of 12 bacterial strains, offers a controlled system to study L.\nB. Listeria monocytogenes is not a foodborne pathogen of global concern, particularly for immunocompromised individuals at risk of severe disease.\nC. Listeria monocytogenes is a foodborne pathogen of global concern, particularly for immunocompromised individuals at risk of severe disease.\nD. In mice, infection outcomes are not strongly influenced by host immunity and gut microbiome composition.\nE. In mice, infection outcomes are strongly influenced by host immunity and gut microbiome composition.\nF. monocytogenes pathogenesis and microbiome interactions.\nG. The Oligo-MM 13 defined microbiota mouse model, containing a simplified community of 12 bacterial strains, offers a controlled system to study L.\nH. The evidence does not state that monocytogenes pathogenesis and microbiome interactions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12972299", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12972299/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3e6ac2b1cd0df94c1ced", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAlgae-derived bioactive peptides are gaining recognition as functional ingredients offering health benefits and sustainability advantages over conventional proteins. This review aimed to evaluate the current evidence on algal peptides, focusing on their nutritional content, mechanistic actions, health effects, potential for sustainability, and translational challenges. A comprehensive literature search was conducted across PubMed, Scopus, ScienceDirect, Springer, Elsevier, and Google Scholar. Peer-reviewed studies reporting bioactive peptides derived from microalgae, macroalgae, or Cyanophyceae were included. In vitro , animal, and human intervention studies evaluating molecular mechanisms, metabolic outcomes, or clinical relevance were considered. Available evidence shows that algal peptides exert multifunctional bioactivities, including inhibition of angiotensin-converting enzyme and renin, antioxidant and anti-inflammatory effects, modulation of glucose metabolism via α-amylase, α-glucosidase, and DPP-IV inhibition, and regulation of lipid metabolism and adipogenesis. Frequently studied sources included Limnospira , Chlorella , Auxenochlorella , Nannochloropsis , Undaria , Palmaria , Ulva , and Neopyropia . Limited human trials suggest modest but clinically relevant improvements in blood pressure, glycemic control, lipid profiles, and body-weight-related outcomes, primarily using whole algal biomass or extracts. Life-cycle assessments highlight favorable land-use efficiency and carbon sequestration potential, although economic feasibility is constrained by energy-intensive downstream processing. Algal-derived peptides demonstrate promising health-promoting effects and align with sustainable nutrition goals. However, their clinical translation is limited by variability in peptide characterization, uncertain bioavailability, and lack of robust human trials. Standardized production methods, improved delivery strategies, comprehensive safety assessments, and well-designed clinical studies are essential to support their application in functional foods and nutraceuticals.\n\nCandidates:\nA. Algae-derived bioactive peptides are not gaining recognition as functional ingredients offering health benefits and sustainability advantages over conventional proteins.\nB. Algae-derived bioactive peptides are gaining recognition as functional ingredients offering health benefits and sustainability advantages over conventional proteins.\nC. Peer-reviewed studies reporting bioactive peptides derived from microalgae, macroalgae, or Cyanophyceae were not included.\nD. This review aimed to evaluate the current evidence on algal peptides, focusing on their nutritional content, mechanistic actions, health effects, potential for sustainability, and translational challenges.\nE. The evidence does not state that this review aimed to evaluate the current evidence on algal peptides, focusing on their nutritional content, mechanistic actions, health effects, potential for sustainability, and translational challenges.\nF. Peer-reviewed studies reporting bioactive peptides derived from microalgae, macroalgae, or Cyanophyceae were included.\nG. A comprehensive literature search was not conducted across PubMed, Scopus, ScienceDirect, Springer, Elsevier, and Google Scholar.\nH. A comprehensive literature search was conducted across PubMed, Scopus, ScienceDirect, Springer, Elsevier, and Google Scholar.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12973848", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12973848/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a6d730a29b0b3696718a", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"3 h\", \"2 h\"]\n\nEvidence:\nNeoscytalidium dimidiatum is a non-dermatophyte mold that commonly causes skin and nail infections in tropical regions and often resists conventional antifungal therapies. Because its clinical and laboratory features often resemble dermatophyte infections, diagnosis is frequently delayed and treatment is sometimes inappropriate. We therefore developed a dot-immunobinding assay (Dot-Iba) to detect N. dimidiatum antigens. We generated a highly specific monoclonal antibody, 3E6F7 (MAb 3E6F7), for antigen capture, and used goat anti-mouse Ig conjugated with alkaline phosphatase (AP) as the signal generator. The test pad comprised a test hole, a nitrocellulose membrane (NC), and water-absorbent pads in a vertical flow-through format to allow a rapid antigen–antibody reaction. The assembled system detected N. dimidiatum antigens in vitro with high specificity and yielded visible results within 2 h; its detection limit was 0.9 µg without cross-reactivity to dermatophyte or non-dermatophyte fungi. This rapid, specific, and easy-to-use assay shows strong potential as a diagnostic tool, particularly in settings with limited access to fungal culture or advanced molecular diagnostics, where early, accurate identification is crucial. Keywords: Dot-immunobinding assay (Dot-Iba), Fungal foot infection, Nail infection, Neoscytalidium dimidiatum , Rapid test", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12975793", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12975793/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ae0682a607f24dadcc37", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPork carcass fabrication is a central determinant of value realization within packing and processing systems, translating biological variation in carcass weight and composition into economic outcomes under dynamic market conditions. The objective of this review is to synthesize current knowledge on the economic drivers of pork carcass fabrication, with a specific focus on the interactions among carcass characteristics, fabrication strategies, and value realization. Regional differences in cutting specifications and market orientation are discussed as key factors shaping primal yields, market allocation, and value distribution across domestic and export channels. The economic contributions of primals, subprimals, trim, fat, and by-products are examined in the context of wholesale pricing signals, carcass merit programs, and packer-specific specifications that link production decisions with downstream processing requirements. Fabrication strategies, including depth of fabrication and primal-specific cutting decisions, are evaluated with respect to yield optimization, labor and packaging costs, and market flexibility. The influence of carcass weight and composition on fabrication efficiency, trim generation, and fixed cost allocation is highlighted, illustrating trade-offs between biological performance and processing constraints. Technological advancements, including instrument grading, automation, and data integration, are reviewed for their role in improving yield prediction, carcass sorting, and operational consistency, while emerging tools such as predictive modeling are identified as promising approaches for managing variability and economic risk. Price volatility, biological variability, and supply chain disruptions are identified as persistent challenges to fabrication economics, underscoring the need for resilient and adaptable processing systems. Beyond economic performance, fabrication decisions are discussed in relation to labor welfare and sustainability outcomes. Collectively, this review emphasizes that optimal pork carcass fabrication is achieved through the strategic integration of biological inputs, economic signals, and operational capabilities. Improved data transparency and collaboration between industry professionals are essential to develop integrated biological-economic frameworks that enhance value realization and long-term sustainability across the pork supply chain.\n\nCandidates:\nA. The objective of this review is not to synthesize current knowledge on the economic drivers of pork carcass fabrication, with a specific focus on the interactions among carcass characteristics, fabrication strategies, and value realization.\nB. The objective of this review is to synthesize current knowledge on the economic drivers of pork carcass fabrication, with a specific focus on the interactions among carcass characteristics, fabrication strategies, and value realization.\nC. Pork carcass fabrication is a central determinant of value realization within packing and processing systems, translating biological variation in carcass weight and composition into economic outcomes under dynamic market conditions.\nD. The economic contributions of primals, subprimals, trim, fat, and by-products are not examined in the context of wholesale pricing signals, carcass merit programs, and packer-specific specifications that link production decisions with downstream processing requirements.\nE. Regional differences in cutting specifications and market orientation are discussed as key factors shaping primal yields, market allocation, and value distribution across domestic and export channels.\nF. The economic contributions of primals, subprimals, trim, fat, and by-products are examined in the context of wholesale pricing signals, carcass merit programs, and packer-specific specifications that link production decisions with downstream processing requirements.\nG. Regional differences in cutting specifications and market orientation are not discussed as key factors shaping primal yields, market allocation, and value distribution across domestic and export channels.\nH. Pork carcass fabrication is not a central determinant of value realization within packing and processing systems, translating biological variation in carcass weight and composition into economic outcomes under dynamic market conditions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12978301", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12978301/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3d604d7877f259810b06", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe integration of artificial intelligence into medicine has led to significant advances, particularly in diagnostics and treatment planning. However, the reliability of AI models is highly dependent on the quality of the training data, especially in medical imaging, where varying patient data and evolving medical knowledge pose a challenge to the accuracy and generalizability of given datasets. The proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability. Through an automated indexing process, CT image series are semantically enhanced using the TotalSegmentator framework for segmentation and resulting SNOMED CT annotations. The metadata is standardized with HL7 FHIR resources to enable efficient data recognition and data exchange between research projects. The study successfully integrates a robust process within the UKSH MeDIC, leading to the semantic enrichment of over 1.7 million CT image series and over 50 million SNOMED CT annotations. The standardized representation using HL7 FHIR resources improves discoverability and facilitates interoperability, providing a foundation for the FAIRness of medical imaging data. However, developing automated annotation methods that can keep pace with growing clinical datasets remains a challenge to ensure continued progress in large-scale integration and indexing of medical imaging for advanced healthcare AI applications. Keywords: Data standardization, Semantic interoperability, Artificial intelligence, Medical image processing, Computed tomography\n\nCandidates:\nA. Through an automated indexing process, CT image series are semantically enhanced using the TotalSegmentator framework for segmentation and resulting SNOMED CT annotations.\nB. The evidence does not state that the integration of artificial intelligence into medicine has led to significant advances, particularly in diagnostics and treatment planning.\nC. However, the reliability of AI models is highly dependent on the quality of the training data, especially in medical imaging, where varying patient data and evolving medical knowledge pose a challenge to the accuracy and generalizability of given datasets.\nD. The integration of artificial intelligence into medicine has led to significant advances, particularly in diagnostics and treatment planning.\nE. The proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability.\nF. Through an automated indexing process, CT image series are not semantically enhanced using the TotalSegmentator framework for segmentation and resulting SNOMED CT annotations.\nG. The evidence does not state that the proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability.\nH. However, the reliability of AI models is not highly dependent on the quality of the training data, especially in medical imaging, where varying patient data and evolving medical knowledge pose a challenge to the accuracy and generalizability of given datasets.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12980909", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12980909/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4dc65627e6a47c7292c3", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"83.51%\", \"83.37%\", \"82.37%\", \"89.37%\", \"78.35%\", \"84.51%\", \"88.37%\", \"77.35%\"]\n\nEvidence:\nTilapia significantly contributes to global food security and is an affordable protein source for most developing nations. Tilapia tilapinevirus (TiLV) poses a significant economic threat to the global tilapia industry. This study aimed to develop a rapid and accurate detection method for TiLV by synthesizing a monoclonal antibody (MAb) against it. A novel peptide, KLH-CQ, derived from the TiLV sequence, was designed considering physicochemical properties like net cationic charge, amphipathicity, helicity, and hydrophobicity. The KLH-CQ (50 µg) was used to immunize Balb/c mice with Freund's complete adjuvant. The presence of specific antibodies in the mice serum was confirmed by ELISA, which showed a high antibody titre of 2.67:0.12 (mean OD of treated Vs control sera). The mouse with the strongest immune response was used for spleen donor in hybridoma production. Epitope mapping via ELISA screening identified five positive clones (TiLV-MAb 1–5), with the most reactive clone selected for further analysis. Using Classen's method, a cutoff OD value of 1.24 ± 0.45 was determined for virus detection. The selected TiLV-MAb was then used as a probing antibody to develop a latex slide agglutination assay (TiLV-LAT) using passive adsorption method. Validation of the assay with tissue and mucus samples revealed a specificity of 88.37% and a sensitivity of 82.37% for TiLV detection. The overall accuracy of the assay was 83.51%, with positive and negative likelihood ratios of 7.06 and 0.2, respectively. The TiLV-LAT successfully detected TiLV in various tissues, showing variable sensitivity: liver (77.35%), mucus (73.53%), brain (67.92%), and kidney (62.26%). TiLV-LAT developed here has minimized the tedious steps involved in nucleic acid-based detection assays, with the recorded sensitivity and specificity; it can be used as a presumptive diagnosis for testing and point of care/farm site. Moreover, non-lethal sampling and virus testing in mucus samples would be useful for fish health monitoring.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12981439", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12981439/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c1fdfa5363085c33c17a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nExtremophiles are microorganisms that thrive in environments previously thought to be uninhabitable, including extreme temperature, salinity, pH, pressure, and radiation. These organisms, found in Archaea, Bacteria, and Eukarya, exhibit distinct structural, metabolic, and genetic adaptations, such as enhanced enzyme stability, efficient DNA repair mechanisms, and robust stress-response systems that enable survival under extreme conditions. Understanding these adaptation mechanisms is key to engineering similar traits in mesophilic organisms. This review discusses the diversity of extremophiles and presents phylogenetic and comparative genomic insights which may provide insights into the origins and evolution of early life on Earth We highlight recent advances in CRISPR/Cas-based genome editing, genome-scale metabolic modeling (GEM), and synthetic biology that have expanded the use of extremophiles in sustainable industrial biotechnology. The exceptional stability and catalytic efficiency of extremozymes under harsh conditions underscore their potential in various biotechnological applications. Finally, we discuss the ecological significance of extremophiles in climate change mitigation and outline current challenges and future directions in extremophile research.\n\nCandidates:\nA. Extremophiles are microorganisms that thrive in environments previously thought to be uninhabitable, including extreme temperature, salinity, pH, pressure, and radiation.\nB. Extremophiles are not microorganisms that thrive in environments previously thought to be uninhabitable, including extreme temperature, salinity, pH, pressure, and radiation.\nC. The evidence does not state that these organisms, found in Archaea, Bacteria, and Eukarya, exhibit distinct structural, metabolic, and genetic adaptations, such as enhanced enzyme stability, efficient DNA repair mechanisms, and robust stress-response systems that enable survival under extreme conditions.\nD. Understanding these adaptation mechanisms is not key to engineering similar traits in mesophilic organisms.\nE. The evidence does not state that this review discusses the diversity of extremophiles and presents phylogenetic and comparative genomic insights which may provide insights into the origins and evolution of early life on Earth We highlight recent advances in CRISPR/Cas-based genome editing, genome-scale metabolic modeling (GEM), and synthetic biology that have expanded the use of extremophiles in sustainable industrial biotechnology.\nF. Understanding these adaptation mechanisms is key to engineering similar traits in mesophilic organisms.\nG. These organisms, found in Archaea, Bacteria, and Eukarya, exhibit distinct structural, metabolic, and genetic adaptations, such as enhanced enzyme stability, efficient DNA repair mechanisms, and robust stress-response systems that enable survival under extreme conditions.\nH. This review discusses the diversity of extremophiles and presents phylogenetic and comparative genomic insights which may provide insights into the origins and evolution of early life on Earth We highlight recent advances in CRISPR/Cas-based genome editing, genome-scale metabolic modeling (GEM), and synthetic biology that have expanded the use of extremophiles in sustainable industrial biotechnology.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12982187", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12982187/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3926ee980baae40ccc37", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCancer treatment and imaging are still limited since many drugs and imaging agents can neither effectively nor selectively reach tumour tissues. Therefore, new strategies are needed to improve drug and imaging agent delivery and reduce side effects. This review focuses on targeted protein-based nanocarriers as innovative devices for cancer diagnosis and therapy (theranostics), capable of delivering drug(s) and imaging agent(s) simultaneously. We discuss overexpressed protein receptors in cancer cells that differ from normal tissue expression and can be exploited for targeted delivery. This review summarises recent preclinical studies using protein nanocarriers as targeted theranostic platforms to improve cancer treatment, reduce side effects, and enable non-invasive tracking of treatment progress. Overall, protein nanocarriers represent promising devices that combine imaging modalities and targeting strategies for more effective cancer diagnosis and therapy in the future.\n\nCandidates:\nA. Cancer treatment and imaging are still limited since many drugs and imaging agents can neither effectively nor selectively reach tumour tissues.\nB. Cancer treatment and imaging are not still limited since many drugs and imaging agents can neither effectively nor selectively reach tumour tissues.\nC. We discuss overexpressed protein receptors in cancer cells that differ from normal tissue expression and can be exploited for targeted delivery.\nD. Therefore, new strategies are needed to improve drug and imaging agent delivery and reduce side effects.\nE. We discuss overexpressed protein receptors in cancer cells that differ from normal tissue expression and cannot be exploited for targeted delivery.\nF. This review focuses on targeted protein-based nanocarriers as innovative devices for cancer diagnosis and therapy (theranostics), capable of delivering drug(s) and imaging agent(s) simultaneously.\nG. The evidence does not state that this review focuses on targeted protein-based nanocarriers as innovative devices for cancer diagnosis and therapy (theranostics), capable of delivering drug(s) and imaging agent(s) simultaneously.\nH. Therefore, new strategies are not needed to improve drug and imaging agent delivery and reduce side effects.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12984166", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12984166/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a94a69aa331c918ba890", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTulathromycin (TULA) is primarily used for treating respiratory diseases in livestock. However, its misuse may lead to bacterial resistance and poses potential health risks such as chronic toxicity and allergic reactions through the food chain. Therefore, it is essential to develop rapid and accurate detection methods. In this study, two quantum dot-based fluorescent immunosorbent assays—direct competitive FLISA (dc-FLISA) and indirect competitive FLISA (ic-FLISA)—were established for detecting TULA residues in milk. The dc-FLISA exhibited a half-maximal inhibitory concentration (IC 50 ) of 1.99 ng·mL −1 , a limit of detection (LOD) of 0.018 ng·mL −1 , and a detection range of 0.058–69.18 ng·mL −1 . The ic-FLISA showed an IC 50 of 0.89 ng·mL −1 , an LOD of 0.005 ng·mL −1 , and a detection range of 0.019–42.65 ng·mL −1 . Spiked recovery tests in milk demonstrated recovery rates ranging from 97.41% to 101.02% for dc-FLISA and from 97.48% to 100.65% for ic-FLISA, with coefficients of variation below 10%. In summary, two simple, effective, rapid, and sensitive methods were successfully developed for detecting TULA residues in milk.\n\nCandidates:\nA. However, its misuse may lead to bacterial resistance and poses potential health risks such as chronic toxicity and allergic reactions through the food chain.\nB. The evidence does not state that in this study, two quantum dot-based fluorescent immunosorbent assays—direct competitive FLISA (dc-FLISA) and indirect competitive FLISA (ic-FLISA)—were established for detecting TULA residues in milk.\nC. Therefore, it is essential to develop rapid and accurate detection methods.\nD. In this study, two quantum dot-based fluorescent immunosorbent assays—direct competitive FLISA (dc-FLISA) and indirect competitive FLISA (ic-FLISA)—were established for detecting TULA residues in milk.\nE. Tulathromycin (TULA) is primarily used for treating respiratory diseases in livestock.\nF. Tulathromycin (TULA) is not primarily used for treating respiratory diseases in livestock.\nG. The evidence does not state that however, its misuse may lead to bacterial resistance and poses potential health risks such as chronic toxicity and allergic reactions through the food chain.\nH. Therefore, it is not essential to develop rapid and accurate detection methods.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12984338", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12984338/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-60d39e037ece6f15d72d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAlgae-derived proteins and peptides have gained increasing interest as sustainable bioresources with valuable nutritional and functional properties. This review aims to synthesize current knowledge on their characteristics and applications while highlighting the emerging role of computational tools in peptide research. Key findings show that algae provide diverse proteins and bioactive peptides with advantageous amino acid profiles and notable antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and skin-protective activities. Their applications span food formulation, pharmaceuticals, and cosmetics, although large-scale utilization remains constrained by production, stability, and bioavailability challenges. Computational strategies, including virtual enzymatic hydrolysis, machine-learning prediction, QSAR modeling, molecular docking, molecular dynamics, and toxicity/allergenicity assessment, offer promising avenues for efficient peptide discovery, though their use in algae is still limited. Overall, this review underscores the potential of algae-derived proteins and peptides as multifunctional ingredients and emphasizes the need to integrate in silico pipelines with improved processing and delivery systems to accelerate future translational applications.\n\nCandidates:\nA. The evidence does not state that their applications span food formulation, pharmaceuticals, and cosmetics, although large-scale utilization remains constrained by production, stability, and bioavailability challenges.\nB. The evidence does not state that algae-derived proteins and peptides have gained increasing interest as sustainable bioresources with valuable nutritional and functional properties.\nC. The evidence does not state that key findings show that algae provide diverse proteins and bioactive peptides with advantageous amino acid profiles and notable antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and skin-protective activities.\nD. Algae-derived proteins and peptides have gained increasing interest as sustainable bioresources with valuable nutritional and functional properties.\nE. Their applications span food formulation, pharmaceuticals, and cosmetics, although large-scale utilization remains constrained by production, stability, and bioavailability challenges.\nF. Key findings show that algae provide diverse proteins and bioactive peptides with advantageous amino acid profiles and notable antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and skin-protective activities.\nG. The evidence does not state that this review aims to synthesize current knowledge on their characteristics and applications while highlighting the emerging role of computational tools in peptide research.\nH. This review aims to synthesize current knowledge on their characteristics and applications while highlighting the emerging role of computational tools in peptide research.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12985022", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12985022/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-50c197d43f443281c8d1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBackground/Objectives : Multiple sclerosis (MS) imposes a substantial clinical, humanistic, and economic burden, and current disease-modifying therapies require lifelong administration without restoring immune tolerance. IMMUTOL, a tolerogenic gene therapy under development within an EU-funded programme, aims to induce durable remission. Methods : This study assessed the early financial feasibility of IMMUTOL using a structured risk-adjusted net present value (rNPV) model, incorporating development and operating costs, probabilities of clinical and regulatory success, manufacturing expenditure, market dynamics, and revenue projections. Uncertainty was examined through one-way, probabilistic, and scenario analyses. Results : Under base-case assumptions, IMMUTOL generated a deterministic rNPV of −$223.8 million with an internal rate of return of 3.4%. Probabilistic analysis yielded a mean rNPV of −$99.4 million and a mean internal rate of return of 10.5%, with 70.2% of simulations producing negative values. Only scenarios combining higher treatment prices with lower manufacturing costs produced consistently positive rNPVs; a price of $1.5 million with a $200,000 production cost resulted in an rNPV of $711.2 million and an internal rate of return of 20.7%. Neither increased market size, reduced time to approval, nor modest cost reductions altered the conclusion. Conclusions : These findings emphasise a structural gap between value-based pricing and the pricing required for commercial viability. Without external support or reductions in cost structures, commercial development may be economically unattractive.\n\nCandidates:\nA. Methods : This study assessed the early financial feasibility of IMMUTOL using a structured risk-adjusted net present value (rNPV) model, incorporating development and operating costs, probabilities of clinical and regulatory success, manufacturing expenditure, market dynamics, and revenue projections.\nB. The evidence does not state that methods : This study assessed the early financial feasibility of IMMUTOL using a structured risk-adjusted net present value (rNPV) model, incorporating development and operating costs, probabilities of clinical and regulatory success, manufacturing expenditure, market dynamics, and revenue projections.\nC. The evidence does not state that iMMUTOL, a tolerogenic gene therapy under development within an EU-funded programme, aims to induce durable remission.\nD. IMMUTOL, a tolerogenic gene therapy under development within an EU-funded programme, aims to induce durable remission.\nE. The evidence does not state that background/Objectives : Multiple sclerosis (MS) imposes a substantial clinical, humanistic, and economic burden, and current disease-modifying therapies require lifelong administration without restoring immune tolerance.\nF. Background/Objectives : Multiple sclerosis (MS) imposes a substantial clinical, humanistic, and economic burden, and current disease-modifying therapies require lifelong administration without restoring immune tolerance.\nG. Uncertainty was examined through one-way, probabilistic, and scenario analyses.\nH. Uncertainty was not examined through one-way, probabilistic, and scenario analyses.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12985023", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12985023/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4a316814e59fe5967328", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTestosterone is a vital steroid hormone with important physiological roles and broad clinical significance, serving as a central molecular precursor in the synthesis of many pharmacologically active steroids. Testosterone is traditionally produced through complex chemical synthesis routes that involve hazardous reagents, harsh conditions, and produce significant toxic waste. In recent decades, growing regulatory requirements and environmental sustainability goals have spurred the development of alternative biotechnological methods that use microbial biotransformation. This review offers a comparative analysis of chemical and biological methods for producing testosterone, focusing on microbial steroid biotransformation pathways and the key enzymatic steps involved in testosterone biosynthesis. It examines key advances in sterol breakdown, pathway engineering, and enzyme driven modifications, including the roles of 17β-hydroxysteroid dehydrogenases and cytochrome P450 monooxygenases. The performance, specificity, and environmental impacts of bacterial and fungal cells as cell factories, especially Mycolicibacterium and Aspergillus species, are critically analyzed within the framework of modern green chemistry principles. Overall, by combining molecular insights with process considerations, this review illustrates how microbial platforms could complement and gradually transform traditional chemical synthesis methods, promoting a shift toward more sustainable steroid hormone production through engineered biocatalysts.\n\nCandidates:\nA. This review offers a comparative analysis of chemical and biological methods for producing testosterone, focusing on microbial steroid biotransformation pathways and the key enzymatic steps involved in testosterone biosynthesis.\nB. Testosterone is not a vital steroid hormone with important physiological roles and broad clinical significance, serving as a central molecular precursor in the synthesis of many pharmacologically active steroids.\nC. Testosterone is a vital steroid hormone with important physiological roles and broad clinical significance, serving as a central molecular precursor in the synthesis of many pharmacologically active steroids.\nD. The evidence does not state that this review offers a comparative analysis of chemical and biological methods for producing testosterone, focusing on microbial steroid biotransformation pathways and the key enzymatic steps involved in testosterone biosynthesis.\nE. In recent decades, growing regulatory requirements and environmental sustainability goals have spurred the development of alternative biotechnological methods that use microbial biotransformation.\nF. Testosterone is not traditionally produced through complex chemical synthesis routes that involve hazardous reagents, harsh conditions, and produce significant toxic waste.\nG. The evidence does not state that in recent decades, growing regulatory requirements and environmental sustainability goals have spurred the development of alternative biotechnological methods that use microbial biotransformation.\nH. Testosterone is traditionally produced through complex chemical synthesis routes that involve hazardous reagents, harsh conditions, and produce significant toxic waste.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12985434", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12985434/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b6e53918eb96b0a30dde", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nArtificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly transforming the study, manufacturing, and clinical translation of adipose-derived stem/stromal cells (ADSCs). ADSC-based therapies face persistent challenges related to donor variability, heterogeneous cell populations, limited standardization of culture protocols, and the need for robust quality control (QC) and potency assessment under Good Manufacturing Practice (GMP) conditions. This review discusses how AI-driven approaches can support the ADSC pipeline from donor and tissue pre-screening, through isolation and expansion, to differentiation and batch release decisions. We highlight major methodological advances in computer vision and label-free imaging for monitoring morphology, confluency, proliferation, senescence, and contamination, as well as AI-assisted optimization strategies for culture parameters and differentiation protocols. In addition, we examine the growing role of multi-omics integration (transcriptomics, proteomics, metabolomics, and secretomics) combined with ML to predict functional potency, stratify donors, and identify biomarkers associated with therapeutic efficacy. Finally, we address current limitations, including data scarcity, inter-laboratory variability, model interpretability, and regulatory requirements, and outline future perspectives such as closed-loop bioprocess control, foundation models, and federated learning frameworks. Overall, AI offers a powerful toolkit to improve the reproducibility, safety, and scalability of ADSC manufacturing and to accelerate the development of standardized, data-driven regenerative medicine products.\n\nCandidates:\nA. This review discusses how AI-driven approaches cannot support the ADSC pipeline from donor and tissue pre-screening, through isolation and expansion, to differentiation and batch release decisions.\nB. The evidence does not state that we highlight major methodological advances in computer vision and label-free imaging for monitoring morphology, confluency, proliferation, senescence, and contamination, as well as AI-assisted optimization strategies for culture parameters and differentiation protocols.\nC. This review discusses how AI-driven approaches can support the ADSC pipeline from donor and tissue pre-screening, through isolation and expansion, to differentiation and batch release decisions.\nD. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is not increasingly transforming the study, manufacturing, and clinical translation of adipose-derived stem/stromal cells (ADSCs).\nE. The evidence does not state that aDSC-based therapies face persistent challenges related to donor variability, heterogeneous cell populations, limited standardization of culture protocols, and the need for robust quality control (QC) and potency assessment under Good Manufacturing Practice (GMP) conditions.\nF. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly transforming the study, manufacturing, and clinical translation of adipose-derived stem/stromal cells (ADSCs).\nG. We highlight major methodological advances in computer vision and label-free imaging for monitoring morphology, confluency, proliferation, senescence, and contamination, as well as AI-assisted optimization strategies for culture parameters and differentiation protocols.\nH. ADSC-based therapies face persistent challenges related to donor variability, heterogeneous cell populations, limited standardization of culture protocols, and the need for robust quality control (QC) and potency assessment under Good Manufacturing Practice (GMP) conditions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12986042", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12986042/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f70afb7280d002edf613", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMacroalgae represent a promising third-generation feedstock for biorefinery due to their high biomass productivity and non-reliance on arable land. However, their complex cell wall structure poses a significant barrier to efficient bioconversion. This review integrates current pretreatment methods, including physical, chemical, biological, and combined approaches, with a focus on their mechanisms, effectiveness, and limitations. Furthermore, it explores the conversion of pretreated macroalgal biomass into bioenergy and biochemicals, such as bioethanol, organic acid and polyhydroxyalkanoate, via microbial fermentation. The review also examines the application of genetic editing tools (e.g., CRISPR-Cas systems) for the targeted modification of macroalgae to improve their inherent characteristics for biorefinery, such as reducing biomass recalcitrance or increasing the content of target carbohydrates. Finally, future perspectives on technological innovations and integrated industrial chains of macroalgal biorefinery are discussed. This review serves as a systematic reference for deepening the understanding of macroalgal cell wall deconstruction processes and supports the development of efficient and environmentally benign pretreatment strategies to advance macroalgal biorefinery toward industrialization.\n\nCandidates:\nA. Macroalgae represent a promising third-generation feedstock for biorefinery due to their high biomass productivity and non-reliance on arable land.\nB. Furthermore, it explores the conversion of pretreated macroalgal biomass into bioenergy and biochemicals, such as bioethanol, organic acid and polyhydroxyalkanoate, via microbial fermentation.\nC. The evidence does not state that however, their complex cell wall structure poses a significant barrier to efficient bioconversion.\nD. The evidence does not state that macroalgae represent a promising third-generation feedstock for biorefinery due to their high biomass productivity and non-reliance on arable land.\nE. However, their complex cell wall structure poses a significant barrier to efficient bioconversion.\nF. The evidence does not state that furthermore, it explores the conversion of pretreated macroalgal biomass into bioenergy and biochemicals, such as bioethanol, organic acid and polyhydroxyalkanoate, via microbial fermentation.\nG. This review integrates current pretreatment methods, including physical, chemical, biological, and combined approaches, with a focus on their mechanisms, effectiveness, and limitations.\nH. The evidence does not state that this review integrates current pretreatment methods, including physical, chemical, biological, and combined approaches, with a focus on their mechanisms, effectiveness, and limitations.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12986301", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12986301/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f935216940585265d6a1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis study explores the application of immobilized phospholipase A1 (PLA1) on hollow double-layer mesoporous silica nanoparticles (PLA1@NH 2 /C 8 -HdlMS) for the degumming of crude arachidonic acid (ARA) oil for the first time. The immobilized enzyme was comprehensively characterized, and the reaction conditions were optimized via single-factor experiments. Under the optimized conditions (enzyme dosage 0.3% w / w , 35 °C, water addition 3%, and reaction time 90 min), PLA1@NH 2 /C 8 -HdlMS achieved a remarkable phosphorus removal rate of 97.9%, reducing the phosphorus content from 441.21 mg/kg to 9.29 mg/kg in 90 min (well below the food-grade standard of <10 mg/kg). The fatty acid composition of the oil remained almost unchanged, while the oxidative induction time of the degummed oil significantly improved by 42%. Notably, PLA1@NH 2 /C 8 -HdlMS demonstrated broad applicability across crude oils, with initial phosphorus contents ranging from 294.98 mg/kg to 537.44 mg/kg, and it maintained ~93% of its initial activity after 11 reuse cycles. Compared to traditional hydration degumming (with a phosphorus removal rate of 56.3%), this enzymatic method offers superior efficiency at lower temperatures, minimizing energy consumption and the thermal degradation of ARA. This green, efficient, and sustainable method for degumming heat-sensitive oils offers significant potential for the industrial application of high-quality functional oils by preserving PUFA integrity and reducing environmental impact.\n\nCandidates:\nA. Under the optimized conditions (enzyme dosage 0.3% w / w , 35 °C, water addition 3%, and reaction time 90 min), PLA1@NH 2 /C 8 -HdlMS achieved a remarkable phosphorus removal rate of 97.9%, reducing the phosphorus content from 441.21 mg/kg to 9.29 mg/kg in 90 min (well below the food-grade standard of <10 mg/kg).\nB. The immobilized enzyme was comprehensively characterized, and the reaction conditions were optimized via single-factor experiments.\nC. This study explores the application of immobilized phospholipase A2 (PLA1) on hollow double-layer mesoporous silica nanoparticles (PLA1@NH 2 /C 8 -HdlMS) for the degumming of crude arachidonic acid (ARA) oil for the first time.\nD. The fatty acid composition of the oil remained almost unchanged, while the oxidative induction time of the degummed oil significantly improved by 42%.\nE. This study explores the application of immobilized phospholipase A1 (PLA1) on hollow double-layer mesoporous silica nanoparticles (PLA1@NH 2 /C 8 -HdlMS) for the degumming of crude arachidonic acid (ARA) oil for the first time.\nF. Under the optimized conditions (enzyme dosage 1.3% w / w , 35 °C, water addition 3%, and reaction time 90 min), PLA1@NH 2 /C 8 -HdlMS achieved a remarkable phosphorus removal rate of 97.9%, reducing the phosphorus content from 441.21 mg/kg to 9.29 mg/kg in 90 min (well below the food-grade standard of <10 mg/kg).\nG. The fatty acid composition of the oil remained almost unchanged, while the oxidative induction time of the degummed oil significantly improved by 43%.\nH. The immobilized enzyme was not comprehensively characterized, and the reaction conditions were optimized via single-factor experiments.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12986537", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12986537/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-45640c4cb35f9288da72", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSurface fouling remains a critical challenge for medical devices and chemosensor systems operating in biological environments, where nonspecific adsorption of proteins, cells, and microorganisms can lead to signal drift, reduced sensitivity, and shortened device lifetime. Conventional antifouling strategies rely primarily on synthetic hydrophilic polymer coatings, such as polyethylene glycol and polyvinylpyrrolidone, which are effective but face limitations related to long-term stability, thickness, and compatibility with surface-sensitive sensing modalities. In this review, we focus on hydrophobins derived from mushroom-forming and filamentous fungi as a bio-based alternative for antifouling and anti-wetting surface modification. Mushroom-derived hydrophobins are small amphiphilic proteins capable of spontaneous self-assembly into nanometer-scale films that modulate surface energy, wettability, and interfacial friction without requiring covalent functionalization. The current state of research on hydrophobin structure, classification, and self-assembly is reviewed, followed by a synthesis of reported antifouling and tribological behaviors relevant to medical and sensor-adjacent surfaces. Representative experimental observations are discussed to illustrate trends consistent with the literature, without establishing new performance benchmarks. The implications of mushroom-derived hydrophobin coatings for chemosensors and biosensors are examined, particularly with respect to signal stability, surface accessibility, and durability. Limitations and future research directions are outlined to support translation into practical sensing technologies.\n\nCandidates:\nA. Conventional antifouling strategies rely primarily on synthetic hydrophilic polymer coatings, such as polyethylene glycol and polyvinylpyrrolidone, which are not effective but face limitations related to long-term stability, thickness, and compatibility with surface-sensitive sensing modalities.\nB. Mushroom-derived hydrophobins are not small amphiphilic proteins capable of spontaneous self-assembly into nanometer-scale films that modulate surface energy, wettability, and interfacial friction without requiring covalent functionalization.\nC. Mushroom-derived hydrophobins are small amphiphilic proteins capable of spontaneous self-assembly into nanometer-scale films that modulate surface energy, wettability, and interfacial friction without requiring covalent functionalization.\nD. Conventional antifouling strategies rely primarily on synthetic hydrophilic polymer coatings, such as polyethylene glycol and polyvinylpyrrolidone, which are effective but face limitations related to long-term stability, thickness, and compatibility with surface-sensitive sensing modalities.\nE. The evidence does not state that in this review, we focus on hydrophobins derived from mushroom-forming and filamentous fungi as a bio-based alternative for antifouling and anti-wetting surface modification.\nF. Surface fouling remains a critical challenge for medical devices and chemosensor systems operating in biological environments, where nonspecific adsorption of proteins, cells, and microorganisms cannot lead to signal drift, reduced sensitivity, and shortened device lifetime.\nG. In this review, we focus on hydrophobins derived from mushroom-forming and filamentous fungi as a bio-based alternative for antifouling and anti-wetting surface modification.\nH. Surface fouling remains a critical challenge for medical devices and chemosensor systems operating in biological environments, where nonspecific adsorption of proteins, cells, and microorganisms can lead to signal drift, reduced sensitivity, and shortened device lifetime.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12986670", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12986670/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c95b8d0889d2ed4fc456", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCoronaviruses, both known and yet to emerge, pose persistent zoonotic and pandemic threats. While current parenteral COVID-19 mRNA vaccines effectively mitigate severe disease caused by SARS-CoV-2, they primarily elicit systemic immunity restricted to specific variants within clade 1b of the sarbecovirus subgenus and provide limited mucosal protection. Addressing these shortcomings, Cheang et al. developed a DC-targeting intranasal booster vaccine that induces robust and durable mucosal and systemic immunity across sarbecovirus clades 1a and 1b. This study highlights a promising strategy for pan-sarbecovirus vaccines by leveraging mucosal immune induction to prevent viral transmission and enhance pandemic preparedness.\n\nCandidates:\nA. developed a DC-targeting intranasal booster vaccine that induces robust and durable mucosal and systemic immunity across sarbecovirus clades 2a and 1b.\nB. developed a DC-targeting intranasal booster vaccine that induces robust and durable mucosal and systemic immunity across sarbecovirus clades 1a and 1b.\nC. The evidence does not state that addressing these shortcomings, Cheang et al.\nD. The evidence does not state that coronaviruses, both known and yet to emerge, pose persistent zoonotic and pandemic threats.\nE. Addressing these shortcomings, Cheang et al.\nF. Coronaviruses, both known and yet to emerge, pose persistent zoonotic and pandemic threats.\nG. While current parenteral COVID-20 mRNA vaccines effectively mitigate severe disease caused by SARS-CoV-2, they primarily elicit systemic immunity restricted to specific variants within clade 1b of the sarbecovirus subgenus and provide limited mucosal protection.\nH. While current parenteral COVID-19 mRNA vaccines effectively mitigate severe disease caused by SARS-CoV-2, they primarily elicit systemic immunity restricted to specific variants within clade 1b of the sarbecovirus subgenus and provide limited mucosal protection.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12987610", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12987610/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-620c7736b0c984e4a889", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nThe production of engineered proteins in transgenic cells is widely used in research, medicine and industry. However, conventional cell-based production systems still face challenges in cost, scalability and biosafety. Here, we present a recombinant protein expression platform with simplified purification based on the photosynthetic unicellular red alga Cyanidioschyzon merolae , which can be cultivated under highly acidic conditions using only inorganic nutrients, air, water and light. We first identified a promoter that drives high-level constitutive gene expression throughout the cell cycle, resulting in substantial mRNA accumulation in C. merolae . A stable transformant expressing His-tagged mVenus under the control of this promoter accumulated the recombinant protein to more than 1% of total soluble protein. The simple cellular architecture of C. merolae , including the absence of a cell wall, enables efficient protein extraction via a single freeze–thaw cycle, followed by purification using immobilized metal affinity chromatography (IMAC), yielding ∼13.9 mg of functional recombinant protein per gram of total soluble protein. Owing to its low cost, scalability, operational simplicity and minimal risk of contamination, this Cyanidioschyzon -based platform offers a practical and promising approach to recombinant protein production in a photosynthetic eukaryote.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12989068", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12989068/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-03f5966fd586870d52df", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nL-Alanyl-L-glutamine (Ala-Gln) is a high-value dipeptide with superior stability, solubility, and bioavailability, underscoring its potential for nutritional supplementation. Compared with conventional chemical catalysis, whole-cell biocatalysts offer a more efficient, simpler, and environmentally friendly alternative for peptide synthesis. Among these, enzyme cell-surface immobilization systems enable the stable display of target enzymes on yeast cells, thereby enhancing enzyme stability while simplifying catalyst recovery and reuse, which is particularly advantageous for large-scale industrial applications. In this study, an engineered Saccharomyces cerevisiae strain displaying α-amino acid ester acyltransferase (SsAET) from Sphingobacterium siyangensis SY1 on the cell surface was developed for clean and efficient biocatalytic production of Ala-Gln. Optimal reaction conditions were established (pH 8.0, 3 h, 20 g DCW/l, AlaOMe/Gl n = 1:2), resulting in a 6.7-fold increase in Ala-Gln production compared with the pre-optimization conditions. Under these optimal conditions, repeated-batch reactions with an increased reaction volume of 20 ml achieved a maximum Ala-Gln concentration of 14.12 mM, representing the highest yield obtained in this study. The SsAET whole-cell biocatalyst retained more than 60% of its relative Ala–Gln production after three consecutive reaction cycles, while the conversion rate based on Gln consumption decreased only slightly from 32.08% to 29.65%, remaining essentially stable. Overall, this study demonstrates a clean, efficient, and reusable whole-cell biocatalytic system based on enzyme cell-surface immobilization for Ala-Gln production, highlighting its potential for industrial-scale peptide synthesis.\n\nCandidates:\nA. In this study, an engineered Saccharomyces cerevisiae strain displaying α-amino acid ester acyltransferase (SsAET) from Sphingobacterium siyangensis SY1 on the cell surface was developed for clean and efficient biocatalytic production of Ala-Gln.\nB. Among these, enzyme cell-surface immobilization systems enable the stable display of target enzymes on yeast cells, thereby enhancing enzyme stability while simplifying catalyst recovery and reuse, which is particularly advantageous for large-scale industrial applications.\nC. Compared with conventional chemical catalysis, whole-cell biocatalysts offer a more efficient, simpler, and environmentally friendly alternative for peptide synthesis.\nD. L-Alanyl-L-glutamine (Ala-Gln) is not a high-value dipeptide with superior stability, solubility, and bioavailability, underscoring its potential for nutritional supplementation.\nE. In this study, an engineered Saccharomyces cerevisiae strain displaying α-amino acid ester acyltransferase (SsAET) from Sphingobacterium siyangensis SY2 on the cell surface was developed for clean and efficient biocatalytic production of Ala-Gln.\nF. The evidence does not state that compared with conventional chemical catalysis, whole-cell biocatalysts offer a more efficient, simpler, and environmentally friendly alternative for peptide synthesis.\nG. Among these, enzyme cell-surface immobilization systems enable the stable display of target enzymes on yeast cells, thereby enhancing enzyme stability while simplifying catalyst recovery and reuse, which is not particularly advantageous for large-scale industrial applications.\nH. L-Alanyl-L-glutamine (Ala-Gln) is a high-value dipeptide with superior stability, solubility, and bioavailability, underscoring its potential for nutritional supplementation.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12989794", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12989794/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-102995fe7db8dad8da5d", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"36%\", \"35%\"]\n\nEvidence:\nD-tagatose is a low-calorie natural rare sugar with significant potential in the food and pharmaceutical industries. Conventional production relies on the enzymatic isomerization of D-galactose, a process limited by an unfavorable thermodynamic equilibrium and high substrate costs. This study presents a novel whole-cell biocatalytic approach for direct production of D-tagatose from lactose, an inexpensive and abundant sugar in dairy waste streams, such as whey permeate. The main lactose permease (lacY) of Escherichia coli BL21(DE3) was deleted, creating a clean host chassis that, due to its native galactose auxotrophy, was incapable of utilizing galactose. Subsequently, genes from the tagatose-6-phosphate (T6P) pathway of Lactococcus lactis , comprising a lactose-specific phosphotransferase system (PTS), a 6-phospho-β-galactosidase, a galactose-6-phosphate isomerase and the general PTS proteins, were modularly introduced, which allowed for the functional validation of each component. The crucial final step involved the expression of a sugar phosphatase to convert the intracellular intermediate, tagatose-6-phosphate, into D-tagatose. The fully engineered strain yielded a 35% conversion ratio of the galactose moiety of lactose to tagatose. This demonstrates the repurposing of the tagatose-6-phosphate pathway in E. coli to enable direct lactose utilization and growth-coupled production of D-tagatose from both lactose and whey permeate, without prior hydrolysis or substrate enrichment. Fermentation and tagatose production using whey permeate as the sole carbon source was also demonstrated, highlighting the potential of this system for the valorization of dairy byproducts into a high-value low calorie sweetener.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12992531", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12992531/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-197c7b6e85158ee59ad5", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBehavior plays a critical role in health and disease. Although molecular omics have advanced the understanding of biological mechanisms, they alone cannot explain the role of behavior in health, and traditional behavioral measurement technologies have struggled to capture the dynamic, multidimensional, and heterogeneous nature of real-world behaviors. With the development of digital technologies such as wearable devices, smartphone sensors, and ecological momentary assessment, behavioromics has emerged as a new research paradigm with a holistic systems perspective, integrating continuous, multimodal behavioral data with molecular, physiological, and environmental information. By organizing behavioral measurement, modeling, and interpretation within a unified analytical framework, behavioromics reveals dynamic behavioral patterns, supports mechanism-informed inference, and identifies actionable targets for early intervention. Despite ongoing challenges in data heterogeneity, causal interpretation, and ethical governance, behavioromics holds promise for reshaping disease prediction, early detection, and precision intervention, and for advancing proactive health through earlier, behavior-centered prevention.\n\nCandidates:\nA. With the development of digital technologies such as wearable devices, smartphone sensors, and ecological momentary assessment, behavioromics has emerged as a new research paradigm with a holistic systems perspective, integrating continuous, multimodal behavioral data with molecular, physiological, and environmental information.\nB. The evidence does not state that with the development of digital technologies such as wearable devices, smartphone sensors, and ecological momentary assessment, behavioromics has emerged as a new research paradigm with a holistic systems perspective, integrating continuous, multimodal behavioral data with molecular, physiological, and environmental information.\nC. The evidence does not state that behavior plays a critical role in health and disease.\nD. The evidence does not state that although molecular omics have advanced the understanding of biological mechanisms, they alone cannot explain the role of behavior in health, and traditional behavioral measurement technologies have struggled to capture the dynamic, multidimensional, and heterogeneous nature of real-world behaviors.\nE. By organizing behavioral measurement, modeling, and interpretation within a unified analytical framework, behavioromics reveals dynamic behavioral patterns, supports mechanism-informed inference, and identifies actionable targets for early intervention.\nF. Although molecular omics have advanced the understanding of biological mechanisms, they alone cannot explain the role of behavior in health, and traditional behavioral measurement technologies have struggled to capture the dynamic, multidimensional, and heterogeneous nature of real-world behaviors.\nG. Behavior plays a critical role in health and disease.\nH. The evidence does not state that by organizing behavioral measurement, modeling, and interpretation within a unified analytical framework, behavioromics reveals dynamic behavioral patterns, supports mechanism-informed inference, and identifies actionable targets for early intervention.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12992924", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12992924/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6cde790d0ff915b9a685", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe aim of this work was to design, develop, and optimise a two-step process for extracting lignin from Eucalyptus globulus residues and subsequently converting the remaining holocellulose into monosaccharides, primarily glucose and xylose. Firstly, the solubility of high-quality lignin was optimised using a new hydro-organo-thermal method involving a diluted imidazole solution in water instead of pure imidazole. Subsequently, enzymatic hydrolysis of the polysaccharides-rich pretreated solid was optimised to give glucose and xylose by using the commercial enzymatic mixture Cellic® CTec3 HS (Novozymes, Bagsværd, Denmark) supplemented with endo-1,4-β-xylanase from Trichoderma viride (Merck, Darmstadt, Germany). The extraction process was thoroughly optimised by acting on several parameters, such as the imidazole concentration, temperature, reaction time, and solid-to-liquid ratio. Under the optimised reaction conditions (imidazole 35 wt%, biomass loading 10 wt%, 120 °C, 2 h, 400 rpm) over the entire process, about 70 wt% of lignin removal was achieved, together with glucose and xylose yields of 99 and 92 mol%, respectively, reaching a high total sugar concentration of 91 g/L in the hydrolysate. • A new hydro-organo-thermal treatment of lignocellulosic biomass has been proposed. • Glucose and xylose yields were 99 and 92 mol% in 48 h at low enzyme dosage. • A total reducing sugar concentration of 91 g/L was achieved in the hydrolysate. The online version contains supplementary material available at 10.1007/s00253-026-13793-2. Keywords: Biorefinery, Eucalyptus globulus , Lignin recovery, Sugars, Enzymatic hydrolysis\n\nCandidates:\nA. Subsequently, enzymatic hydrolysis of the polysaccharides-rich pretreated solid was optimised to give glucose and xylose by using the commercial enzymatic mixture Cellic® CTec4 HS (Novozymes, Bagsværd, Denmark) supplemented with endo-1,4-β-xylanase from Trichoderma viride (Merck, Darmstadt, Germany).\nB. The extraction process was not thoroughly optimised by acting on several parameters, such as the imidazole concentration, temperature, reaction time, and solid-to-liquid ratio.\nC. Subsequently, enzymatic hydrolysis of the polysaccharides-rich pretreated solid was optimised to give glucose and xylose by using the commercial enzymatic mixture Cellic® CTec3 HS (Novozymes, Bagsværd, Denmark) supplemented with endo-1,4-β-xylanase from Trichoderma viride (Merck, Darmstadt, Germany).\nD. The aim of this work was to design, develop, and optimise a two-step process for extracting lignin from Eucalyptus globulus residues and subsequently converting the remaining holocellulose into monosaccharides, primarily glucose and xylose.\nE. The extraction process was thoroughly optimised by acting on several parameters, such as the imidazole concentration, temperature, reaction time, and solid-to-liquid ratio.\nF. Firstly, the solubility of high-quality lignin was not optimised using a new hydro-organo-thermal method involving a diluted imidazole solution in water instead of pure imidazole.\nG. Firstly, the solubility of high-quality lignin was optimised using a new hydro-organo-thermal method involving a diluted imidazole solution in water instead of pure imidazole.\nH. The aim of this work was not to design, develop, and optimise a two-step process for extracting lignin from Eucalyptus globulus residues and subsequently converting the remaining holocellulose into monosaccharides, primarily glucose and xylose.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12995944", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12995944/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2ebca1a60f5b05a1168e", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nLignin is the most abundant renewable source of aromatic carbon on Earth and a central yet historically underutilized component of lignocellulosic biomass. Its complex and heterogeneous molecular architecture has long constrained efficient and selective conversion into value-added products, despite its high aromatic carbon content and chemical functionality. Recent advances in lignin extraction, fractionation, modification, and application-driven design have substantially expanded the range of achievable material and chemical performance within circular bioeconomy frameworks. This review provides a comprehensive and critically integrated assessment of lignin valorization that explicitly links plant biosynthesis and structural diversity to industrial convertibility, functional materials development, and sustainability performance. Green extraction technologies—including deep eutectic solvent and hydrotropic systems—are evaluated with respect to lignin structural quality, energy demand, solvent recovery, and downstream compatibility. Targeted chemical and enzymatic modification strategies enabling more reproducible lignin streams are discussed alongside applications in carbon fibers, nanomaterials, adhesives, bioplastics, cementitious systems, and additive manufacturing. Quantitative benchmarking against fossil-based incumbents identifies application domains where lignin-derived materials already achieve comparable performance, as well as areas where intrinsic structural limitations remain. In parallel, catalytic depolymerization pathways toward renewable aromatic chemicals are assessed from both mechanistic and systems-level perspectives. Environmental and economic implications are critically examined using recent life-cycle and techno-economic evidence, highlighting the influence of allocation choices, energy integration, and comparison with lignin incineration for energy recovery. Overall, this review clarifies how application-targeted lignin design and system-level sustainability assessment are essential for translating lignin’s biological complexity into scalable, competitive solutions for sustainable materials and chemicals.\n\nCandidates:\nA. Recent advances in lignin extraction, fractionation, modification, and application-driven design have substantially expanded the range of achievable material and chemical performance within circular bioeconomy frameworks.\nB. The evidence does not state that this review provides a comprehensive and critically integrated assessment of lignin valorization that explicitly links plant biosynthesis and structural diversity to industrial convertibility, functional materials development, and sustainability performance.\nC. The evidence does not state that recent advances in lignin extraction, fractionation, modification, and application-driven design have substantially expanded the range of achievable material and chemical performance within circular bioeconomy frameworks.\nD. This review provides a comprehensive and critically integrated assessment of lignin valorization that explicitly links plant biosynthesis and structural diversity to industrial convertibility, functional materials development, and sustainability performance.\nE. Lignin is not the most abundant renewable source of aromatic carbon on Earth and a central yet historically underutilized component of lignocellulosic biomass.\nF. The evidence does not state that its complex and heterogeneous molecular architecture has long constrained efficient and selective conversion into value-added products, despite its high aromatic carbon content and chemical functionality.\nG. Its complex and heterogeneous molecular architecture has long constrained efficient and selective conversion into value-added products, despite its high aromatic carbon content and chemical functionality.\nH. Lignin is the most abundant renewable source of aromatic carbon on Earth and a central yet historically underutilized component of lignocellulosic biomass.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12996219", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12996219/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fb173da62d658fc95140", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"72 h\", \"73 h\"]\n\nEvidence:\nAs the terminal management for evaluating the engineering effectiveness of antibiotics production and utilization, the toxic effects of moxifloxacin (MOX) and trace concentration of Cu 2+ (MOX-Cu) on Caenorhabditis elegans ( C. elegans ) were investigated at physiological, biochemical, and molecular level. Although the stimulate effects were observed after prolonged exposure (72 h) to MOX (0.2-2.0 mg/L), the expressions of HSPs, ace genes, and daf-16 were inhibited, indicating its adverse impact on cellular health, locomotion behaviors, and antioxidant defense of C. elegans . Similarly, the down-regulation of oxidative stress ( sod-1 and daf-16 ) and cell damage (HSPs) related genes and the up-regulation of apoptosis-related genes ( cep-1 and ape-1 ) indicated the oxidative stress and genotoxicity after prolonged exposure to MOX-Cu. For the chronic exposure (10 days) to MOX, the level of ROS was reduced due to the increased expressions of daf-16 , sod-3 , and hsp-16 , accompanied with and the down-regulation of cep-1 . Meanwhile, at the exposure to MOX-Cu, the levels of ROS and lipofuscin were decreased due to the up-regulation of sod-1 and daf-16 , and the antioxidant defense was promoted and confirmed by the increase of amino acids and their related metabolic pathways. These results can provide a theoretical basis for the toxicity evaluation of typical antibiotics (MOX) that co-existing with trace heavy metals in natural environment media and bioresources processes. The online version contains supplementary material available at 10.1186/s40643-026-01033-4. Keywords: Caenorhabditis elegans , Moxifloxacin, Copper, Antioxidant defenses, Metabolomics", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12996479", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12996479/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2530bc6a5f143c377ac3", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"19.0%\", \"-15.9%\", \"-14.9%\", \"8.6%\", \"18.0%\", \"-18.4%\", \"9.6%\", \"-19.4%\"]\n\nEvidence:\nFilamentous algae, characterized by high cellulose content and absence of lignin, present a promising sustainable alternative to conventional plant and synthetic fibers. The present study systematically evaluated the suitability of freshwater filamentous algae as a new resource for textile fibers, targeting applications in moisture-absorbent textiles. Among twelve strains screened, the isolate Rhizoclonium sp. emerged as the most promising candidate due to its high biomass yield (1.04 g dry weight L − 1 ) after 21 days of cultivation. In addition, it showed superior visible fiber flexibility following air-drying, an essential prerequisite for textile processing. Cultivation conditions were optimized (using WHM medium, pH 8, and thiamin supplementation) to maximize fiber quality, resulting in 8.6% increase in biomass productivity. Biochemical profiling of the optimized biomass revealed a significant enhancement of total carbohydrates (+ 18.0%), alongside reductions in protein (-18.4%) and ash content (-14.9%), supporting improved fiber durability and flexibility. Comparative FTIR analysis showed a strong cellulose signature and marked similarity to cotton, while also revealing high native starch content, further supporting their applicability as bio-based binders in nonwoven products. Functional characterization demonstrated that optimized Rhizoclonium sp. fibers exhibited exceptional moisture regain (~ 12%), surpassing conventional fibers such as cotton and lyocell. Overall, this study establishes native Rhizoclonium sp. as a highly versatile and renewable bioresource for innovative aquatic fibers, underpinning the development of an environmentally responsible algae-derived textile value chain. The online version contains supplementary material available at 10.1186/s40643-026-01028-1.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12996525", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12996525/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8d4d7d0e696ea9c34335", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMany Gram-negative nosocomial pathogens rely on adhesive filaments, known as archaic chaperone-usher pili, to establish stress- and drug-resistant, multi-layered biofilms. Here, we uncover the mechanism by which these pili build three-dimensional (3D) biofilm architectures. In situ analyses of Acinetobacter baumannii biofilms using electron microscopy (EM) reveal an extensive network of ultrathin, flat stacks of archaic Csu pili interconnecting bacterial cells in 3D space. Cryo-EM structures of a single native pilus, pilus pairs, and two types of multi-pilus stacks show that the pili pack into antiparallel sheets, with their rods connected laterally by junctions at their zigzag corners. This antiparallel arrangement ensures that contacts form primarily between pili from interacting cells rather than pili from the same cell. With a remarkably short helical repeat, archaic chaperone-usher pili spontaneously establish a high density of junctions that determines the biofilm’s 3D architecture. Our findings may help develop new therapies against multidrug-resistant bacterial infections by targeting pilus-pilus interactions. Subject terms: Biofilms, Cryoelectron microscopy, Bacterial secretion, Pathogens, Cryoelectron tomography\n\nCandidates:\nA. Many Gram-negative nosocomial pathogens rely on adhesive filaments, known as archaic chaperone-usher pili, to establish stress- and drug-resistant, multi-layered biofilms.\nB. Here, we uncover the mechanism by which these pili build three-dimensional (4D) biofilm architectures.\nC. Cryo-EM structures of a single native pilus, pilus pairs, and two types of multi-pilus stacks show that the pili pack into antiparallel sheets, with their rods connected laterally by junctions at their zigzag corners.\nD. The evidence does not state that many Gram-negative nosocomial pathogens rely on adhesive filaments, known as archaic chaperone-usher pili, to establish stress- and drug-resistant, multi-layered biofilms.\nE. The evidence does not state that cryo-EM structures of a single native pilus, pilus pairs, and two types of multi-pilus stacks show that the pili pack into antiparallel sheets, with their rods connected laterally by junctions at their zigzag corners.\nF. In situ analyses of Acinetobacter baumannii biofilms using electron microscopy (EM) reveal an extensive network of ultrathin, flat stacks of archaic Csu pili interconnecting bacterial cells in 3D space.\nG. In situ analyses of Acinetobacter baumannii biofilms using electron microscopy (EM) reveal an extensive network of ultrathin, flat stacks of archaic Csu pili interconnecting bacterial cells in 4D space.\nH. Here, we uncover the mechanism by which these pili build three-dimensional (3D) biofilm architectures.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12996565", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12996565/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f0cc5e579264d84a1468", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nWine production generates significant quantities of by-products each year, among which surplus grape must is notable for its high content of fermentable sugars, organic acids, and polyphenols. As wine consumption declines and grape must surpluses grow, identifying sustainable valorization strategies becomes increasingly critical. This study investigates for the first time the potential use of red and white grape musts as substrates for polyhydroxyalkanoates (PHAs) production by two well-characterized bacterial strains, Cupriavidus necator DSM 545 and Hydrogenophaga pseudoflava DSM 1034, under both batch and fed-batch fermentation regimes. Both musts supported microbial growth and PHAs accumulation. In batch cultures, C. necator DSM 545 achieved a PHB content of up to 61.5% of cell dry weight (CDW), while H. pseudoflava DSM 1034 reached 67.9% PHB on grape must, with yields and biomass comparable to or exceeding those obtained with synthetic sugar-based media. Under fed-batch conditions with red must, C. necator DSM 545 sustained growth and PHB production across multiple feeding feeding periods, outperforming the control medium. Conversely, H. pseudoflava DSM 1034 displayed initial growth but failed to increase biomass over time, suggesting that this strain may be poorly suited for use in fed-bacth-based applications, likely due to nutrient depletion or the accumulation of inhibitory compounds. Overall, these results proved that grape musts are promising feedstocks for sustainable PHAs production. Their integration into circular economy frameworks offers a valuable opportunity for waste recovery and the development of bioplastics within the agri-food industry, especially in light of the increasing grape must surpluses recently experienced worldwide.\n\nCandidates:\nA. The evidence does not state that both musts supported microbial growth and PHAs accumulation.\nB. Both musts supported microbial growth and PHAs accumulation.\nC. As wine consumption declines and grape must surpluses grow, identifying sustainable valorization strategies becomes increasingly critical.\nD. This study investigates for the first time the potential use of red and white grape musts as substrates for polyhydroxyalkanoates (PHAs) production by two well-characterized bacterial strains, Cupriavidus necator DSM 546 and Hydrogenophaga pseudoflava DSM 1034, under both batch and fed-batch fermentation regimes.\nE. Wine production generates significant quantities of by-products each year, among which surplus grape must is not notable for its high content of fermentable sugars, organic acids, and polyphenols.\nF. The evidence does not state that as wine consumption declines and grape must surpluses grow, identifying sustainable valorization strategies becomes increasingly critical.\nG. This study investigates for the first time the potential use of red and white grape musts as substrates for polyhydroxyalkanoates (PHAs) production by two well-characterized bacterial strains, Cupriavidus necator DSM 545 and Hydrogenophaga pseudoflava DSM 1034, under both batch and fed-batch fermentation regimes.\nH. Wine production generates significant quantities of by-products each year, among which surplus grape must is notable for its high content of fermentable sugars, organic acids, and polyphenols.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12996567", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12996567/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-61aadab30f7bde5390f2", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nHalophiles attract increasing attention to serve as sustainable industrial hosts for prolonged continuous processes or in open-vat fermentations owing to the reduced risk of contamination enabled by high salt concentration in the medium. Despite growing interest, their application in large-scale manufacturing remains limited partly due to bioprocessing challenges. A robust host that performs consistently well across scales should withstand variations in oxygen availability since local hypoxic regions often manifest in large-scale tanks even under strict operational control. In this work, we modified Halomonas sp. to achieve robust growth profiles in micro-aerobic environments by gradually exposing a continuous culture to stress induced by reduced oxygen availability using adaptive laboratory evolution. Nominal contamination was observed at 8.5% salt concentration even during non-aseptic operation, offering a suitable environment for prolonged continuous cultivation. Following adaptive laboratory evolution, Halomonas sp. achieved comparable growth at low-to-moderate (0.5%-3.0%) and moderate-to-high (3.0–5.0%) dissolved oxygen availability (OD 600 = 4.46 ± 0.29 & µ = 0.40 h − 1 and 4.96 ± 0.81 & µ = 0.35 h − 1 , respectively). Adaptive evolution improved the growth robustness of Halomonas sp. by 62% and its PHB production yield by 21%, while reducing ectoine production by 79% indicting improved metabolic perception of oxygen availability and reduced stress response. Mutations in cellular transport mechanisms and enzymatic pathways enabled this response as indicated by whole genome sequencing. The proposed framework is an effective strategy to reduce the oxygen sensitivity of microbial strains in general to improve their physiological properties, and to genetically design populations suitable for large scale bioprocess operations. The online version contains supplementary material available at 10.1186/s12934-026-02951-w. Keywords: Adaptive laboratory evolution, Halomonas sp., Dissolved oxygen, Growth robustness, Sodium chloride, Contamination risk", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12997722", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12997722/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b1bf23b08db8688d5c32", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nNatural killer cells play a critical role in innate immunity by targeting and eliminating cancerous and infected cells. The cell line NK-92, currently used in several clinical trials, has shown great potential in cancer immunotherapy due to strong cytotoxic capabilities and suitability as an off-the-shelf therapy. However, effective clinical application of NK-92 therapies requires optimised cell culture conditions, including cultivation medium composition and expansion methods. This study aimed at identifying optimal cultivation medium formulations and intensifying NK-92 cell expansion by systematically evaluating critical quality attributes and key performance indicators. We screened various cultivation media formulations, including alpha minimum essential medium, Roswell Park Memorial Institute medium, and stem cell growth medium, supplemented with either human serum, fetal bovine serum, chemically defined serum replacement, and a combination of fetal bovine serum and horse serum. Cells cultivated in alpha minimum essential medium supplemented with human serum significantly outperformed the other formulations, achieving the highest growth rate, maximum viable cell count, and superior cytotoxicity at high cost-efficiency. Media containing chemically defined serum replacement showed reduced cell proliferation despite high cytotoxicity per cell, indicating a complex balance between cell quantity and functionality. Following cultivation medium selection, cultivation strategies including batch, fed-batch, and repetitive batch were compared. The repetitive batch method, where cultivation medium is replenished repeatedly throughout the cultivation, demonstrated superior expansion capability, long-term cell viability, significantly improved cytotoxicity, although associated with higher medium consumption and thus costs. This study identified alpha minimum essential medium supplemented with human serum in repetitive batch cultivation as the optimal approach for NK-92 cell expansion. This combination enhances cell growth, viability, and cytotoxicity, meeting essential clinical quality attributes while maintaining cost-effectiveness. These findings provide valuable guidance and should act as a baseline for consistent, scalable, and commercially viable production of NK cell-based cancer therapies. The online version contains supplementary material available at 10.1186/s12896-026-01116-2. Keywords: NK-92 cells, Natural killer cells, Process mode, Cultivation medium optimisation, Process intensification, Batch, Fed-batch, Repetitive batch", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC12997954", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12997954/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e69317e775235e8515b2", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nKinetic modeling of microbial growth is essential for the design, optimization, and scale-up of industrial bioprocesses. Classical empirical models often lack biologically interpretable parameters or fail to capture complex multiphasic (polyauxic) behaviors, while fully mechanistic models are impractical for systems involving complex substrates and mixed cultures. This study proposes a unified mathematical framework that reformulates the canonical Boltzmann and Gompertz equations into semi-mechanistic forms, explicitly defining the maximum specific reaction rate and lag phase duration. Polyauxic growth is represented as a weighted sum of sigmoidal phases, subject to stringent constraints that ensure parameter identifiability, temporal consistency, and biological plausibility. The methodology integrates a workflow to address nonlinear regression in high-dimensional parameter spaces. A two-stage optimization strategy using Differential Evolution for global search followed by L-BFGS-B for local refinement avoid bias and heuristic parameter initialization. A Charbonnier loss function and the Robust Regression and Outlier Removal procedure are employed to identify and mitigate experimental outliers. Model parsimony is enforced using Akaike (AIC, AICc) and Bayesian (BIC) information criteria to select the optimal number of growth phases and avoid overparameterization. The framework was evaluated using experimental anaerobic digestion datasets, demonstrating that conventional single-phase models can obscure relevant metabolic transitions in co-digestion systems. Keywords: Anaerobic digestion, Microbiology, Bioreactor design, Sigmoid function, Microbial growth\n\nCandidates:\nA. Kinetic modeling of microbial growth is essential for the design, optimization, and scale-up of industrial bioprocesses.\nB. The evidence does not state that this study proposes a unified mathematical framework that reformulates the canonical Boltzmann and Gompertz equations into semi-mechanistic forms, explicitly defining the maximum specific reaction rate and lag phase duration.\nC. Polyauxic growth is not represented as a weighted sum of sigmoidal phases, subject to stringent constraints that ensure parameter identifiability, temporal consistency, and biological plausibility.\nD. Classical empirical models often lack biologically interpretable parameters or fail to capture complex multiphasic (polyauxic) behaviors, while fully mechanistic models are not impractical for systems involving complex substrates and mixed cultures.\nE. Classical empirical models often lack biologically interpretable parameters or fail to capture complex multiphasic (polyauxic) behaviors, while fully mechanistic models are impractical for systems involving complex substrates and mixed cultures.\nF. Polyauxic growth is represented as a weighted sum of sigmoidal phases, subject to stringent constraints that ensure parameter identifiability, temporal consistency, and biological plausibility.\nG. Kinetic modeling of microbial growth is not essential for the design, optimization, and scale-up of industrial bioprocesses.\nH. This study proposes a unified mathematical framework that reformulates the canonical Boltzmann and Gompertz equations into semi-mechanistic forms, explicitly defining the maximum specific reaction rate and lag phase duration.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12999704", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12999704/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-441cf7a71738c097b9e8", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHuman Bocavirus 2 (HBoV2) is an emerging enteric virus frequently detected in wastewater, yet its environmental fate, persistence, and treatment remain poorly understood. Improved characterization of HBoV2 is important for advancing wastewater-based epidemiology and understanding viral transmission dynamics. A year-long wastewater surveillance study was conducted from December 2023 to December 2024 in a small urban wastewater treatment system. Viral concentrations were quantified using digital PCR (dPCR), decay kinetics were assessed using controlled mesocosm experiments, and phylogenetic relationships were evaluated through NP1 gene sequencing. HBoV2 was detected in 93.8% of influent samples (average 4.76 log₁₀ genome copies per liter (GC/L) ) with seasonal peaks in summer and fall. Phylogenetic analysis revealed clustering with globally circulating HBoV2 lineages. Decay experiments indicated slow viral degradation (k = 0.10 d⁻¹), comparable to other non-enveloped DNA viruses including Adeno-Associated Virus type 2 (AAV-2), Adenovirus 41 (AdV41), and Human Polyomavirus (HPyV). Additionally, HBoV2 persisted through secondary treatment, with no significant reduction observed between influent and secondary effluent samples. Our findings indicate HBoV2 as highly prevalent and environmentally stable in a small urban city’s wastewater and that it is resistant to conventional treatment. These findings highlight HBoV2’s potential role in gastrointestinal infections worldwide and the need for further monitoring of this environmental contaminant. The online version contains supplementary material available at 10.1007/s11033-026-11669-2. Keywords: Human bocavirus, Wastewater treatment, Decay, Enteric viruses, Fecal contamination\n\nCandidates:\nA. Human Bocavirus 2 (HBoV2) is an emerging enteric virus frequently detected in wastewater, yet its environmental fate, persistence, and treatment remain poorly understood.\nB. A year-long wastewater surveillance study was conducted from December 2023 to December 2024 in a small urban wastewater treatment system.\nC. A year-long wastewater surveillance study was conducted from December 2024 to December 2024 in a small urban wastewater treatment system.\nD. Viral concentrations were quantified using digital PCR (dPCR), decay kinetics were assessed using controlled mesocosm experiments, and phylogenetic relationships were evaluated through NP1 gene sequencing.\nE. Human Bocavirus 3 (HBoV2) is an emerging enteric virus frequently detected in wastewater, yet its environmental fate, persistence, and treatment remain poorly understood.\nF. Viral concentrations were quantified using digital PCR (dPCR), decay kinetics were assessed using controlled mesocosm experiments, and phylogenetic relationships were evaluated through NP2 gene sequencing.\nG. Improved characterization of HBoV3 is important for advancing wastewater-based epidemiology and understanding viral transmission dynamics.\nH. Improved characterization of HBoV2 is important for advancing wastewater-based epidemiology and understanding viral transmission dynamics.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC12999748", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12999748/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aac5e576f75e21171802", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nVia efficient, scalable, and climate resilient processes, seaweed biorefineries can advance cleaner production, delivering nutrient-rich ingredients with relatively lower land and freshwater requirements. Here, a land-based Ulva ohnoi platform that integrates optimized outdoor tank cultivation, pH-shift protein extraction with dual-product valorization, scale-aware techno-economic analysis (TEA), and a climate-scenario emulator was developed and evaluated. Year-round cultivation yielded a pooled feedstock with carbohydrate and protein contents at 46.6% and 26.8%, respectively, and under optimal conditions, protein yield was 10.30% DW. Further, the protein content of cultivated Ulva protein (CUP) was 47.64%, with bound amino acids showing predominance (96%), implying food-grade utility. pH-shift extraction also improved in-vitro pepsin digestibility to 68.08%. The total dietary fiber content of CUP residue (CUPR) was 40.59%, with insoluble dietary fiber (31.59%) showing predominance, supporting the applicability of CUPR in improving gastrointestinal tract function. TEA revealed strong economies of scale for the platform. The 10,000-kg production model outperformed the 100- and 1,000-kg production models, with gross margin, Return On Investment (ROI), and Internal Rate of Return (IRR) at 78.56%, 110%, and 66.02%, respectively, and a payback time of only 0.91 years. Based on site-specific regression analysis (R 2 = 0.884), protein yield increased with temperature but decreased with rainfall, and further warming increased mean protein yield, while higher-emission pathways introduced rainfall-driven volatility, necessitating strategies for sustaining performance under climate variability. Overall, the use of the Ulva platform showed a broad growth temperature window as well as rapid acclimation for U. ohnoi , implying resilience even under global warming conditions and increasing weather variability. Keywords: Ulva ohnoi , Land-based seaweed cultivation, pH-shift protein extraction, Dual-product valorization, Techno-economic analysis, Climate resilience\n\nCandidates:\nA. Here, a land-based Ulva ohnoi platform that integrates optimized outdoor tank cultivation, pH-shift protein extraction with dual-product valorization, scale-aware techno-economic analysis (TEA), and a climate-scenario emulator was developed and evaluated.\nB. Here, a land-based Ulva ohnoi platform that integrates optimized outdoor tank cultivation, pH-shift protein extraction with dual-product valorization, scale-aware techno-economic analysis (TEA), and a climate-scenario emulator was not developed and evaluated.\nC. Via efficient, scalable, and climate resilient processes, seaweed biorefineries cannot advance cleaner production, delivering nutrient-rich ingredients with relatively lower land and freshwater requirements.\nD. Year-round cultivation yielded a pooled feedstock with carbohydrate and protein contents at 47.6% and 26.8%, respectively, and under optimal conditions, protein yield was 10.30% DW.\nE. Year-round cultivation yielded a pooled feedstock with carbohydrate and protein contents at 46.6% and 26.8%, respectively, and under optimal conditions, protein yield was 10.30% DW.\nF. Further, the protein content of cultivated Ulva protein (CUP) was 48.64%, with bound amino acids showing predominance (96%), implying food-grade utility.\nG. Via efficient, scalable, and climate resilient processes, seaweed biorefineries can advance cleaner production, delivering nutrient-rich ingredients with relatively lower land and freshwater requirements.\nH. Further, the protein content of cultivated Ulva protein (CUP) was 47.64%, with bound amino acids showing predominance (96%), implying food-grade utility.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13000019", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13000019/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6a42fe5cb4730874376c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMethanol, a renewable non-food C1 substrate, holds great promise as a feedstock for sustainable biomanufacturing and carbon neutral production. However, its industrial application is hindered by low methanol assimilation efficiency in most microbes. Recent advances in synthetic biology and metabolic engineering have enabled the development of methylotrophic microbial cell factories through strategies including building efficient methanol-utilizing pathways, engineering methanol dehydrogenase for enhanced oxidation efficiency, and optimizing redox balance via cofactor utilization. Additionally, approaches such as mitigating the accumulation of toxic metabolites and adaptive laboratory evolution have been adopted to improve the robustness of synthetic methylotrophs. This review summarizes these innovations and provides a blueprint for rationally designing high-performance microbial platforms to facilitate industrial methanol utilization and advance sustainable development.\n\nCandidates:\nA. Methanol, a renewable non-food C1 substrate, holds great promise as a feedstock for sustainable biomanufacturing and carbon neutral production.\nB. Additionally, approaches such as mitigating the accumulation of toxic metabolites and adaptive laboratory evolution have been adopted to improve the robustness of synthetic methylotrophs.\nC. Methanol, a renewable non-food C2 substrate, holds great promise as a feedstock for sustainable biomanufacturing and carbon neutral production.\nD. The evidence does not state that recent advances in synthetic biology and metabolic engineering have enabled the development of methylotrophic microbial cell factories through strategies including building efficient methanol-utilizing pathways, engineering methanol dehydrogenase for enhanced oxidation efficiency, and optimizing redox balance via cofactor utilization.\nE. However, its industrial application is not hindered by low methanol assimilation efficiency in most microbes.\nF. The evidence does not state that additionally, approaches such as mitigating the accumulation of toxic metabolites and adaptive laboratory evolution have been adopted to improve the robustness of synthetic methylotrophs.\nG. Recent advances in synthetic biology and metabolic engineering have enabled the development of methylotrophic microbial cell factories through strategies including building efficient methanol-utilizing pathways, engineering methanol dehydrogenase for enhanced oxidation efficiency, and optimizing redox balance via cofactor utilization.\nH. However, its industrial application is hindered by low methanol assimilation efficiency in most microbes.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13001265", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13001265/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8f2c00d2996710643a18", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe pharmaceutical industry remains critically dependent on plant-derived natural products, yet the supply of these complex molecules is perpetually threatened by the inherent biological instability of plant systems. For decades, the field has struggled to force undifferentiated plant cell cultures into the mold of consistent industrial fermentation, a strategy largely defeated by intrinsic biological stochasticity arising from epigenetic reprogramming, somaclonal variation, transcriptional noise, and systemic metabolic rigidity, as well as by a linear cost structure that prohibits pharmaceutical scalability. This literature-based review articulates a fundamental paradigm shift: the strategic decoupling of discovery from production. It argues that the genomic and epigenomic plasticity of plant cells—rather than being suppressed—should be deliberately induced and explored through stress elicitation to generate a “productive chaos” of chemical diversity for discovery. This expanded metabolic landscape is then decoded using single-cell–resolved multi-omics and spatial metabolomics to identify rare, elite producer states, alongside advanced artificial intelligence, molecular networking, and structure prediction to characterize novel bioactive candidates. Once identified, these biosynthetic pathways are functionally repatriated into defined, heterologous microbial hosts, engineered via systems-level metabolic and architectural optimization—including cofactor balancing, dynamic pathway control, subcellular compartmentalization, and cytochrome P450–reductase stoichiometry—to achieve stable, high-titer manufacturing. By integrating high-throughput discovery, AI-guided strain design, techno-economic analysis, and regulatory Quality-by-Design principles, this discovery–production decoupling resolves the long-standing tension between biological complexity and industrial rigor. This framework transforms the economics of natural product supply, transitioning from the low-CAPEX/high-OPEX trap of extraction to the high-CAPEX/low-OPEX scalability of fermentation, offering a scientifically grounded, commercially viable, and regulatorily tractable pathway to unlock the full therapeutic potential of the plant kingdom.\n\nCandidates:\nA. The pharmaceutical industry remains critically dependent on plant-derived natural products, yet the supply of these complex molecules is perpetually threatened by the inherent biological instability of plant systems.\nB. This literature-based review articulates a fundamental paradigm shift: the strategic decoupling of discovery from production.\nC. It argues that the genomic and epigenomic plasticity of plant cells—rather than being suppressed—should be deliberately induced and explored through stress elicitation to generate a “productive chaos” of chemical diversity for discovery.\nD. The pharmaceutical industry remains critically dependent on plant-derived natural products, yet the supply of these complex molecules is not perpetually threatened by the inherent biological instability of plant systems.\nE. For decades, the field has struggled to force undifferentiated plant cell cultures into the mold of consistent industrial fermentation, a strategy largely defeated by intrinsic biological stochasticity arising from epigenetic reprogramming, somaclonal variation, transcriptional noise, and systemic metabolic rigidity, as well as by a linear cost structure that prohibits pharmaceutical scalability.\nF. The evidence does not state that this literature-based review articulates a fundamental paradigm shift: the strategic decoupling of discovery from production.\nG. The evidence does not state that it argues that the genomic and epigenomic plasticity of plant cells—rather than being suppressed—should be deliberately induced and explored through stress elicitation to generate a “productive chaos” of chemical diversity for discovery.\nH. The evidence does not state that for decades, the field has struggled to force undifferentiated plant cell cultures into the mold of consistent industrial fermentation, a strategy largely defeated by intrinsic biological stochasticity arising from epigenetic reprogramming, somaclonal variation, transcriptional noise, and systemic metabolic rigidity, as well as by a linear cost structure that prohibits pharmaceutical scalability.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13002628", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13002628/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1dd035f70b6a5c5b86e1", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nShake flasks are widely used in early‐stage bioprocess development but are limited by their inability to monitor and control key gas‐transfer variables such as dissolved oxygen and carbon dioxide. In this study, we present a jacketed breathable flask system that enables real‐time gas control in a standard shaking environment. Across multiple media formulations and fill volumes, this system consistently deferred oxygen limitation and enhanced culture performance, achieving > 150% higher biomass and 140% greater recombinant protein yield compared to conventional flasks. Time‐resolved analysis of pH and extracellular metabolites revealed reduced accumulation of oxygen‐sensitive byproducts, including acetate, pyruvate, and succinate, indicating a shift toward more efficient respiratory metabolism. The jacketed breathable flask also enabled continuous monitoring and regulation of critical process parameters, creating a bioreactor‐like environment in a high‐throughput, low‐cost format. The biomass accumulation and specific growth rate observed in jacketed breathable flask are comparable to those reported for Escherichia coli cultures in stirred tank bioreactor application notes for Eppendorf BioBLU 3f. These findings establish breathable flasks as a scalable and accessible platform with bioreactor‐like performance for upstream process optimization and accelerate biomanufacturing development at the lab scale.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13003440", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13003440/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cf934f2c9de63c9aaa21", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation. While still in its infancy, Bayesian optimization has recently gained traction in bioprocess engineering. However, experimentation with biological systems is highly complex and the resulting experimental uncertainty requires specific extensions to classical Bayesian optimization. Moreover, current literature often targets readers with a strong statistical background, limiting its accessibility for practitioners. In light of these developments, this review has two aims: first, to provide an intuitive and practical introduction to Bayesian optimization; and second, to outline promising application areas and open algorithmic challenges, thereby highlighting opportunities for future research in machine learning.\n\nCandidates:\nA. However, experimentation with biological systems is highly complex and the resulting experimental uncertainty requires specific extensions to classical Bayesian optimization.\nB. The evidence does not state that moreover, current literature often targets readers with a strong statistical background, limiting its accessibility for practitioners.\nC. However, experimentation with biological systems is not highly complex and the resulting experimental uncertainty requires specific extensions to classical Bayesian optimization.\nD. While still in its infancy, Bayesian optimization has recently gained traction in bioprocess engineering.\nE. Moreover, current literature often targets readers with a strong statistical background, limiting its accessibility for practitioners.\nF. Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation.\nG. Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it cannot handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation.\nH. The evidence does not state that while still in its infancy, Bayesian optimization has recently gained traction in bioprocess engineering.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13003447", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13003447/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4ad555ce4cea3d98e063", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicroalgae are photosynthetic microorganisms capable of synthesizing diverse high-value bioactive compounds, including premium proteins, polyunsaturated fatty acids, pigments, and vitamins. These natural products exhibit significant potential in enhancing livestock growth and health, offering biological activity and nutritional benefits that surpass chemically synthesized alternatives. Nevertheless, the commercial production of microalgae-derived natural products remains insufficient to meet escalating market demands. Utilizing synthetic biology strategies, especially the CRISPR system, to increase productivity of microalgae cell factories is crucial for scaling up high-value product biosynthesis. This article reviews the current applications, construction strategies, and critical pathway nodes in microalgae cell factory, with emphasis on CRISPR-based genome editing breakthroughs for optimizing microalgae nutritional profiles, and recent progress in microalgae utilization for livestock production, providing a forward-looking perspective on future developments. Keywords: Biosynthesis, CRISPR, Feed, Genome editing, Microalgae, Sustainability\n\nCandidates:\nA. Utilizing synthetic biology strategies, especially the CRISPR system, to increase productivity of microalgae cell factories is not crucial for scaling up high-value product biosynthesis.\nB. These natural products exhibit significant potential in enhancing livestock growth and health, offering biological activity and nutritional benefits that surpass chemically synthesized alternatives.\nC. The evidence does not state that these natural products exhibit significant potential in enhancing livestock growth and health, offering biological activity and nutritional benefits that surpass chemically synthesized alternatives.\nD. Microalgae are photosynthetic microorganisms capable of synthesizing diverse high-value bioactive compounds, including premium proteins, polyunsaturated fatty acids, pigments, and vitamins.\nE. The evidence does not state that nevertheless, the commercial production of microalgae-derived natural products remains insufficient to meet escalating market demands.\nF. Utilizing synthetic biology strategies, especially the CRISPR system, to increase productivity of microalgae cell factories is crucial for scaling up high-value product biosynthesis.\nG. Microalgae are not photosynthetic microorganisms capable of synthesizing diverse high-value bioactive compounds, including premium proteins, polyunsaturated fatty acids, pigments, and vitamins.\nH. Nevertheless, the commercial production of microalgae-derived natural products remains insufficient to meet escalating market demands.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13003683", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13003683/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-109c5710fc82cecaaaca", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nFoetal bovine serum (FBS) has been widely used as a nutrient-rich supplement in mammalian cell culture for over 6 decades; however, its usage has increasingly raised various concerns and challenges related to quality variations, unethical collection practices, supply-demand imbalance and regulatory challenges. In recent years, alternatives have been investigated to reduce or replace FBS in mammalian cell culture. Starting from a comprehensive analysis of components of FBS and their functions in cell growth, this review compares the main types of FBS alternatives, i.e., human and animal-derived, plant-based alternatives and serum free media. Future perspectives discussed include the development of application-specific FBS alternatives, improvements in the quality and specialized formulation of FBS, optimization of existing alternatives and the establishment of databases and incentive mechanisms to facilitate the transition away from FBS. Lastly, the guidance for selecting appropriate FBS alternatives is also discussed.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13008496", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13008496/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ad59bc3337e26040c5dd", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"78.3%\", \"95%\", \"30%\", \"3.6%\", \"94%\", \"77.3%\", \"31%\", \"4.6%\"]\n\nEvidence:\nOsteosarcoma, the most aggressive primary malignant bone tumor, has stagnant therapeutic outcomes despite decades of standard MAP chemotherapy and surgery; 5-year overall survival (OS) is <30% for metastatic/recurrent cases. Plagued by genomic heterogeneity, immunosuppressive TME, and low immunogenicity, emerging immunotherapies lack robust large-scale clinical validation. We systematically analyzed 864 interventional osteosarcoma trials from Trialtrove (as of September 2025). Results showed trial numbers peaked at 54 in 2021, with 77.3% past (completed/terminated) and over 94% in phase I/II (only 3.6% phase III-IV). Geographically, the U.S. dominated (60.9%, focusing on immunotherapy/targeted therapy), while low- and middle-income countries (LMICs) accounted for <2% of trials despite bearing 40% of the global disease burden. Conventional chemotherapy remains the cornerstone, with immuno-oncology (540 trials) as the leading novel strategy; top targets include VEGFR2 (104), PD-1 (70), and mTOR (60). Biomarker use was imbalanced: liver/nutritional markers prevailed, while key immune/genomic biomarkers (CD8A, TP53) were underrepresented (<8% combined). Key challenges include severe trial-phase imbalance, global disparities, and preclinical-clinical gaps; opportunities lie in synergistic novel therapies (ICI combinations, GD2-targeted CAR-T) and decentralized clinical trials (DCT). Future priorities: accelerate late-phase trials for promising regimens, reduce global disparities via regional consortia, integrate precision biomarkers for patient stratification, and translate TME insights into trials. This analysis highlights the need to shift from conventional chemotherapy optimization to precision-driven, globally equitable strategies to improve outcomes for high-risk osteosarcoma patients.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13008900", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13008900/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-04000efe3de2ede9ac71", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nRNA therapeutics are expanding rapidly, driving demand for manufacturing processes that can keep pace with clinical translation. Because mRNA yield and impurity profiles are jointly influenced by upstream plasmid DNA (pDNA) preparation and the in vitro transcription (IVT) reaction, in-process measurements are increasingly applied across the product lifecycle, albeit with distinct objectives in process development versus current good manufacturing practice (cGMP) production. Within the U.S. Food and Drug Administration’s Process Analytical Technology framework, we categorize in-process analytical methods by control intent: (1) measurements that build process understanding and define operating windows; (2) in-process controls (IPCs) that support predefined stop/go, forward-processing, and endpoint decisions; and (3) measurements that could enable advanced or adaptive process control (APC) through closed-loop feedback. We discuss how each category is deployed during process development and in cGMP manufacturing. Following the workflow from pDNA preparation through IVT, we highlight analytical measurements that establish template readiness—such as plasmid topology, linearization completeness, and co-purifying impurities that can propagate into transcription performance and complicate downstream processing—as well as time-resolved measurements during IVT that track reactant consumption and product formation to inform endpoint selection, feed timing, and deviation triage under predefined decision rules. We compare the strengths, implementation constraints, and validation considerations of at-line, on-line, and in-line approaches, and identify key gaps that currently limit broader adoption, including practical time-resolved quantification of double-stranded RNA and the availability of production-ready in-line sensing technologies. Collectively, these in-process analytics deliver near-term value by enabling process understanding and IPC-based decision support, while establishing the foundation required for future APC in mRNA manufacturing.\n\nCandidates:\nA. Food and Drug Administration’s Process Analytical Technology framework, we categorize in-process analytical methods by control intent: (1) measurements that build process understanding and define operating windows; (2) in-process controls (IPCs) that support predefined stop/go, forward-processing, and endpoint decisions; and (3) measurements that could enable advanced or adaptive process control (APC) through closed-loop feedback.\nB. Food and Drug Administration’s Process Analytical Technology framework, we categorize in-process analytical methods by control intent: (2) measurements that build process understanding and define operating windows; (2) in-process controls (IPCs) that support predefined stop/go, forward-processing, and endpoint decisions; and (3) measurements that could enable advanced or adaptive process control (APC) through closed-loop feedback.\nC. RNA therapeutics are expanding rapidly, driving demand for manufacturing processes that can keep pace with clinical translation.\nD. We discuss how each category is deployed during process development and in cGMP manufacturing.\nE. We discuss how each category is not deployed during process development and in cGMP manufacturing.\nF. RNA therapeutics are not expanding rapidly, driving demand for manufacturing processes that can keep pace with clinical translation.\nG. Because mRNA yield and impurity profiles are jointly influenced by upstream plasmid DNA (pDNA) preparation and the in vitro transcription (IVT) reaction, in-process measurements are increasingly applied across the product lifecycle, albeit with distinct objectives in process development versus current good manufacturing practice (cGMP) production.\nH. Because mRNA yield and impurity profiles are not jointly influenced by upstream plasmid DNA (pDNA) preparation and the in vitro transcription (IVT) reaction, in-process measurements are increasingly applied across the product lifecycle, albeit with distinct objectives in process development versus current good manufacturing practice (cGMP) production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13013469", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13013469/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6f95a98092617257f41b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDNA polymerase θ (Pol θ)‐mediated end‐joining (TMEJ), one of several pathways for repairing DNA double‐strand breaks, is traditionally thought to initiate via anchoring at short, consecutive, and perfectly matched microhomologies (MHs). Emerging evidence indicates that Pol θ can utilize MHs containing mismatches both in vitro and in vivo. This revised definition of MH provides a mechanistic explanation for a broader spectrum of Pol θ‐dependent repair outcomes. Here, we summarize recent findings on the revised definition of MHs utilized by Pol θ, assess the applicability of this concept across species, and compare TMEJ with other (micro)hom(e)ology‐mediated repair pathways. We explore how mismatch‐containing MHs expand Pol θ‐associated mutational signatures and provide a framework for future studies on Pol θ’s role in DNA repair and cancer biology. Keywords: DNA polymerase θ (Pol θ), microhomology, mismatches, mutational signatures, TMEJ\n\nCandidates:\nA. Here, we summarize recent findings on the revised definition of MHs utilized by Pol θ, assess the applicability of this concept across species, and compare TMEJ with other (micro)hom(e)ology‐mediated repair pathways.\nB. The evidence does not state that this revised definition of MH provides a mechanistic explanation for a broader spectrum of Pol θ‐dependent repair outcomes.\nC. DNA polymerase θ (Pol θ)‐mediated end‐joining (TMEJ), one of several pathways for repairing DNA double‐strand breaks, is not traditionally thought to initiate via anchoring at short, consecutive, and perfectly matched microhomologies (MHs).\nD. The evidence does not state that here, we summarize recent findings on the revised definition of MHs utilized by Pol θ, assess the applicability of this concept across species, and compare TMEJ with other (micro)hom(e)ology‐mediated repair pathways.\nE. Emerging evidence indicates that Pol θ cannot utilize MHs containing mismatches both in vitro and in vivo.\nF. This revised definition of MH provides a mechanistic explanation for a broader spectrum of Pol θ‐dependent repair outcomes.\nG. Emerging evidence indicates that Pol θ can utilize MHs containing mismatches both in vitro and in vivo.\nH. DNA polymerase θ (Pol θ)‐mediated end‐joining (TMEJ), one of several pathways for repairing DNA double‐strand breaks, is traditionally thought to initiate via anchoring at short, consecutive, and perfectly matched microhomologies (MHs).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13015775", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13015775/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e63223938a1560297118", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMicroalgae have emerged as a versatile biotechnological platform, promising to become an efficient source of commodities such as food, feed, and biofuels, but these organisms have also sparked profound scientific and commercial interest for their potential in producing high-value recombinant proteins. Recombinant gene expression is highly dependent on the loci in which the transgene integrates, and in green algae transgenes integrate randomly into the nuclear genome, mostly through non homologous end joining. Therefore, recombinant gene expression varies greatly among different algal transformants and many algal colonies must be screened before a suitable production strain can be found, which can be quite laborious and become a bottleneck in the recombinant strain production pipeline. Here we describe a method for mid-throughput screening of recombinant protein expression; the Microalgal Colony Blot. This screening method allows for the detection of recombinant protein expression in up to 100 algal colonies per petri dish, with each petri dish preparation taking only 20 minutes. A nitrocellulose membrane is layered on top of a petri dish containing agar media, and algal cells are inoculated on top of the filter in a liquid suspension using a micropipette. The colonies are allowed to grow for up to 7 days, with the colonies secreting recombinant protein (either through active secretion or through cell lysis) as they grow with the recombinant protein being immediately bound by the nitrocellulose membrane. After the incubation period, the membrane is treated like a regular western blot, with blocking, washing, antibody binding and visualization. In this manner, up to 1000 colonies can be comfortably screened per day by a single person. Knowing that in C. reinhardtii only about 5% of the transgenic colonies from a transformation produce significant recombinant protein expression, being able to screen 1000 colonies ensures that around 50 suitable candidates will be identified within a single day.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13016312", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13016312/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9fdb6c5ab1c50aa7de8d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\n2,3-Butanediol (2,3-BDO) is a versatile platform chemical with diverse applications in cosmetics, pharmaceuticals, agricultural and food manufacturing. Among its stereoisomers, optically pure ( meso )-2,3-BDO is particularly valuable; however, achieving high titers with stereoselectivity remains challenging in conventional hosts due to byproduct formation, low tolerance, and plasmid instability. In this study, we established Corynebacterium glutamicum as an efficient and robust chassis for the industrial-level production of optically pure ( meso )-2,3-BDO. A structure-guided engineering approach was applied to 2,3-butanediol dehydrogenase (KpBDH), where α6-helix truncation enhanced catalytic efficiency and enabled near-complete conversion of acetoin to the target isomer. To further improve productivity, competing byproduct pathways were deleted, and cofactor homeostasis was reinforced by integrating UdhA for NADH regeneration and DrPPK for ATP regeneration. Finally, all biosynthetic modules were stably integrated into the chromosome, generating the plasmid-free strain ES11. In 5L fed-batch fermentation, ES11 produced 100.4 ± 0.4 g/L ( meso )-2,3-BDO with > 99% optical purity, a yield of 0.33 ± 0.04 g/g glucose, and productivity of 0.82 ± 0.06 g/L/h. This work represents the first demonstration of > 100 g/L optically pure ( meso )-2,3-BDO using C. glutamicum and establishes an integrated strategy of enzyme engineering, pathway optimization, and process design. The online version contains supplementary material available at 10.1186/s40643-026-01025-4. Keywords: Corynebacterium glutamicum ; ( meso )-2,3-butanediol; Metabolic engineering; Enzyme engineering; Plasmid-free biomanufacturing\n\nCandidates:\nA. A structure-guided engineering approach was applied to 3,3-butanediol dehydrogenase (KpBDH), where α6-helix truncation enhanced catalytic efficiency and enabled near-complete conversion of acetoin to the target isomer.\nB. 2,3-Butanediol (2,3-BDO) is a versatile platform chemical with diverse applications in cosmetics, pharmaceuticals, agricultural and food manufacturing.\nC. In this study, we established Corynebacterium glutamicum as an efficient and robust chassis for the industrial-level production of optically pure ( meso )-3,3-BDO.\nD. A structure-guided engineering approach was applied to 2,3-butanediol dehydrogenase (KpBDH), where α6-helix truncation enhanced catalytic efficiency and enabled near-complete conversion of acetoin to the target isomer.\nE. Among its stereoisomers, optically pure ( meso )-3,3-BDO is particularly valuable; however, achieving high titers with stereoselectivity remains challenging in conventional hosts due to byproduct formation, low tolerance, and plasmid instability.\nF. Among its stereoisomers, optically pure ( meso )-2,3-BDO is particularly valuable; however, achieving high titers with stereoselectivity remains challenging in conventional hosts due to byproduct formation, low tolerance, and plasmid instability.\nG. 3,3-Butanediol (2,3-BDO) is a versatile platform chemical with diverse applications in cosmetics, pharmaceuticals, agricultural and food manufacturing.\nH. In this study, we established Corynebacterium glutamicum as an efficient and robust chassis for the industrial-level production of optically pure ( meso )-2,3-BDO.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13018514", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13018514/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4b14c05a757062a7e0be", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"50%\", \"41 °C\", \"101%\", \"40 °C\", \"67%\", \"68%\", \"51%\", \"100%\"]\n\nEvidence:\nThe enzymatic esterification of 5-hydroxymethylfurfural (HMF) with long-chain fatty acids offers a sustainable route for producing biolubricants and other high-value chemicals. This work evaluates the synthesis of 5-hidroxymethylfurfural stearate catalyzed by immobilized lipases in both batch and continuous packed-bed bioreactors, combining molecular dynamics (MD) simulations with experimental validation to identify suitable green solvents. Four solvents were tested: 2-methyl-3-buten-2-ol (2-MB), tert-butanol (TB), 2-methyltetrahydrofuran (2-MeTHF), and cyclopentyl methyl ether (CPME). MD simulations revealed that CPME increased hydrophobic surface exposure and flexibility near the catalytic site, favoring substrate accessibility. In preliminary experimental tests, CPME provided the highest conversion (50%). In batch bioreactor at 40 °C, 30 mM HMF and 250 mM stearic acid achieved 67% conversion with Candida antarctica lipase B (CALB), maintaining full activity (100%) over four reuse cycles. In continuous operation, using a single packed-bed bioreactor at 0.02 mL min⁻¹ yielded conversions above 50% (residence time ≈ 55 min), while connecting two packed-bed bioreactors in series increased conversion to over 90% and productivity to 0.094 h⁻¹, compared with 0.076 h⁻¹ for one column and 0.003 h⁻¹ in batch mode. Deviations from ideal plug flow were observed over time, attributed to substrate or product deposition and in-situ water formation shifting the reaction equilibrium. Overall, CPME proved to be an efficient and sustainable solvent for the enzymatic synthesis of 5-hydroxymethylfurfural stearate, demonstrating the feasibility of continuous operation and highlighting pathways for further optimization through improved immobilization or reactor design. The online version contains supplementary material available at 10.1186/s40643-026-01036-1.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13018518", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13018518/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e0d355941cc974b0dd9d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGut microbiota regulation is a key strategy for treating metabolic dysfunction-associated fatty liver disease (MAFLD). Arbutin (ARB) is a natural hydroquinone active agent with anti-inflammatory and antioxidant effects, as well as regulatory effects on the gut microbiota. However, its therapeutic effect on MAFLD and the responsible mechanisms remain unclear. This study explored the therapeutic effect and mechanisms of ARB in MAFLD treatment. High-fat diet (HFD)-fed mice served as the in vivo MAFLD model, and ARB treatment was given simultaneously. The extent of liver injury was assessed through histopathological staining. AML12 cells treated with free fatty acids served as the in vitro model. The effects of ARB were evaluated via oil red O staining and biochemical assays. Subsequently, we utilized bioinformatics techniques to predict the potential mechanisms and targets of ARB. The expression of liver apoptosis-related genes was detected using molecular biology techniques. Alterations in the gut microbiota were analyzed by 16S rRNA sequencing. Ultrahigh-performance liquid chromatography–high-resolution mass spectrometry was used to analyze the changes in fecal metabolite levels. ARB treatment effectively improved liver injury in mice with MAFLD. Its mechanism was associated with anti-apoptotic effects mediated by signal transducer and activator of transcription 3. Meanwhile, ARB effectively reversed gut microbiota imbalance in mice with MAFLD and altered the composition of gut microbes and fecal metabolites. ARB displayed potential effects in alleviating the pathology of MAFLD, exerting anti-apoptotic actions, and restoring the gut microbiota balance. The online version contains supplementary material available at 10.1186/s40643-026-01032-5.\n\nCandidates:\nA. The evidence does not state that this study explored the therapeutic effect and mechanisms of ARB in MAFLD treatment.\nB. This study explored the therapeutic effect and mechanisms of ARB in MAFLD treatment.\nC. The evidence does not state that however, its therapeutic effect on MAFLD and the responsible mechanisms remain unclear.\nD. Gut microbiota regulation is not a key strategy for treating metabolic dysfunction-associated fatty liver disease (MAFLD).\nE. Gut microbiota regulation is a key strategy for treating metabolic dysfunction-associated fatty liver disease (MAFLD).\nF. Arbutin (ARB) is not a natural hydroquinone active agent with anti-inflammatory and antioxidant effects, as well as regulatory effects on the gut microbiota.\nG. However, its therapeutic effect on MAFLD and the responsible mechanisms remain unclear.\nH. Arbutin (ARB) is a natural hydroquinone active agent with anti-inflammatory and antioxidant effects, as well as regulatory effects on the gut microbiota.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13018519", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13018519/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8a6ea6be962d4dec533a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe global demand for probiotics has been increasing over the past few decades. Of these, Saccharomyces cerevisiae has attracted growing interest for use in functional foods and feed supplements due to its probiotic potential, nutritional value, and well-documented safety. For industrial applications, functional characterization, safety assessment, and robust production processes are key prerequisites. This study evaluated the probiotic properties and production potential of the S. cerevisiae strain TBRC 3616, isolated from decaying leaves in a tropical ecosystem in Thailand. The strain exhibited key probiotic traits, including acid and bile salt tolerance, Caco-2 cell adhesion, antipathogen activity, antioxidant capacity, and enzyme activities (catalase, protease, and esterase). The yeast exhibited no hemolytic activity and was not susceptible to the tested antibiotics, except colistin at 50 μg. High-cell-density cultivation was achieved using the developed fed-batch fermentation, resulting in a high yeast titer of 12.14 ± 0.03 log CFU L –1 , and a cell production rate of 10.52 ± 0.02 log CFU L –1 h –1 . Furthermore, downstream processing efficiency was markedly enhanced by implementing an optimized freeze-drying protocol using 5% (w/v) maltodextrin, resulting in a 4-fold increase in cell viability compared to the control. These findings provide a production framework that supports the potential for scale-up of S. cerevisiae TBRC 3616 as a probiotic yeast for future applications.\n\nCandidates:\nA. For industrial applications, functional characterization, safety assessment, and robust production processes are not key prerequisites.\nB. The evidence does not state that of these, Saccharomyces cerevisiae has attracted growing interest for use in functional foods and feed supplements due to its probiotic potential, nutritional value, and well-documented safety.\nC. The evidence does not state that the global demand for probiotics has been increasing over the past few decades.\nD. The evidence does not state that this study evaluated the probiotic properties and production potential of the S.\nE. For industrial applications, functional characterization, safety assessment, and robust production processes are key prerequisites.\nF. The global demand for probiotics has been increasing over the past few decades.\nG. This study evaluated the probiotic properties and production potential of the S.\nH. Of these, Saccharomyces cerevisiae has attracted growing interest for use in functional foods and feed supplements due to its probiotic potential, nutritional value, and well-documented safety.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13019213", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13019213/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-21441f3995edfe176504", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nManufacturing cell and gene therapies (CGTs) at scale presents challenges in cost, product consistency, and adaptability to personalized treatments. Traditional large-volume bioreactors are designed to support cell growth through controlled nutrient delivery and gas exchange, but are poorly suited to the decentralized, small-batch production required for personalized therapies such as chimeric antigen receptor (CAR) T cells. To address this, we have developed the KCL-Microbioreactor (K-MBR), a closed microbioreactor platform based on microfluidic principles. Engineered in polydimethylsiloxane (PDMS), the K-MBR combines spatial confinement, semi-continuous perfusion, and integrated viral transduction in a compact footprint, enabling efficient gene delivery and robust expansion of therapeutic cells. We demonstrate the platform’s utility by generating functional CAR-Tregs targeting HLA-A2, achieving a 92% increase in yield compared to conventional methods. The K-MBR offers a streamlined solution for CGT manufacturing, with potential to reduce production costs and enhance scalability across a broad range of cell therapies. Subject areas: Biological sciences\n\nCandidates:\nA. Traditional large-volume bioreactors are designed to support cell growth through controlled nutrient delivery and gas exchange, but are poorly suited to the decentralized, small-batch production required for personalized therapies such as chimeric antigen receptor (CAR) T cells.\nB. The evidence does not state that to address this, we have developed the KCL-Microbioreactor (K-MBR), a closed microbioreactor platform based on microfluidic principles.\nC. Traditional large-volume bioreactors are not designed to support cell growth through controlled nutrient delivery and gas exchange, but are poorly suited to the decentralized, small-batch production required for personalized therapies such as chimeric antigen receptor (CAR) T cells.\nD. Engineered in polydimethylsiloxane (PDMS), the K-MBR combines spatial confinement, semi-continuous perfusion, and integrated viral transduction in a compact footprint, enabling efficient gene delivery and robust expansion of therapeutic cells.\nE. The evidence does not state that engineered in polydimethylsiloxane (PDMS), the K-MBR combines spatial confinement, semi-continuous perfusion, and integrated viral transduction in a compact footprint, enabling efficient gene delivery and robust expansion of therapeutic cells.\nF. Manufacturing cell and gene therapies (CGTs) at scale presents challenges in cost, product consistency, and adaptability to personalized treatments.\nG. To address this, we have developed the KCL-Microbioreactor (K-MBR), a closed microbioreactor platform based on microfluidic principles.\nH. The evidence does not state that manufacturing cell and gene therapies (CGTs) at scale presents challenges in cost, product consistency, and adaptability to personalized treatments.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13019498", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13019498/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0b1c8793d5d129a981e2", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHigh-yielding recombinant protein expression systems often face challenges due to the metabolic burden caused by the competition for cellular resources, resulting in reduced growth and, hence, limiting their industrial applicability. Furthermore, industrial recombinant protein production is also affected by the occurrence of oxygen gradients, which is a prevalent issue in large-scale bioreactors. These gradients create a heterogeneous environment in the bioreactor, which affects cell growth and metabolism, having severe consequences on the process performance. Both these factors alter cellular physiology and metabolism, thereby affecting recombinant protein yields. Understanding metabolic adaptations to these stress conditions is crucial for uncovering the underlying cellular mechanisms, which can direct further optimization of the recombinant strains. In this study, we aimed to explore the combined response of the central metabolism of Escherichia coli to metabolic burden and microaerobic conditions. Two recombinant protein-producing E. coli BL21 strains carrying XylS/ Pm vectors with low (A2-mCh) and medium plasmid copy numbers (A3-mCh), and producing mCherry protein, were studied by introducing oxygen limitation. Central metabolite pools were analyzed by three targeted LC–MS/MS methods, using the isotope dilution strategy for absolute quantification. Both recombinant strains exhibited different levels of metabolic burden, with the strain possessing a higher plasmid copy number showing more pronounced growth retardation and a stronger impact on metabolite pools. Both strains, however, showed a similar response to oxygen limitation, with significant adaptations in the central metabolite pools. The low plasmid copy number strain showed an increase in the concentration of lower glycolytic and tricarboxylic acid cycle metabolites, while the pools of upper glycolytic and pentose phosphate pathways and nucleoside phosphates were mostly unaffected. However, a more extreme response was seen in A3-mCh, where the majority of the metabolite pools were increased. Oxygen limitation caused lower metabolic activity, but the energy charge and redox balance were maintained, and no negative effect was observed on mCherry production rates. This study provides insights into metabolic adaptations in E. coli BL21 recombinant strains, having quite robust mechanisms to maintain intracellular metabolic homeostasis during both internal and external perturbations. The online version contains supplementary material available at 10.1186/s12934-026-02924-z.\n\nCandidates:\nA. Both these factors alter cellular physiology and metabolism, thereby affecting recombinant protein yields.\nB. Furthermore, industrial recombinant protein production is also affected by the occurrence of oxygen gradients, which is a prevalent issue in large-scale bioreactors.\nC. High-yielding recombinant protein expression systems often face challenges due to the metabolic burden caused by the competition for cellular resources, resulting in reduced growth and, hence, limiting their industrial applicability.\nD. The evidence does not state that both these factors alter cellular physiology and metabolism, thereby affecting recombinant protein yields.\nE. These gradients create a heterogeneous environment in the bioreactor, which affects cell growth and metabolism, having severe consequences on the process performance.\nF. The evidence does not state that high-yielding recombinant protein expression systems often face challenges due to the metabolic burden caused by the competition for cellular resources, resulting in reduced growth and, hence, limiting their industrial applicability.\nG. Furthermore, industrial recombinant protein production is not also affected by the occurrence of oxygen gradients, which is a prevalent issue in large-scale bioreactors.\nH. The evidence does not state that these gradients create a heterogeneous environment in the bioreactor, which affects cell growth and metabolism, having severe consequences on the process performance.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13020385", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13020385/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-eee0a277b049329c2e19", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"50 °C\", \"75%\", \"51 °C\", \"76%\", \"4 °C\", \"7 h\", \"8 h\", \"5 °C\"]\n\nEvidence:\nAgricultural residues like sugarcane bagasse and rice straw are rich in cellulose and xylan. Their efficient conversion into (oligo)saccharides and value-added products requires microbial cellulases and xylanases, but low enzyme yields and high production costs hinder industrial application. This study isolated Penicillium oxalicum UNN1, a high-xylanase-producing strain with an initial activity of 51.63 U/mL. Submerged fermentation conditions were optimized using different carbon/nitrogen sources to enhance enzyme production. The optimized xylanase activity reached 191.22 U/mL (sugarcane bagasse xylan as sole carbon source) and 142.32 U/mL (combined with Avicel), with filter paper cellulase activity of 0.76 U/mL. The crude enzymes exhibited optimal activity at pH 5.0 and 50 °C. Cellulase retained over 75% activity after 7 h at pH 4.0–6.0 (4 °C) or 40 °C (pH 5.0), while xylanase activity remained nearly unchanged, even after over 21 days of storage at 4 °C (pH 5.0). However, the half-life of xylanase was less than 1 h at 50 °C, though it exceeded 72 h at 40 °C (pH 5.5). 3–5 mM Ca²⁺ and Cu²⁺ strongly inhibited both enzymes. Crude enzyme addition (about 7 U cellulase and 1,400 U xylanase) effectively enhanced reducing sugar production from agricultural residues. Single-factor and response surface optimization yielded optimal hydrolysis conditions: 480 U/g sugarcane bagasse xylan of xylanase, hydrolysate pH of 5.5, hydrolysis temperature of 40 °C, achieving a maximum reducing sugar yield of 0.355 g/g dry biomass. This work demonstrates the potential of P. oxalicum UNN1 enzymes for efficient and stable saccharification of agricultural residues, offering a viable approach for their valorization and environmental management. The online version contains supplementary material available at 10.1186/s40643-026-01035-2. Keywords: Penicillium oxalicum , Submerged fermentation, Cellulase, Xylanase, Optimization, Hydrolysis", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13022107", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13022107/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cf0600a31d0030aabc01", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"2.8 nm\", \"1.8 nm\", \"21–40%\", \"20–40%\"]\n\nEvidence:\nAllergic rhinitis (AR) is a prevalent inflammatory disorder of the upper respiratory tract, affecting 20–40% of the global population and severely impairing quality of life. Given the limitations and adverse effects associated with conventional pharmacotherapy, naturally derived bioactives with low toxicity are gaining prominence as alternative interventions. In this study, we developed a bioresource-based nanoemulsion (NE) by integrating Sargassum polysaccharides (SP) into algal oil (AO) to enhance intranasal delivery and therapeutic efficacy against AR. Structural analysis confirmed that SP comprised sulfated polysaccharides enriched in fucose, glucose, and galactose. The optimized SP–AO NE, formulated with Tween 80 and prepared via ultrasonic emulsification, exhibited uniform spherical droplets (53.4 ± 1.8 nm), a low polydispersity index (0.3 ± 0.1), and a negative zeta potential (− 29.1 ± 2.8 mV), indicating high colloidal stability and effective oxidative protection of AO during refrigerated storage. In an ovalbumin-induced AR mouse model, intranasal administration of SP–AO NE significantly alleviated nasal rubbing, epithelial hypertrophy, goblet cell hyperplasia, mast cell infiltration, and pulmonary inflammation. Intranasal SP–AO NE treatment decreased IgE levels in serum, nasal lavage, and bronchoalveolar lavage fluids, while enhancing mucosal IgA. In addition, SP–AO NE downregulated IL-4 and TNF-α expression and upregulated TGF-β1, demonstrating a robust immunomodulatory effect. Overall, this work presents a stable, biocompatible, and functional NE that improves intranasal delivery of algal bioactives, offering a promising natural therapeutic strategy for the management of AR.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13022132", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13022132/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f9d65da55e02809ad5dc", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe aqueous phase (AP) generated during biomass pyrolysis is often considered a waste product due to its dilute and toxic nature, making it difficult to upgrade. This study explores the potential of using AP as a laccase inducer for the white-rot fungus, Pleorotus ostreatus , and as a mediator in laccase-catalyzed reactions. As an inducer, AP increased laccase production from P. ostreatus to 570 U/g, outperforming copper, a common inducer, by almost 180%. A maximum laccase yield of 955 U/g was achieved when P. ostreatus was co-induced by both AP and copper. Characterization of the AP-induced laccase revealed greater pH tolerance relative of this enzyme compared to copper-induced laccase. The AP-induced laccase was further evaluated for various applications. Laccase alone was effective in decolorizing coomassie blue dye, increasing saccharification yield from prairie biomass, and detoxifying tetracycline. When laccase was mediated with AP, the enzyme was also capable of decolorizing crystal violet dye, demonstrating additional benefit of AP to mediate laccase-based oxidation reactions with certain substrates. Overall, these findings suggest that using AP to induce laccase production, and potentially mediate the laccase-based reactions, could be a promising method to valorize this byproduct from biomass pyrolysis.\n\nCandidates:\nA. This study explores the potential of using AP as a laccase inducer for the white-rot fungus, Pleorotus ostreatus , and as a mediator in laccase-catalyzed reactions.\nB. The aqueous phase (AP) generated during biomass pyrolysis is not often considered a waste product due to its dilute and toxic nature, making it difficult to upgrade.\nC. ostreatus to 570 U/g, outperforming copper, a common inducer, by almost 180%.\nD. The evidence does not state that as an inducer, AP increased laccase production from P.\nE. The aqueous phase (AP) generated during biomass pyrolysis is often considered a waste product due to its dilute and toxic nature, making it difficult to upgrade.\nF. ostreatus to 571 U/g, outperforming copper, a common inducer, by almost 180%.\nG. As an inducer, AP increased laccase production from P.\nH. The evidence does not state that this study explores the potential of using AP as a laccase inducer for the white-rot fungus, Pleorotus ostreatus , and as a mediator in laccase-catalyzed reactions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13022153", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13022153/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8a2f9e6b4f095a6e3f54", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe genus Ligustrum (Oleaceae) encompasses woody plants with both medicinal and edible uses, distinguished by a wide range of bioactive compounds, including triterpenoids, phenylethanoid glycosides, flavonoids, and other active constituents. These metabolites demonstrate multi-target pharmacological effects, such as anti-inflammatory, antioxidant, antitumor, and anti-osteoporotic activities. In traditional medicine, species like L. lucidum and L. robustum are well-documented for their therapeutic roles in nourishing the liver and kidneys, enhancing vision, darkening hair, and serving as functional tea ingredients. Beyond their medicinal and health-promoting properties, Ligustrum species are also employed as ornamental plants, bioindicators of atmospheric pollution, algicidal agents, and feed additives. Given the increasing global demand and underutilization of Ligustrum resources, there is an urgent need to establish a sustainable supply framework focused on alternative strategies such as metabolic engineering, synthetic biology, and environmentally friendly manufacturing. This review encapsulates recent progress in the exploration of chemical diversity, pharmacological characteristics, omics-based analyses, and biosynthetic research pertaining to Ligustrum . It proposes an integrated methodology that amalgamates multi-omics approaches, synthetic biology, and environmentally sustainable manufacturing processes to advance strategies for whole-plant valorization and germplasm conservation. The objective is to establish a theoretical framework and technical paradigm to facilitate the comprehensive exploitation and sustainable utilization of Ligustrum as a medicinal resource.\n\nCandidates:\nA. robustum are not well-documented for their therapeutic roles in nourishing the liver and kidneys, enhancing vision, darkening hair, and serving as functional tea ingredients.\nB. The genus Ligustrum (Oleaceae) encompasses woody plants with both medicinal and edible uses, distinguished by a wide range of bioactive compounds, including triterpenoids, phenylethanoid glycosides, flavonoids, and other active constituents.\nC. These metabolites demonstrate multi-target pharmacological effects, such as anti-inflammatory, antioxidant, antitumor, and anti-osteoporotic activities.\nD. In traditional medicine, species like L.\nE. The evidence does not state that in traditional medicine, species like L.\nF. The evidence does not state that these metabolites demonstrate multi-target pharmacological effects, such as anti-inflammatory, antioxidant, antitumor, and anti-osteoporotic activities.\nG. robustum are well-documented for their therapeutic roles in nourishing the liver and kidneys, enhancing vision, darkening hair, and serving as functional tea ingredients.\nH. The evidence does not state that the genus Ligustrum (Oleaceae) encompasses woody plants with both medicinal and edible uses, distinguished by a wide range of bioactive compounds, including triterpenoids, phenylethanoid glycosides, flavonoids, and other active constituents.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13022168", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13022168/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cff2ec1a464a74b8dd1a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nClimate change and environmental pollution are among the most pressing global challenges today, with water pollution standing out as a particularly critical issue. Industrial wastewater discharge, especially from distilleries, significantly contributes to the degradation of aquatic and terrestrial ecosystems. Molasses-based distilleries are major perpetrators, producing vast quantities of dark brown effluent known as spent wash. This colouration is largely due to the presence of melanoidin, a recalcitrant compound formed via the Maillard reaction. Although many distilleries now utilize anaerobic digestion to convert this organic-rich waste into biogas, the resultant biomethanated spent wash remains highly coloured and environmentally hazardous. Direct discharge of untreated or partially treated spent wash into rivers, lakes, or soil severely disrupts ecological balance and poses risks to biodiversity. Existing disposal practices, such as lagoon storage or composting with press mud, offer limited solutions to the colour problem. Fungi, particularly those producing laccase and other oxidative enzymes, have demonstrated promising potential for decolourizing spent wash in laboratory studies. However, the enzymatic pathways involved in melanoidin degradation are still not fully understood. To address the persistant colour challenge, integrated treatment strategies combining fungal systems with complementary physical or chemical processes (eg, adsorption or advanced oxidation) may be required to achieve effective decolourisation. Such advancements are vital for creating effective, eco-friendly solutions to mitigate the environmental impact of the distillery industry and promote a circular bioeconomy. Keywords: Decolourization, Distillery spentwash, Effluent, Fungi, Molasses\n\nCandidates:\nA. Climate change and environmental pollution are among the most pressing global challenges today, with water pollution standing out as a particularly critical issue.\nB. This colouration is not largely due to the presence of melanoidin, a recalcitrant compound formed via the Maillard reaction.\nC. Climate change and environmental pollution are not among the most pressing global challenges today, with water pollution standing out as a particularly critical issue.\nD. Industrial wastewater discharge, especially from distilleries, significantly contributes to the degradation of aquatic and terrestrial ecosystems.\nE. The evidence does not state that industrial wastewater discharge, especially from distilleries, significantly contributes to the degradation of aquatic and terrestrial ecosystems.\nF. This colouration is largely due to the presence of melanoidin, a recalcitrant compound formed via the Maillard reaction.\nG. Molasses-based distilleries are major perpetrators, producing vast quantities of dark brown effluent known as spent wash.\nH. Molasses-based distilleries are not major perpetrators, producing vast quantities of dark brown effluent known as spent wash.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13022225", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13022225/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0f811fb3717b1cebd454", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe yeast Komagataella phaffii is an emerging microbial host for the production of functional recombinant proteins. However, proteolytic degradation during fermentation often compromises product yield and stability, posing a major hurdle for industrial-scale applications. This study presents a strategy to enhance the production of recombinant humanized type I collagen (rhColI) by engineering a host strain with reduced protease activity. An initial production strain, CL1, was engineered using post-transformational vector amplification (PTVA), achieving a titer of 558.86 ± 20.05 mg/L in flask culture. Subsequent scale-up fermentation, however, revealed significant rhColI degradation. To address this, we systematically deleted 11 candidate endogenous protease genes. The knockout of a serine protease gene, Kp Sub2, resulted in the most pronounced improvement, elevating the rhColI titer to 1039.06 ± 34.08 mg/L, a 23.47% increase over the parent strain CL1. Furthermore, the purified Kp Sub2 protein, obtained from inclusion bodies in Escherichia coli , demonstrated broad proteolytic activity against various types of recombinant humanized collagens. This broad substrate specificity was consistent with the observation that Kp Sub2 deletion also mitigated the degradation of recombinant humanized type III collagen (rhColIII). Our findings establish Kp Sub2 as a key mediator of collagen degradation in K. phaffii and provide an effective engineering strategy for optimizing the production of collagen and other degradation-susceptible functional proteins in this host. The online version contains supplementary material available at 10.1186/s40643-026-01039-y. Keywords: Recombinant humanized collagen, Serine protease, Proteolytic degradation, Komagataella phaffii\n\nCandidates:\nA. An initial production strain, CL1, was engineered using post-transformational vector amplification (PTVA), achieving a titer of 558.86 ± 20.05 mg/L in flask culture.\nB. However, proteolytic degradation during fermentation often compromises product yield and stability, posing a major hurdle for industrial-scale applications.\nC. The yeast Komagataella phaffii is an emerging microbial host for the production of functional recombinant proteins.\nD. This study presents a strategy to enhance the production of recombinant humanized type I collagen (rhColI) by engineering a host strain with reduced protease activity.\nE. The yeast Komagataella phaffii is not an emerging microbial host for the production of functional recombinant proteins.\nF. The evidence does not state that however, proteolytic degradation during fermentation often compromises product yield and stability, posing a major hurdle for industrial-scale applications.\nG. An initial production strain, CL2, was engineered using post-transformational vector amplification (PTVA), achieving a titer of 558.86 ± 20.05 mg/L in flask culture.\nH. The evidence does not state that this study presents a strategy to enhance the production of recombinant humanized type I collagen (rhColI) by engineering a host strain with reduced protease activity.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13022228", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13022228/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ad2ef05e60da2529f867", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nTransient DNA transfection is routinely used to study gene function and elucidate the regulation of biological pathways, and it is also widely applied in biotechnology for large-scale recombinant protein production. The results of recent studies involving mammalian cells have highlighted that competition for cellular resources during gene expression can bias data interpretation, directly affecting co-transfection experiments. In this study, our results showed that co-transfected plasmids markedly enhance transient—but not stable—expression of various reporter genes across different cell types. The enhancement of transient reporter gene expression by additional plasmid DNA occurs when these DNAs are co-delivered simultaneously and is unlikely to be mediated by cytokine induction. Furthermore, co-transfected plasmids were shown to upregulate transcription, but not translation, of the reporter gene during transient expression. Thus, the observed enhancement may result from competition between co-transfected plasmids and reporter constructs for cellular proteins that interact with transfected DNA, such as histones. Indeed, Pracinostat (SB939), an inhibitor of histone deacetylase, was able to enhance the transient expression of the reporter gene dose-dependently. Overall, this study provides insights that may facilitate improved transient expression of recombinant genes in biotechnological applications.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13023970", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13023970/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c265b65ca467cc85d2a6", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCarotenoids are increasingly studied for their robust antioxidant capacity, anti-inflammatory potential, protective vision and validated contribution to human health. Carotenoids are mainly obtained through chemical synthesis and plant extraction, which results in relatively high costs for producing carotenoids. However, microalgae represent a sustainable and high-yield platform for natural carotenoid production, with advantages including rapid growth, high pigment accumulation, and broad environmental adaptability. This review summarizes recent biotechnological advances in enhancing carotenoid production, with a focus on metabolic engineering, environmental regulation, and cultivation strategies. CRISPR/Cas9 enables precision metabolic pathway engineering, while environmental factors like light, nutrients, and stress significantly influence yield. Different cultivation strategies allow carotenoids to fulfill commercial or research needs. The two-stage strategy achieves rapid biomass increase during the growth stage, then shifts to accumulate carotenoids. This regulatory mode significantly reduces cell death by continuous stress, providing high productivity and stability in large-scale production. Carotenoids participate in many innovative applications across various fields, including treatments in medicine, skin protection in cosmetics, protein stabilization in foods, enhancing animals’ survival and so on. Future research will integrate bioprocess optimization, precision strain engineering, and adaptive environmental strategies to scale high-value microalgal carotenoid production as a commercially and environmentally viable solution.\n\nCandidates:\nA. Carotenoids are increasingly studied for their robust antioxidant capacity, anti-inflammatory potential, protective vision and validated contribution to human health.\nB. Carotenoids are mainly obtained through chemical synthesis and plant extraction, which results in relatively high costs for producing carotenoids.\nC. However, microalgae represent a sustainable and high-yield platform for natural carotenoid production, with advantages including rapid growth, high pigment accumulation, and broad environmental adaptability.\nD. Carotenoids are not increasingly studied for their robust antioxidant capacity, anti-inflammatory potential, protective vision and validated contribution to human health.\nE. The evidence does not state that however, microalgae represent a sustainable and high-yield platform for natural carotenoid production, with advantages including rapid growth, high pigment accumulation, and broad environmental adaptability.\nF. Carotenoids are not mainly obtained through chemical synthesis and plant extraction, which results in relatively high costs for producing carotenoids.\nG. This review summarizes recent biotechnological advances in enhancing carotenoid production, with a focus on metabolic engineering, environmental regulation, and cultivation strategies.\nH. The evidence does not state that this review summarizes recent biotechnological advances in enhancing carotenoid production, with a focus on metabolic engineering, environmental regulation, and cultivation strategies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13024249", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13024249/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3095465d2cb6eddd3ece", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nYeasts play a vital role in food fermentation processes, where their viability, stress tolerance, and metabolic performance directly influence product quality and process efficiency. Controlling and modulating yeast behavior represents a challenge in the food industry, particularly in non-thermal processing contexts. Ultraviolet (UV) technology has traditionally been applied as a microbial control tool; however, yeast response mechanisms to UV irradiation extend beyond simple inactivation. Depending on wavelength, dose, and treatment conditions, UV exposure can lead to complete inactivation, partial reduction in viability, or induce stable phenotypic changes associated with cellular stress responses and Deoxyribonucleic Acid (DNA) damage processing. This review examines current knowledge on yeast–UV interactions across different food matrices, highlighting how UV treatments influence yeast physiology and functionality. In addition, recent studies suggest that UV-induced genetic alterations, when properly controlled, may contribute to yeast diversification and functional modulation without the use of genetically modified organisms. The review discusses technological opportunities, practical limitations, and future research needs, emphasizing the dual role of UV technology as a tool for yeast control and as a potential driver of functional modulation.\n\nCandidates:\nA. The evidence does not state that yeasts play a vital role in food fermentation processes, where their viability, stress tolerance, and metabolic performance directly influence product quality and process efficiency.\nB. The evidence does not state that controlling and modulating yeast behavior represents a challenge in the food industry, particularly in non-thermal processing contexts.\nC. Controlling and modulating yeast behavior represents a challenge in the food industry, particularly in non-thermal processing contexts.\nD. Depending on wavelength, dose, and treatment conditions, UV exposure can lead to complete inactivation, partial reduction in viability, or induce stable phenotypic changes associated with cellular stress responses and Deoxyribonucleic Acid (DNA) damage processing.\nE. Ultraviolet (UV) technology has traditionally been applied as a microbial control tool; however, yeast response mechanisms to UV irradiation extend beyond simple inactivation.\nF. Depending on wavelength, dose, and treatment conditions, UV exposure cannot lead to complete inactivation, partial reduction in viability, or induce stable phenotypic changes associated with cellular stress responses and Deoxyribonucleic Acid (DNA) damage processing.\nG. The evidence does not state that ultraviolet (UV) technology has traditionally been applied as a microbial control tool; however, yeast response mechanisms to UV irradiation extend beyond simple inactivation.\nH. Yeasts play a vital role in food fermentation processes, where their viability, stress tolerance, and metabolic performance directly influence product quality and process efficiency.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13024916", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13024916/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8a3a0f029498dd2a4ad7", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nAmid growing concerns about climate change and its potential impacts on food security and malnutrition, there is a need for climate-smart crops to help mitigate these challenges. African yam bean ( Sphenostylis stenocarpa ) and Bambara groundnut ( Vigna subterranea ) are considered climate-smart neglected or underutilised species (NUS) in sub-Saharan Africa (SSA). These legumes are rich in nutrients, comprising fats, carbohydrates, and protein, as well as essential micronutrients. However, their use is constrained by the presence of antinutritive factors (ANFs) such as oxalates, tannins, and phytates, which reduces mineral bioaccessibility and protein digestibility. Fermentation provides a cost-effective means of effectively reducing these antinutrients, thereby making these crops more mainstream due to their enhanced bioavailability and bioactivity. This review summarises the impact of diverse microbes and fermentation techniques on the bioavailability of essential nutrients in Bambara groundnut and African yam bean. The importance of pre-treatment steps such as soaking, germination, dehulling, and thermal treatment will also be discussed. By synthesising recent studies, the review explores the mechanisms by which fermentation degrades the ANFs, enhances nutrient bioavailability and improves protein digestibility from these crops. This review explores the pivotal roles of fermenting microbes, such as species of Lactobacillus and Bacillus , during the process of biotransformation.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13025381", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13025381/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1e25f5a957f16c584d5c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCollagen is the most abundant structural and functional protein in humans and other vertebrates. It possesses remarkable biological functions and is widely used in food, cosmetics, and healthcare. Currently, mainstream animal-derived collagen materials carry risks such as viral transmission and allergic reactions. However, recombinant collagen, heterologously expressed using genetic recombination technology combined with high-density fermentation processes, offers greater biocompatibility, low immunogenicity, and consistent quality, offering promising development prospects. However, current research on recombinant collagen still faces challenges such as low yield and poor functionality. This article briefly describes the structure, types, and functions of collagen, discusses the advantages and limitations of different recombinant collagen expression systems, and highlights the strategies for improving the yield and optimizing the function of recombinant collagen, ranging from gene editing to fermentation optimization. In highlighting practical approaches to achieving high yield, we present a series of case examples to illustrate the successful application of these principles. This review aims to help researchers, engineers, and industry practitioners better understand research trends in the expression and production of recombinant collagen, and to promote its further development and commercialization across diverse application areas.\n\nCandidates:\nA. It possesses remarkable biological functions and is widely used in food, cosmetics, and healthcare.\nB. Collagen is the most abundant structural and functional protein in humans and other vertebrates.\nC. It possesses remarkable biological functions and is not widely used in food, cosmetics, and healthcare.\nD. Collagen is not the most abundant structural and functional protein in humans and other vertebrates.\nE. However, recombinant collagen, heterologously expressed using genetic recombination technology combined with high-density fermentation processes, offers greater biocompatibility, low immunogenicity, and consistent quality, offering promising development prospects.\nF. Currently, mainstream animal-derived collagen materials carry risks such as viral transmission and allergic reactions.\nG. The evidence does not state that currently, mainstream animal-derived collagen materials carry risks such as viral transmission and allergic reactions.\nH. The evidence does not state that however, recombinant collagen, heterologously expressed using genetic recombination technology combined with high-density fermentation processes, offers greater biocompatibility, low immunogenicity, and consistent quality, offering promising development prospects.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13026310", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13026310/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-436c6d2b0b70752d8959", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDue to their exceptional nutritional benefits, microalgae and cyanobacteria are recognized as sustainable food sources and key contributors to the circular bioeconomy. Arthrospira sp. has garnered significant attention as one of the most promising cyanobacteria for a wide range of applications. The purpose of this study is to systematically analyze and synthesize global research trends in Arthrospira sp. applications. In this context, a “systematic review” refers to an integrated bibliometric and thematic analysis encompassing publication trends, geographical distribution of research outputs, leading journals, key application sectors, market development, and associated challenges and future prospects. Consequently, extensive research has been conducted on this species, leading to diverse areas of interest and application. This review article is the first of its kind, offering a comprehensive summary of trends in Arthrospira sp. applications over the past 15 years. It presents a bibliometric analysis of publications from 2010 to 2024 in journals indexed by Scopus. The analysis revealed that Bioresource Technology is the leading journal in publishing related research, with China producing the highest number of studies. Furthermore, phycocyanin extraction emerged as the most frequently studied application. Recently explored applications include its use as a biofertilizer, in bioplastic production, and in cosmetics. The Arthrospira sp. market is currently valued at an estimated $619 million in 2024, positioning it as a dominant player in the global industry. However, challenges persist, including safety concerns related to potential allergies and toxicity, as well as regulatory hurdles that may affect commercialization and market expansion.\n\nCandidates:\nA. The purpose of this study is to systematically analyze and synthesize global research trends in Arthrospira sp.\nB. The purpose of this study is not to systematically analyze and synthesize global research trends in Arthrospira sp.\nC. In this context, a “systematic review” refers to an integrated bibliometric and thematic analysis encompassing publication trends, geographical distribution of research outputs, leading journals, key application sectors, market development, and associated challenges and future prospects.\nD. The evidence does not state that has garnered significant attention as one of the most promising cyanobacteria for a wide range of applications.\nE. Due to their exceptional nutritional benefits, microalgae and cyanobacteria are recognized as sustainable food sources and key contributors to the circular bioeconomy.\nF. The evidence does not state that in this context, a “systematic review” refers to an integrated bibliometric and thematic analysis encompassing publication trends, geographical distribution of research outputs, leading journals, key application sectors, market development, and associated challenges and future prospects.\nG. Due to their exceptional nutritional benefits, microalgae and cyanobacteria are not recognized as sustainable food sources and key contributors to the circular bioeconomy.\nH. has garnered significant attention as one of the most promising cyanobacteria for a wide range of applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13029403", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13029403/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3a8f087f85ae7689dbb6", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"91%\", \"90%\"]\n\nEvidence:\nThe removal of cadmium from contaminated water remains a critical challenge due to its high toxicity, persistence, and limited treatability at low concentrations. In this study, we propose a novel algal–nanoparticle system that integrates cadmium adsorption by Chlorella vulgaris with zinc ferrite (ZnFe 2 O 4 ) nanoparticle-assisted sedimentation, with the aim of addressing a significant operational challenge in algal remediation. The microalgal biomass demonstrated the capacity to remove cadmium with efficiencies exceeding 90%, facilitated by adsorption through surface functional groups. The incorporation of ZnFe 2 O 4 nanoparticles promoted the formation of dense, magnetically responsive aggregates, significantly accelerating biomass settling without the necessity for additional chemical flocculants. The strategy’s efficacy is evidenced by its enhancement of metal removal and solid–liquid separation processes, which renders it a potentially scalable and environmentally sustainable approach for the treatment of cadmium-contaminated wastewater. The strategy holds relevance for effluents derived from mining, electroplating, fertilizer production and battery manufacturing.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13029502", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13029502/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-28cee976b632c305d0b4", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"75%\", \"51%\", \"73.00%\", \"72.00%\", \"50%\", \"71.37%\", \"70.37%\", \"76%\"]\n\nEvidence:\nNitrogen supplements such as yeast extract and peptone/tryptone are the main cost drivers in bacterial cellulose (BC) fermentation. This study evaluated fourteen cereal, pseudo-cereal and legume flours as media substitutes for Komagataeibacter xylinus DSMZ 2325 using two strategies: (i) constant total nitrogen (CTN; 0.6 g·L −1 ) and (ii) constant nitrogen-source mass (CNSM; 5.0 g·L −1 ). BC yield (dry g·L −1 ) was determined under static cultivation and analyzed by ANOVA, correlation statistics and techno-economic assessment. Flour type and substitution level significantly influenced BC production ( p < 0.05). CTN substitution enhanced production, with the highest peak yields obtained for W-BC, C-BC, M-BC and SP-BC (6.68–8.97 g·L −1 ). CNSM substitution limited production, with O-BC and T-BC performing best (4.24–5.14 g·L −1 ). Techno-economic analysis further showed that the CTN regime substantially improved cost efficiency and reduced BC unit production cost, with the maximum reduction observed for TR-BC at 75% substitution (from 0.27 to 0.08 €/g; 70.37%) relative to the corresponding CTN HS control. Under the CNSM regime, the maximum reduction was observed for BY-BC at 50% substitution (from 0.25 to 0.07 €/g; 72.00%) relative to the corresponding CNSM HS control. These findings demonstrate that graded nitrogen substitution is an effective strategy for economically sustainable and scalable BC production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13030736", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13030736/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a6865b1297cfbca35af8", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nDespite well-known benefits, the increasing use of single-use technology (SUT) in biopharmaceutical processes has raised concerns about the environmental impact of plastic waste. This paper provides the first bioprocessing industry example of a “closing the loop” proof of concept by implementing a circular economy model for a polycarbonate (PC) bioreactor vessel used in process development applications. Through a collaborative effort between an end user, a SUT supplier, and a resin supplier, a lab-scale study was initiated to collect, decontaminate, and mechanically recycle material from vessels after use in mammalian cell culture experiments to produce new vessels. The study demonstrates that using recycled PC reduces vessels' environmental footprint and does not adversely impact vessel extractables. Even in direct contact with cells and media, recycled PC left cell culture performance and monoclonal antibody production largely unaffected. This work paves the way for broader adoption of circular practices in the industry. Life Cycle Assessment (LCA) was used to evaluate closed-loop recycling of PC across multiple environmental indicators (e.g., climate change, resource use, water use, etc.). Benefits typically favor the closed-loop system, but sensitivity analysis indicates that variations in parameters such as recovery yield, contamination rate, means of transport, and electricity mix can erode these advantages in non-ideal settings. The paper outlines the logistics, challenges, and learnings from this program, emphasizing the need for standardized procedures and collaboration across teams to achieve sustainable SUT circularity. • Recycling bioreactor polycarbonate vessels reduces environmental footprint. • Equivalent extractables observed in virgin and recycled polycarbonate vessels. • Cell culture performance is comparable across recycled and virgin vessels. The online version contains supplementary material available at 10.1007/s00253-026-13796-z.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13032987", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13032987/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6215e51d6e55918bdede", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe rising demand for renewable and sustainable oleochemicals underscores the necessity of producing yeast oil from lignocellulosic hydrolysates. We isolated two oleaginous yeast strains of Candida maltosa (UFV-1 and UFV-2) able to assimilate xylose and tolerate hemicellulosic hydrolysate inhibitors (acetic acid, furfural, hydroxymethylfurfural and formic acid). To optimize growth and lipid production by C. maltosa in malt bagasse hemicellulosic hydrolysate, we applied a central composite rotational design with response surface methodology, evaluating initial pH and yeast extract:peptone ratio (P:YE). The maximum lipid responses were recorded in: (P:YE = 1.0:0.0 g/L, pH = 6.0) for UFV-1 and (P:YE = 0.58:0.42 g/L, pH = 6.5) for UFV-2. Biomass and lipid production were higher in the detoxified hydrolysate than in the non-detoxified hydrolysate. Biomass titers increased from 9.12 to 17.0 g/L and from 10.5 to 12.9 g/L for UFV-1 and UFV-2, respectively; whilst the lipid titers increased from 0.84 to 3.14 g/L and 0.91 to 1.58 g/L for UFV-1 and UFV-2, respectively. Importantly, the levels of 16:0, 18:0, 10:0 fatty acids increased in the detoxified medium. Cultivation of the UFV-1 strain from detoxified hydrolysate in a benchtop bioreactor improved biomass formation, achieving a lipid productivity of 0.29 ± 0.02 g/L h, highlighting its potential to produce oil from hemicellulosic hydrolysates in biorefineries. The online version contains supplementary material available at 10.1007/s11274-026-04919-9. Keywords: Sustainable oleochemicals, Yeast, Response surface methodology, Biorefineries\n\nCandidates:\nA. We isolated two oleaginous yeast strains of Candida maltosa (UFV-2 and UFV-2) able to assimilate xylose and tolerate hemicellulosic hydrolysate inhibitors (acetic acid, furfural, hydroxymethylfurfural and formic acid).\nB. maltosa in malt bagasse hemicellulosic hydrolysate, we applied a central composite rotational design with response surface methodology, evaluating initial pH and yeast extract:peptone ratio (P:YE).\nC. The evidence does not state that the rising demand for renewable and sustainable oleochemicals underscores the necessity of producing yeast oil from lignocellulosic hydrolysates.\nD. To optimize growth and lipid production by C.\nE. The evidence does not state that to optimize growth and lipid production by C.\nF. The rising demand for renewable and sustainable oleochemicals underscores the necessity of producing yeast oil from lignocellulosic hydrolysates.\nG. The evidence does not state that maltosa in malt bagasse hemicellulosic hydrolysate, we applied a central composite rotational design with response surface methodology, evaluating initial pH and yeast extract:peptone ratio (P:YE).\nH. We isolated two oleaginous yeast strains of Candida maltosa (UFV-1 and UFV-2) able to assimilate xylose and tolerate hemicellulosic hydrolysate inhibitors (acetic acid, furfural, hydroxymethylfurfural and formic acid).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13035538", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13035538/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7efd5350a268c38a5f18", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nVero cells are extensively used in viral vaccine production, but their adaptation to serum-free suspension culture is hindered by excessive autophagy and strong anchorage dependence. In this study, we identified Connective Tissue Growth Factor (CTGF/CCN2) as significantly upregulated under starvation—an inducer of autophagy—via RNA-seq screening. A stable CTGF knockdown Vero cell line (knockdown efficiency >50%) was established using lentiviral shRNA. Functional characterization demonstrated that CTGF depletion concurrently attenuated autophagic flux (evidenced by reduced LC3-II/I ratio and lysosomal activity) and impaired cell adhesion (with adhesion rates decreased by 50%–60% on extracellular matrix proteins), while maintaining normal cell proliferation. Our findings reveal a new role for CTGF in regulating the environmental adaptation of Vero cells by coordinating autophagy and adhesion functions. The engineered cell line provides a novel strategy to overcome suspension adaptation bottlenecks, offering significant potential for improving vaccine production scalability.\n\nCandidates:\nA. Functional characterization demonstrated that CTGF depletion concurrently attenuated autophagic flux (evidenced by reduced LC3-II/I ratio and lysosomal activity) and impaired cell adhesion (with adhesion rates decreased by 50%–60% on extracellular matrix proteins), while maintaining normal cell proliferation.\nB. A stable CTGF knockdown Vero cell line (knockdown efficiency >51%) was established using lentiviral shRNA.\nC. A stable CTGF knockdown Vero cell line (knockdown efficiency >50%) was established using lentiviral shRNA.\nD. In this study, we identified Connective Tissue Growth Factor (CTGF/CCN2) as significantly upregulated under starvation—an inducer of autophagy—via RNA-seq screening.\nE. Vero cells are extensively used in viral vaccine production, but their adaptation to serum-free suspension culture is hindered by excessive autophagy and strong anchorage dependence.\nF. Vero cells are extensively used in viral vaccine production, but their adaptation to serum-free suspension culture is not hindered by excessive autophagy and strong anchorage dependence.\nG. In this study, we identified Connective Tissue Growth Factor (CTGF/CCN3) as significantly upregulated under starvation—an inducer of autophagy—via RNA-seq screening.\nH. Functional characterization demonstrated that CTGF depletion concurrently attenuated autophagic flux (evidenced by reduced LC4-II/I ratio and lysosomal activity) and impaired cell adhesion (with adhesion rates decreased by 50%–60% on extracellular matrix proteins), while maintaining normal cell proliferation.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13035795", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13035795/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8ebe78a10eb0ab495821", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nOsteosarcoma (OS) remains a challenging malignancy with a high propensity for metastasis and poor survival outcomes. Bone marrow mesenchymal stem cell-derived extracellular vesicles (BMSC-EVs) have emerged as key mediators in the tumor microenvironment, promoting OS progression. This study identifies a novel molecular axis centered on circRNA-0010220 within BMSC-EVs that drives OS aggressiveness. We demonstrate that BMSC-EVs are internalized by OS cells, enhancing their proliferation, migration, and invasion. High-throughput sequencing revealed circRNA-0010220 as the most significantly upregulated circRNA in EV-treated OS cells. Functional studies showed that circRNA-0010220 knockdown in BMSCs attenuated the oncogenic effects of their EVs both in vitro and in vivo. Mechanistically, circRNA-0010220 recruits the histone methyltransferase EZH2 to the CTNNBIP1 promoter, facilitating H3K27me3-mediated epigenetic silencing. The subsequent downregulation of CTNNBIP1 leads to activation of the Wnt/β-catenin signaling pathway. This cascade was consistently observed across gain-of-function and loss-of-function experiments, and pharmacologic inhibition of β-catenin reversed the pro-tumorigenic effects. Our findings elucidate a complete signaling axis from BMSC-EVs to Wnt/β-catenin activation via circRNA-0010220/EZH2/CTNNBIP1, providing new insights into the epigenetic regulation of OS progression and suggesting potential therapeutic targets.\n\nCandidates:\nA. This study identifies a novel molecular axis centered on circRNA-0010220 within BMSC-EVs that drives OS aggressiveness.\nB. This study identifies a novel molecular axis centered on circRNA-10221 within BMSC-EVs that drives OS aggressiveness.\nC. Bone marrow mesenchymal stem cell-derived extracellular vesicles (BMSC-EVs) have emerged as key mediators in the tumor microenvironment, promoting OS progression.\nD. We demonstrate that BMSC-EVs are not internalized by OS cells, enhancing their proliferation, migration, and invasion.\nE. Osteosarcoma (OS) remains a challenging malignancy with a high propensity for metastasis and poor survival outcomes.\nF. The evidence does not state that osteosarcoma (OS) remains a challenging malignancy with a high propensity for metastasis and poor survival outcomes.\nG. We demonstrate that BMSC-EVs are internalized by OS cells, enhancing their proliferation, migration, and invasion.\nH. The evidence does not state that bone marrow mesenchymal stem cell-derived extracellular vesicles (BMSC-EVs) have emerged as key mediators in the tumor microenvironment, promoting OS progression.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13039318", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13039318/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7a087f7c6cee11947c9d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIn this review, we systematically categorize diverse organoid engineering strategies—including cellular programming, material engineering, and platform- or system-level innovations—according to their impact on reproducibility and scalability, and highlight representative applications and emerging directions. By reframing organoid generation as a manufacturing process, these technological advances pave the way toward standardized and high-fidelity organoid production for both fundamental research and translational applications. Subject terms: Biological techniques, Biotechnology, Engineering, Medical research, Stem cells\n\nCandidates:\nA. Subject terms: Biological techniques, Biotechnology, Engineering, Medical research, Stem cells\nB. In this review, we systematically categorize diverse organoid engineering strategies—including cellular programming, material engineering, and platform- or system-level innovations—according to their impact on reproducibility and scalability, and highlight representative applications and emerging directions.\nC. The evidence does not state that in this review, we systematically categorize diverse organoid engineering strategies—including cellular programming, material engineering, and platform- or system-level innovations—according to their impact on reproducibility and scalability, and highlight representative applications and emerging directions.\nD. The evidence does not state that subject terms: Biological techniques, Biotechnology, Engineering, Medical research, Stem cells\nE. By reframing organoid generation as a manufacturing process, these technological advances pave the way toward standardized and high-fidelity organoid production for both fundamental research and translational applications.\nF. The evidence does not state that by reframing organoid generation as a manufacturing process, these technological advances pave the way toward standardized and high-fidelity organoid production for both fundamental research and translational applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13042217", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13042217/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3539a269fe568c5e6cf7", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe rapid escalation of global plastic consumption, coupled with the environmental impacts of petrochemical polymers, has sparked a surge of interest in bioplastics, particularly those derived from microbial and enzymatic processes. This review provides a comprehensive overview of the metabolic pathways, structural properties and emerging technological innovations shaping the next generation of bioplastics, with a particular focus on polyhydroxyalkanoates (PHA). The following sections outline the conceptual distinctions between bio‐based and biodegradable plastics, the key bacterial pathways responsible for the biosynthesis of PHA, PLA precursors, bacterial cellulose, microbial polyamides and other bio‐derived polymers. The physicochemical and morphological features of PHA‐based materials are analysed as well. These features include monomer composition, crystallinity, copolymer architecture and molecular weight. The relationship between these features and the mechanical and thermal performance of the materials is then investigated. A dedicated section is allocated to recent advances in in vitro enzymatic PHA synthesis, covering PHA synthase (PhaC) classes, engineered variants, cell‐free metabolic engineering platforms, enzyme immobilisation and surface‐display strategies that enable fully programmable and modular polymerisation. Finally, we discuss future perspectives, with particular emphasis on sustainable feedstocks, process intensification through synthetic biology, techno‐economic challenges and the regulatory landscape required for large‐scale adoption. The present review integrates biochemical, structural and bioprocessing insights to map current progress and identify strategic directions for enabling enzymatic bioplastics as scalable, customisable and environmentally sound alternatives within a circular bioeconomy framework. This review highlights recent advances in microbial and enzymatic routes for producing polyhydroxyalkanoate‐based bioplastics, with emphasis on engineered enzymes and cell‐free systems. By integrating biochemical and bioprocess insights, it outlines strategies to enable scalable and sustainable biopolymer production within a circular bioeconomy. Keywords: bioplastic copolymers, enzymatic polymerisation, metabolic engineering, PHA synthase\n\nCandidates:\nA. The evidence does not state that the rapid escalation of global plastic consumption, coupled with the environmental impacts of petrochemical polymers, has sparked a surge of interest in bioplastics, particularly those derived from microbial and enzymatic processes.\nB. The evidence does not state that the following sections outline the conceptual distinctions between bio‐based and biodegradable plastics, the key bacterial pathways responsible for the biosynthesis of PHA, PLA precursors, bacterial cellulose, microbial polyamides and other bio‐derived polymers.\nC. The rapid escalation of global plastic consumption, coupled with the environmental impacts of petrochemical polymers, has sparked a surge of interest in bioplastics, particularly those derived from microbial and enzymatic processes.\nD. The physicochemical and morphological features of PHA‐based materials are analysed as well.\nE. The physicochemical and morphological features of PHA‐based materials are not analysed as well.\nF. The following sections outline the conceptual distinctions between bio‐based and biodegradable plastics, the key bacterial pathways responsible for the biosynthesis of PHA, PLA precursors, bacterial cellulose, microbial polyamides and other bio‐derived polymers.\nG. This review provides a comprehensive overview of the metabolic pathways, structural properties and emerging technological innovations shaping the next generation of bioplastics, with a particular focus on polyhydroxyalkanoates (PHA).\nH. The evidence does not state that this review provides a comprehensive overview of the metabolic pathways, structural properties and emerging technological innovations shaping the next generation of bioplastics, with a particular focus on polyhydroxyalkanoates (PHA).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13042732", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13042732/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e473517c54d1ec749b5c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBacteria‐based cancer therapy is a promising cancer treatment strategy that utilizes genetically engineered attenuated bacteria to specifically target tumor tissues, directly kill cancer cells, or activate the host's immune response. Engineering modifications of attenuated bacteria can achieve more effective anti‐tumor effects, and consequently, a number of clinical trials have been carried out. However, the clinical translation of attenuated bacteria faces multiple challenges, including safety risks, fluctuating therapeutic efficacy, difficulties in vivo monitoring, complex production quality control, and lack of regulatory approval standards. This review mainly summarizes five aspects of bacterial cancer therapy: the safety risks and attenuation strategies, the regulation of therapeutic stability, bacterial in vivo imaging technologies, the optimization of bacterial production processes, and market approval pathways. It aims to precisely control bacterial behavior through genetic circuits and enable real‐time monitoring of the treatment process by means of multimodal imaging, ultimately promoting the clinical translation of attenuated bacterial therapy.\n\nCandidates:\nA. The evidence does not state that this review mainly summarizes five aspects of bacterial cancer therapy: the safety risks and attenuation strategies, the regulation of therapeutic stability, bacterial in vivo imaging technologies, the optimization of bacterial production processes, and market approval pathways.\nB. However, the clinical translation of attenuated bacteria faces multiple challenges, including safety risks, fluctuating therapeutic efficacy, difficulties in vivo monitoring, complex production quality control, and lack of regulatory approval standards.\nC. This review mainly summarizes five aspects of bacterial cancer therapy: the safety risks and attenuation strategies, the regulation of therapeutic stability, bacterial in vivo imaging technologies, the optimization of bacterial production processes, and market approval pathways.\nD. Bacteria‐based cancer therapy is not a promising cancer treatment strategy that utilizes genetically engineered attenuated bacteria to specifically target tumor tissues, directly kill cancer cells, or activate the host's immune response.\nE. The evidence does not state that however, the clinical translation of attenuated bacteria faces multiple challenges, including safety risks, fluctuating therapeutic efficacy, difficulties in vivo monitoring, complex production quality control, and lack of regulatory approval standards.\nF. Bacteria‐based cancer therapy is a promising cancer treatment strategy that utilizes genetically engineered attenuated bacteria to specifically target tumor tissues, directly kill cancer cells, or activate the host's immune response.\nG. Engineering modifications of attenuated bacteria cannot achieve more effective anti‐tumor effects, and consequently, a number of clinical trials have been carried out.\nH. Engineering modifications of attenuated bacteria can achieve more effective anti‐tumor effects, and consequently, a number of clinical trials have been carried out.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13042812", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13042812/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e41c49036f0d5bb31d15", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nNext-generation prebiotics are increasingly recognized for their ability to modulate gut microbiota and promote host health. Xylo-oligosaccharides (XOS) and malto-oligosaccharides (MOS), derived from cereal biomass, are particularly promising due to their structural stability, low effective dosages, and selective stimulation of beneficial gut microorganisms. This review synthesizes current knowledge on XOS and MOS, with emphasis on enzyme-enabled production technologies, microbial fermentation behavior, and health outcomes in humans and animals. Recent advances in tailored enzyme cocktails and sequential bioconversion strategies enable precise control over oligosaccharide yield and degree of polymerization, facilitating targeted production of functionally distinct fractions. Comparative analyses demonstrate that XOS and MOS offer greater microbial selectivity than conventional prebiotics, such as fructo- and galacto-oligosaccharides, and consistently enhance short-chain fatty acid production. These effects contribute to improved metabolic regulation, immune function, and resistance to enteric pathogens. A distinctive contribution of this review is the valorization of Kazakhstan’s underutilized cereal residues, including wheat, rice, barley, and millet straw and bran, as sustainable feedstocks for prebiotic production. Integrating regional biomass with engineered enzyme systems provides a scalable and environmentally sound platform. Furthermore, combined XOS–MOS formulations may exert synergistic microbiota-modulating effects through complementary fermentation dynamics. Collectively, these insights position enzyme-based conversion of cereal biomass as a novel, regionally relevant strategy for advancing next-generation prebiotics.\n\nCandidates:\nA. This review synthesizes current knowledge on XOS and MOS, with emphasis on enzyme-enabled production technologies, microbial fermentation behavior, and health outcomes in humans and animals.\nB. The evidence does not state that recent advances in tailored enzyme cocktails and sequential bioconversion strategies enable precise control over oligosaccharide yield and degree of polymerization, facilitating targeted production of functionally distinct fractions.\nC. The evidence does not state that this review synthesizes current knowledge on XOS and MOS, with emphasis on enzyme-enabled production technologies, microbial fermentation behavior, and health outcomes in humans and animals.\nD. Next-generation prebiotics are not increasingly recognized for their ability to modulate gut microbiota and promote host health.\nE. Xylo-oligosaccharides (XOS) and malto-oligosaccharides (MOS), derived from cereal biomass, are particularly promising due to their structural stability, low effective dosages, and selective stimulation of beneficial gut microorganisms.\nF. Recent advances in tailored enzyme cocktails and sequential bioconversion strategies enable precise control over oligosaccharide yield and degree of polymerization, facilitating targeted production of functionally distinct fractions.\nG. Xylo-oligosaccharides (XOS) and malto-oligosaccharides (MOS), derived from cereal biomass, are not particularly promising due to their structural stability, low effective dosages, and selective stimulation of beneficial gut microorganisms.\nH. Next-generation prebiotics are increasingly recognized for their ability to modulate gut microbiota and promote host health.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13046717", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13046717/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-616cf0f10f27a9594f95", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIn biotechnological processes, cell density and physiology are critical parameters for controlling the feed rate, harvest time, and process performance. We developed an automated flow cytometry approach that enables continuous, real-time (fully automated, hourly) monitoring of bacterial populations in continuous bioreactors. The method employed a double-staining protocol that combined DAPI to assess total DNA content and Alexa Fluor 488-EdU via Click-iT technology to identify the proportions of cells undergoing active DNA replication through EdU incorporation. The integrated workflow included fixation, permeabilization, staining, and measurement steps and was applied to three Gram-negative strains: Bradyrhizobium sp., Escherichia coli , and Stenotrophomonas rhizophila . Automated analysis captured growth dynamics and cell cycle progression, providing insights into population behavior under different dilution rates. In this study, automated on-line sampling enabled hourly flow cytometry measurements of cell concentration and physiological indicators during continuous cultivation, supporting real-time monitoring and control in industrial biotechnology.\n\nCandidates:\nA. The method employed a double-staining protocol that combined DAPI to assess total DNA content and Alexa Fluor 489-EdU via Click-iT technology to identify the proportions of cells undergoing active DNA replication through EdU incorporation.\nB. The evidence does not state that we developed an automated flow cytometry approach that enables continuous, real-time (fully automated, hourly) monitoring of bacterial populations in continuous bioreactors.\nC. The integrated workflow included fixation, permeabilization, staining, and measurement steps and was not applied to three Gram-negative strains: Bradyrhizobium sp., Escherichia coli , and Stenotrophomonas rhizophila .\nD. The integrated workflow included fixation, permeabilization, staining, and measurement steps and was applied to three Gram-negative strains: Bradyrhizobium sp., Escherichia coli , and Stenotrophomonas rhizophila .\nE. The method employed a double-staining protocol that combined DAPI to assess total DNA content and Alexa Fluor 488-EdU via Click-iT technology to identify the proportions of cells undergoing active DNA replication through EdU incorporation.\nF. In biotechnological processes, cell density and physiology are critical parameters for controlling the feed rate, harvest time, and process performance.\nG. We developed an automated flow cytometry approach that enables continuous, real-time (fully automated, hourly) monitoring of bacterial populations in continuous bioreactors.\nH. In biotechnological processes, cell density and physiology are not critical parameters for controlling the feed rate, harvest time, and process performance.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13046813", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13046813/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4f3f2c64da928dfc09d1", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"65%\", \"3%\", \"37 °C\", \"64%\", \"101 rpm\", \"4%\", \"38 °C\", \"100 rpm\"]\n\nEvidence:\nThis study investigates the optimisation of poly(3-hydroxybutyrate) (PHB) production by Mycolicibacterium smegmatis using sugarcane bagasse (SCB) hydrolysate as a low-cost, renewable carbon source. Key fermentation parameters including temperature, pH, agitation, inoculum size and nitrogen supplementation were optimised to enhance biomass growth and PHB accumulation. Under optimised conditions (37 °C, pH 7, 100 rpm, 3% inoculum), M. smegmatis achieved a maximum PHB content of 64% of DCW from SCB hydrolysate, corresponding to a PHB titre of ~ 1.0 g L −1 and a volumetric productivity of 0.014 g L −1 h −1 after 72 h of cultivation, with yeast extract identified as the most effective nitrogen source. Thin-layer chromatography (TLC) analysis demonstrated that M. smegmatis could not only co-utilise glucose and xylose simultaneously but preferentially consumed xylose, which is a major advantage when processing lignocellulosic biomasses, where xylose is abundant and typically underutilised by many microorganisms. Whole-genome sequencing with Oxford Nanopore Technologies (ONT) confirmed the presence of PHB production genes ( phbA , phbB , phbC ) and a xylose-utilisation pathway, supporting its metabolic capability. These findings establish M. smegmatis as a promising candidate for converting agricultural waste into biodegradable bioplastics, contributing to circular bioeconomy strategies. The online version contains supplementary material available at 10.1186/s40643-026-01047-y.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13046884", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13046884/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4c331bfaffa5e893062a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMembrane fouling during ultrafiltration/diafiltration (UF/DF) remains a major challenge for extending membrane lifetime in biologics manufacturing. Developing cleaning strategies that combine high efficiency with scalability is essential for robust downstream processing. In this study, different UF/DF membrane cleaning methods were assessed under bench-scale conditions with consideration for large-scale implementation. Permeate-closed cleaning (PCC), which temporarily closes the permeate port to enhance shear, effectively mitigated normalized water permeability (NWP) decline. Furthermore, forward and reverse PCC at elevated feed flux achieved comparable fouling control with fewer steps by leveraging hydrodynamic shear and localized backwashing. To simplify large-scale adoption, high-flux PCC was developed and demonstrated the ability to restore NWP and maintain stability over extended cycles. Quantitatively, high‑flux PCC reduced early NWP decay by ~ 18% over the first three cycles and recovered > 15% NWP in previously fouled membranes, stabilizing permeability over extended reuse. Backwashing under controlled negative transmembrane pressure (TMP) further improved pore-level cleaning and extended membrane usability. Additionally, combining sodium hypochlorite (NaClO) with sodium hydroxide (NaOH) provided strong oxidative and alkaline action; within the evaluated range (25–150 ppm NaClO), 50 ppm was identified as a practical lower limit for effective recovery, while 150 ppm restored NWP from ~ 70 to ~ 100% with residual NaClO < 0.02 ppm after final rinsing. Collectively, these findings present a portfolio of cleaning strategies—hydrodynamic, chemical, and combined approaches—that enhance cleaning efficiency, reduce chemical exposure, and support sustainable UF/DF operations in biologics manufacturing. The online version contains supplementary material available at 10.1186/s40643-026-01045-0. Keywords: Ultrafiltration/diafiltration (UF/DF), Membrane fouling, Cleaning strategies, Permeate-closed cleaning, Backwashing, Biologics downstream processing\n\nCandidates:\nA. The evidence does not state that membrane fouling during ultrafiltration/diafiltration (UF/DF) remains a major challenge for extending membrane lifetime in biologics manufacturing.\nB. In this study, different UF/DF membrane cleaning methods were not assessed under bench-scale conditions with consideration for large-scale implementation.\nC. Membrane fouling during ultrafiltration/diafiltration (UF/DF) remains a major challenge for extending membrane lifetime in biologics manufacturing.\nD. Developing cleaning strategies that combine high efficiency with scalability is not essential for robust downstream processing.\nE. Permeate-closed cleaning (PCC), which temporarily closes the permeate port to enhance shear, effectively mitigated normalized water permeability (NWP) decline.\nF. The evidence does not state that permeate-closed cleaning (PCC), which temporarily closes the permeate port to enhance shear, effectively mitigated normalized water permeability (NWP) decline.\nG. In this study, different UF/DF membrane cleaning methods were assessed under bench-scale conditions with consideration for large-scale implementation.\nH. Developing cleaning strategies that combine high efficiency with scalability is essential for robust downstream processing.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13046915", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13046915/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4422b914523b1f0d4934", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAcacia seyal is a medicinal plant rich in bioactive compounds known for their antimicrobial, antioxidant, and anticancer properties. Conventional plant extracts often suffer from poor stability and limited bioavailability, reducing their therapeutic effectiveness. The utilization of chitosan nanoparticles (CS-NPs) offers a promising strategy to enhance biological activity of plant extracts. Encapsulation of A. seyal seeds extract (ASSE) in CS-NPs (ASSE@CS-NPs) was the aim of the present investigation to compete Helicobacter pylori and HCT116 cancer cells. The phytochemical profile of ASSE revealed diverse phenolic acids and flavonoids via HPLC analysis. Methyl gallate was dominated (20.98 mg/g), followed by gallic acid, and catechin. FTIR analysis confirmed characteristic functional groups in ASSE and CS-NPs. Peak shifts and intensity variations in ASSE@CS-NPs indicated successful encapsulation through hydrogen bonding and electrostatic interactions. ASSE@CS-NPs showed the strongest anti- H. pylori effect with inhibition zone (23.7 mm) and lowest MIC/MBC (15.62 µg/mL). Compared to CS-NPs (20.7 mm, 31.25 µg/mL) and ASSE (19.7 mm, 31.25 µg/mL). ASSE@CS-NPs showed notable antioxidant activity, achieving 96% scavenging at 1000 µg/mL with IC 50 of 3µg/mL compared to ASSE (5 µg/mL) and CS-NPs (74 µg/mL). ASSE@CS-NPs exhibited potent cytotoxicity against HCT116 cells with IC 50 of 23 µg/mL, outperforming ASSE (588 µg/mL) and CS-NPs (120 µg/mL). The formulation achieved > 80% inhibition at 62.5 µg/mL. Molecular docking was performed to evaluate the binding affinity and interaction patterns of methyl gallate (a main constituent of ASSE) and chitosan against H. pylori urease (PDB ID: 6ZJA). Docking results revealed favorable binding scores for both ligands, with chitosan showing higher affinity (S score: − 6.42 kcal/mol) compared to methyl gallate (S score: − 5.45 kcal/mol). Key hydrogen bond interactions were observed with active site residues ASP362 and ALA169 for methyl gallate, and ASP223 and HIS323 for chitosan. These results suggest that ASSE@CS-NPs possess inhibitory potential against H. pylori and HCT116 cells. The online version contains supplementary material available at 10.1186/s40643-026-01031-6.\n\nCandidates:\nA. The evidence does not state that conventional plant extracts often suffer from poor stability and limited bioavailability, reducing their therapeutic effectiveness.\nB. seyal seeds extract (ASSE) in CS-NPs (ASSE@CS-NPs) was the aim of the present investigation to compete Helicobacter pylori and HCT116 cancer cells.\nC. The evidence does not state that the utilization of chitosan nanoparticles (CS-NPs) offers a promising strategy to enhance biological activity of plant extracts.\nD. Acacia seyal is a medicinal plant rich in bioactive compounds known for their antimicrobial, antioxidant, and anticancer properties.\nE. seyal seeds extract (ASSE) in CS-NPs (ASSE@CS-NPs) was the aim of the present investigation to compete Helicobacter pylori and HCT117 cancer cells.\nF. Conventional plant extracts often suffer from poor stability and limited bioavailability, reducing their therapeutic effectiveness.\nG. Acacia seyal is not a medicinal plant rich in bioactive compounds known for their antimicrobial, antioxidant, and anticancer properties.\nH. The utilization of chitosan nanoparticles (CS-NPs) offers a promising strategy to enhance biological activity of plant extracts.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13047003", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13047003/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2d1cdf39b52c1bfd70e4", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe use of animal-derived reagents in biomedical research poses challenges for reproducibility due to batch-to-batch variability and inter-species differences, along with ethical concerns related to their origin. In pursuing a human-relevant in vitro model, an animal-free and defined cell culture process is preferred to improve relevance and reproducibility. We investigated the use of serum replacement (SR) consisting of human hepatocyte-derived proteins in cell culture and recombinant antibodies with a plant-derived blocking solution (animal-free blocker, AFB) in immunocytochemical staining of cells. Human serum (HS) instead of animal-derived serum was used in this study for comparison with SR. We showed that bone marrow stem/stromal cells (BMSCs) maintain their proliferation capacity and cell-specific morphology in SR-supplemented medium, whereas human umbilical vein endothelial cells (HUVECs) show compromised growth under similar conditions. In a more complex co-culture, BMSCs + HUVECs formed a stable vascular network in SR-supplemented medium. In immunocytochemical staining, we compared the performance of recombinant antibodies with animal-derived antibodies and an AFB solution with a bovine serum albumin (BSA)-based blocking solution. Adipose stem/stromal cells (ASCs) showed their typical spindle-shaped morphology when stained with recombinant antibodies against alpha-smooth muscle actin (αSMA) in both AFB and BSA-based blocking solutions. We detected partial non-specific binding of recombinant antibodies and animal-derived antibodies against β-tubulin III in ASC. In contrast, we did not observe non-specific binding on these neuronal antibodies in HUVECs in any tested condition. While protocol optimization depends on the cell type used, our findings indicate that animal-derived materials can reliably be replaced.\n\nCandidates:\nA. The evidence does not state that the use of animal-derived reagents in biomedical research poses challenges for reproducibility due to batch-to-batch variability and inter-species differences, along with ethical concerns related to their origin.\nB. In pursuing a human-relevant in vitro model, an animal-free and defined cell culture process is preferred to improve relevance and reproducibility.\nC. We investigated the use of serum replacement (SR) consisting of human hepatocyte-derived proteins in cell culture and recombinant antibodies with a plant-derived blocking solution (animal-free blocker, AFB) in immunocytochemical staining of cells.\nD. Human serum (HS) instead of animal-derived serum was used in this study for comparison with SR.\nE. Human serum (HS) instead of animal-derived serum was not used in this study for comparison with SR.\nF. In pursuing a human-relevant in vitro model, an animal-free and defined cell culture process is not preferred to improve relevance and reproducibility.\nG. The use of animal-derived reagents in biomedical research poses challenges for reproducibility due to batch-to-batch variability and inter-species differences, along with ethical concerns related to their origin.\nH. The evidence does not state that we investigated the use of serum replacement (SR) consisting of human hepatocyte-derived proteins in cell culture and recombinant antibodies with a plant-derived blocking solution (animal-free blocker, AFB) in immunocytochemical staining of cells.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13047193", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13047193/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-19298302a281b97e9dd1", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"4 h\", \"93%\", \"18 h\", \"19 h\", \"2 h\", \"94%\", \"5 h\", \"3 h\"]\n\nEvidence:\nCorynebacterium glutamicum is a major industrial cell factory for amino acid production. Acidic byproduct accumulation can lower broth pH, disrupt cytoplasmic pH homeostasis, induce oxidative stress, and ultimately compromise productivity. As low-pH stress is dynamic and process-dependent, distinguishing responses to acute acid shock versus sustained acidification is important for improving strain robustness. Here, we investigated low-pH adaptation under two regimes: acute, unbuffered pH 4.0 shock and sustained pH 5.5 stress with pH control, by integrating viability assays, time-series RNA-seq, and genetic validation. Acute shock caused 93% viability loss within 2 h, followed by broth neutralisation and regrowth, whereas sustained stress led to a 4.1-log 10 decline over 18 h. Transcriptomics identified 905/847 differentially expressed genes at 1/4 h under acute shock and 643/608/1857 genes at 1/8/18 h under sustained stress. Shared responses included repression of central metabolism and induction of β-ketoadipate catabolism, potassium uptake, sodium/proton antiport, urease-mediated ammonium release, amino acid biosynthesis, and oxidative and membrane-stress defences. Acute shock showed rapid global reprogramming dominated by oxidative protection, chaperone induction, and ion-flux control, whereas sustained stress induced progressive metabolic rewiring, cell envelope reinforcement, and redox buffering. Functional validation using both overexpression and knockout mutants confirmed the contribution of ion transport, iron regulation, β-ketoadipate metabolism, urease, and respiratory modules to low-pH tolerance in a regime-dependent manner. This study provides the first time-resolved, regime-specific transcriptomic dissection of low-pH adaptation in C. glutamicum and identifies key modules for engineering acid-resilient strains.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13049421", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13049421/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6ccb59352550dc0a4069", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nThermophilic methanogens of the genus Methanothermobacter are established biocatalysts in power-to-gas applications, converting H 2 and CO 2 into CH 4 through the process of methanogenesis. Further expanding this platform for the bioproduction of value-added compounds (power-to-x) has the potential to increase the economic viability of such processes. This requires a genetic toolset that enables the controlled expression of recombinant pathways. Here, we report the fully autotrophic inducible recombinant bioproduction of acetoin from H 2 and CO 2 in Methanothermobacter thermautotrophicus ΔH. To facilitate inducible gene expression, we implemented an anhydrotetracycline (aTc)-inducible promoter system, expanding our available set of promoters. The aTc-inducible system enabled controlled expression of a codon-optimized acetoin-production operon comprising the acetolactate synthase- and acetolactate decarboxylase-encoding genes from Streptococcus thermophilus . Batch cultivation at 42 °C demonstrated aTc-dependent acetoin formation, yielding up to 0.45 ± 0.08 mM acetoin. Fed-batch bioreactor experiments confirmed growth-coupled, recombinant acetoin production, while eliminating the non-specific acetoin accumulation that we observed during non-growth phases in batch cultivation. Continuous cultivation in a chemostat resulted in stable acetoin production rates of 1.28 ± 0.07 μmol L −1 h −1 (0.11 ± 0.01 mg L −1 h −1 ) at 42 °C. Elevated temperatures led to reduced acetoin production, suggesting diminished activity or thermal instability of the heterologous enzymes. This study demonstrates the feasibility of value-added bioproduction in Methanothermobacter and establishes an inducible expression system suitable for pathway engineering in thermophilic methanogens. Together with genome-scale modeling and emerging enzyme engineering strategies, these results lay the foundation for developing robust, CH 4 -co-producing power-to-x bioprocesses with Methanothermobacter species. Keywords: Methanogenic archaea, Genetic engineering, Inducible promoter system, Bioreactors, Chemostat, Recombinant acetoin production", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13049647", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13049647/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-070859428cb21f3c5993", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe immortalized NK-92 cell line is widely used to study natural killer (NK) cell biology and develop immunotherapies. NK-92 cells exhibit strong cytotoxic activity against tumor and virus-infected cells and are often used as a functional surrogate for primary NK cells. Beyond research applications, NK-92 cells are currently being evaluated in clinical trials as an allogeneic, off-the-shelf cell therapy. NK cells are typically cultured in static systems, which limits scalability. For clinical and commercial applications, large-scale cell expansion requires scalable platforms, such as bioreactors. However, conventional bubble-aerated bioreactors generate shear stress and foam, which can impair proliferation during prolonged culture and compromise cell quality. To address these limitations, a membrane-based stirring and aeration system was compared to a conventional pitched-blade impeller with microsparger aeration in 2 L stirred-tank bioreactors. NK-92 cells were expanded from static pre-cultures into shake flasks and subsequently inoculated into each bioreactor with continuous feeding to maintain ideal nutrient supply. Both systems supported comparable growth, viability, and metabolic profiles. Cells showed comparable growth, viability and metabolism profile in both systems. However, cells expanded in the membrane-based system exhibited markedly higher cytotoxicity and cytotoxic capacity. Computational fluid dynamic simulations of both systems suggest that this observation is likely attributable to the more homogeneous shear distribution in the membrane-stirred setup compared to the pitched-blade configuration. Overall, this work presents a novel cultivation method to produce highly cytotoxic NK-92 cells in a well scalable stirred-tank bioreactor platform for allogeneic off-the-shelf cell therapy.\n\nCandidates:\nA. The immortalized NK-92 cell line is widely used to study natural killer (NK) cell biology and develop immunotherapies.\nB. Beyond research applications, NK-92 cells are currently being evaluated in clinical trials as an allogeneic, off-the-shelf cell therapy.\nC. NK cells are not typically cultured in static systems, which limits scalability.\nD. NK cells are typically cultured in static systems, which limits scalability.\nE. NK-93 cells exhibit strong cytotoxic activity against tumor and virus-infected cells and are often used as a functional surrogate for primary NK cells.\nF. The immortalized NK-93 cell line is widely used to study natural killer (NK) cell biology and develop immunotherapies.\nG. NK-92 cells exhibit strong cytotoxic activity against tumor and virus-infected cells and are often used as a functional surrogate for primary NK cells.\nH. Beyond research applications, NK-93 cells are currently being evaluated in clinical trials as an allogeneic, off-the-shelf cell therapy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13050783", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13050783/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c104f3b160ef5173a3be", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"2 L\", \"81%\", \"80%\", \"2.7%\", \"100%\", \"3 L\", \"3.7%\", \"99%\"]\n\nEvidence:\nWastewater treatment faces increasing pressure to transition from energy-intensive technology to sustainable alternatives aligned with global resource efficiency and climate goals. Microalgae-based processes have emerged as promising solutions in environmental remediation applications; however, their large-scale deployment remains constrained by contamination risks, stringent operational requirements, and high downstream costs. These challenges are particularly evident in the treatment of nutrient-rich industrial influents such as dairy wastewater, which represents an environmental concern. Addressing this gap is important for strengthening overall climate action efforts and safeguarding ecosystems by reducing greenhouse gas emissions and transforming nutrient loads from pollution sources into potential resources. In this study, a two-stage biological treatment of raw dairy wastewater was tested as an alternative to conventional technology. The process relied on activated algae biomass, consisting of microalgae-bacteria consortia, operated in sequencing batch mode. The treatment stages were strategically designed to address elevated organic and ammonium loads while maintaining aerobic conditions exclusively through photosynthesis. The first stage operated at high COD loadings (>1 g O 2 L) and achieved organic matter removal above 80%, while the second stage, adapted to lower COD (<0.5 g O 2 L), ensured residual ammonium below the detection limit and overall COD removal up to 99%. Optimization of operational conditions further improved microalgae harvesting efficiency (from 88.6 ± 2.7% to 94.4 ± 1.8%) and enhanced floc stability through diversification of microalgae communities. Complementary, microfauna analysis outlined the presence of protozoan and metazoan populations confirming process stability and ecological balance comparable to traditional activated sludge system. The findings demonstrate potential of the activated algae system as a resilient and resource-efficient alternative to conventional wastewater treatment technology. By avoiding energy demand for mechanical aeration and ensuring nutrient recovery in line with environmental regulatory frameworks, the developed process supports sustainable wastewater treatment management while aligning with international goals on climate change mitigation and aquatic ecosystems protection.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13050919", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13050919/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2532d6145c21575ad19e", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCurrent academic research and industrial processes for cultivated meat production primarily utilize cultures of single cell types (monocultures) that do not capture the complexity of the in vivo environment of muscle and fatty tissues in the body. These systems are simplified for operational practicalities and cost reduction rather than to improve product quality. Irrespective of the manufacturing process, a food product must be of high quality to survive in competitive markets. Co‐culture of multiple cell types is a well‐established practice that enhances tissue qualities by allowing the cells to exchange cytokines and nutrients and to engage in cell‐cell signaling. These synergies promote tissue function and phenotype stability and alter cellular and tissue composition. Co‐culture can improve processes by reducing the need for exogenous media components such as growth factors. Expanding this system to include the products of rumen fermentation, such as B‐vitamins and short‐chain fatty acid (SCFA) has potential to further improve culture conditions and product qualities, particularly for cultivated meat of ruminant species (e.g., cattle, goats, sheep, and lambs). In this review, we cover the role of multiple cell types (notably fibroblasts and liver cells) as well as the role of rumen fermentation products in supporting the development and quality of the desired muscle and fatty tissues. We identify the opportunities and highlight the challenges associated with connecting these cultures as modules in a multi‐organ bioreactor series to further enable synergies and improve product qualities as part of a novel, bio‐inspired, multi‐organ production system.\n\nCandidates:\nA. The evidence does not state that irrespective of the manufacturing process, a food product must be of high quality to survive in competitive markets.\nB. These systems are not simplified for operational practicalities and cost reduction rather than to improve product quality.\nC. Co‐culture of multiple cell types is not a well‐established practice that enhances tissue qualities by allowing the cells to exchange cytokines and nutrients and to engage in cell‐cell signaling.\nD. Irrespective of the manufacturing process, a food product must be of high quality to survive in competitive markets.\nE. These systems are simplified for operational practicalities and cost reduction rather than to improve product quality.\nF. The evidence does not state that current academic research and industrial processes for cultivated meat production primarily utilize cultures of single cell types (monocultures) that do not capture the complexity of the in vivo environment of muscle and fatty tissues in the body.\nG. Co‐culture of multiple cell types is a well‐established practice that enhances tissue qualities by allowing the cells to exchange cytokines and nutrients and to engage in cell‐cell signaling.\nH. Current academic research and industrial processes for cultivated meat production primarily utilize cultures of single cell types (monocultures) that do not capture the complexity of the in vivo environment of muscle and fatty tissues in the body.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13051749", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13051749/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0c10a9650efe7e16a1cb", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAvian pathology is the scientific study of diseases in birds, focusing on the structural, functional and molecular changes in tissues and organs caused by infections, toxins, nutritional deficiencies or others. It plays a critical role in maintaining poultry health, ensuring food security and supporting economic growth. The aim of this review is to highlight current trends, challenges and future directions in avian pathology, with a special emphasis on advancements in diagnostic approaches that enhance avian disease detection and management. However, the practical application of advanced technologies in avian pathology remains limited, particularly in Ethiopia. Recent diagnostic advancements, including immunohistochemistry, molecular techniques as well as digital pathology, have improved the detection, characterisation and management of poultry diseases. Future directions emphasise the use of artificial intelligence (AI) and machine learning for accurate diagnostics, real‐time disease monitoring and outbreak prediction. Ethiopia has achieved significant progress in avian pathology, particularly through polymerase chain reaction and histopathology. Despite ongoing advancements, the poultry industry continues to face challenges, including emerging and re‐emerging pathogens, limited access to diagnostic infrastructure, zoonotic risks and antimicrobial resistance. Therefore, strengthening biosecurity practices, promoting responsible antimicrobial use and expanding the use of molecular, digital pathology and AI‐supported diagnostic tools remain essential strategies for protecting both poultry population and public health. To further enhance disease detection and control, diagnostic capacity and professional training in avian pathology should be strengthened in Ethiopia.\n\nCandidates:\nA. The evidence does not state that it plays a critical role in maintaining poultry health, ensuring food security and supporting economic growth.\nB. It plays a critical role in maintaining poultry health, ensuring food security and supporting economic growth.\nC. The evidence does not state that however, the practical application of advanced technologies in avian pathology remains limited, particularly in Ethiopia.\nD. The aim of this review is to highlight current trends, challenges and future directions in avian pathology, with a special emphasis on advancements in diagnostic approaches that enhance avian disease detection and management.\nE. Avian pathology is not the scientific study of diseases in birds, focusing on the structural, functional and molecular changes in tissues and organs caused by infections, toxins, nutritional deficiencies or others.\nF. The aim of this review is not to highlight current trends, challenges and future directions in avian pathology, with a special emphasis on advancements in diagnostic approaches that enhance avian disease detection and management.\nG. However, the practical application of advanced technologies in avian pathology remains limited, particularly in Ethiopia.\nH. Avian pathology is the scientific study of diseases in birds, focusing on the structural, functional and molecular changes in tissues and organs caused by infections, toxins, nutritional deficiencies or others.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13051840", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13051840/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-99dc04384a035f6deba8", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis study explores an integrated bioprocessing approach to enhance the bioactivity of Chlorella vulgaris biomass through enzymatic pretreatment and lactic acid fermentation. Three pretreatment strategies—ultrasound-assisted hydrolysis, hydrothermal treatment, and enzymatic hydrolysis using Viscozyme, Alcalase, or their combination- were evaluated for their ability to release fermentable sugars and proteins. Enzymatic hydrolysis proved most effective, yielding up to 151.18 mg g DW − 1 of reducing sugars and 14.4 mg g DW − 1 of protein. Subsequent fermentation with Lactiplantibacillus plantarum and Levilactobacillus brevis demonstrated robust microbial growth (Δ log CFU ≈ 4.0) and significant acidification (pH 3.6–4.2), accompanied by lactic acid production up to 17.35 g/L. Functional characterisation revealed that combined enzymatic pretreatment and fermentation improved antioxidant properties, reaching values of 8.64, 19.46, 1.45 mg Trolox g DW⁻¹ in the CUPRAC, ABTS, and DPPH assays, respectively, while TPC reached 9.67 GAE g DW⁻¹. Moreover, Viscozyme-pretreated, L. plantarum -fermented samples showed inhibitory zones under non-neutralised screening conditions against Gram-positive bacteria. These findings highlight the combined effect of enzymatic pretreatment and microbial fermentation in enhancing the biofunctional value of C. vulgaris , offering a sustainable strategy for developing natural antioxidants and antimicrobial agents for food, nutraceutical, and cosmetic applications. The online version contains supplementary material available at 10.1186/s40643-026-01050-3. Keywords: Microalgae, Chlorella vulgaris , Fermentation, Lactic acid bacteria, Enzyme pretreatment, Antioxidant capacity\n\nCandidates:\nA. Subsequent fermentation with Lactiplantibacillus plantarum and Levilactobacillus brevis demonstrated robust microbial growth (Δ log CFU ≈ 4.0) and significant acidification (pH 3.6–4.2), accompanied by lactic acid production up to 17.35 g/L.\nB. The evidence does not state that this study explores an integrated bioprocessing approach to enhance the bioactivity of Chlorella vulgaris biomass through enzymatic pretreatment and lactic acid fermentation.\nC. Enzymatic hydrolysis proved most effective, yielding up to 151.18 mg g DW − 1 of reducing sugars and 14.4 mg g DW − 1 of protein.\nD. Three pretreatment strategies—ultrasound-assisted hydrolysis, hydrothermal treatment, and enzymatic hydrolysis using Viscozyme, Alcalase, or their combination- were evaluated for their ability to release fermentable sugars and proteins.\nE. This study explores an integrated bioprocessing approach to enhance the bioactivity of Chlorella vulgaris biomass through enzymatic pretreatment and lactic acid fermentation.\nF. Enzymatic hydrolysis proved most effective, yielding up to 152.18 mg g DW − 1 of reducing sugars and 14.4 mg g DW − 1 of protein.\nG. Three pretreatment strategies—ultrasound-assisted hydrolysis, hydrothermal treatment, and enzymatic hydrolysis using Viscozyme, Alcalase, or their combination- were not evaluated for their ability to release fermentable sugars and proteins.\nH. Subsequent fermentation with Lactiplantibacillus plantarum and Levilactobacillus brevis demonstrated robust microbial growth (Δ log CFU ≈ 5.0) and significant acidification (pH 3.6–4.2), accompanied by lactic acid production up to 17.35 g/L.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13053731", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13053731/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dd291b1bc4ffa41fd0cd", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCultivated meat represents a substitute for traditional livestock farming through the external cultivation of animal cells. The technology is still in its infancy and requires continued research and development to achieve commercial viability. This analysis offers an overview of cultivated meat's current standing by examining its nutritional value and safety and comparing it with traditional meat options. The study examines both commercial viability and regulatory hurdles for market entry as well as consumer acceptance and psychological obstacles to adoption. The discussion encompasses food safety concerns, production costs, market opportunities, global regulatory approaches, and industry‐leading company trends in the cultivated meat field. The analysis presents key technological challenges and solutions while examining changes in consumer mindsets, besides sustainability and ethical issues, which remain crucial yet evolving aspects of cultivated meat development. The expanding global population has led to cultivated meat being recognized as a vital sustainable solution for future food security.\n\nCandidates:\nA. The evidence does not state that cultivated meat represents a substitute for traditional livestock farming through the external cultivation of animal cells.\nB. This analysis offers an overview of cultivated meat's current standing by examining its nutritional value and safety and comparing it with traditional meat options.\nC. The evidence does not state that this analysis offers an overview of cultivated meat's current standing by examining its nutritional value and safety and comparing it with traditional meat options.\nD. The evidence does not state that the study examines both commercial viability and regulatory hurdles for market entry as well as consumer acceptance and psychological obstacles to adoption.\nE. The study examines both commercial viability and regulatory hurdles for market entry as well as consumer acceptance and psychological obstacles to adoption.\nF. The technology is not still in its infancy and requires continued research and development to achieve commercial viability.\nG. The technology is still in its infancy and requires continued research and development to achieve commercial viability.\nH. Cultivated meat represents a substitute for traditional livestock farming through the external cultivation of animal cells.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13054240", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13054240/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8099f2a960c80bc5e7a9", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis article showed the data of ocular clinical parameters, tear fluid cytokines, and topical cyclosporine-A 0.1% (Ikervis®) compliance in allogeneic hematopoietic stem cell transplant (allo-HSCT) patients. Study visits were conducted at 3–5 weeks pre-allo-HSCT (screening), pre-allo-HSCT, and 3/6/12 months post-allo-HSCT, with data collected at each time point. At each visit, tear samples were assessed for 96 cytokines by the Proximity Extension Assay O-linked target 96 platform.\n\nCandidates:\nA. Study visits were conducted at 3–5 weeks pre-allo-HSCT (screening), pre-allo-HSCT, and 3/6/12 months post-allo-HSCT, with data collected at each time point.\nB. At each visit, tear samples were assessed for 97 cytokines by the Proximity Extension Assay O-linked target 96 platform.\nC. This article showed the data of ocular clinical parameters, tear fluid cytokines, and topical cyclosporine-A 0.1% (Ikervis®) compliance in allogeneic hematopoietic stem cell transplant (allo-HSCT) patients.\nD. At each visit, tear samples were assessed for 96 cytokines by the Proximity Extension Assay O-linked target 96 platform.\nE. This article showed the data of ocular clinical parameters, tear fluid cytokines, and topical cyclosporine-A 1.1% (Ikervis®) compliance in allogeneic hematopoietic stem cell transplant (allo-HSCT) patients.\nF. Study visits were conducted at 4–5 weeks pre-allo-HSCT (screening), pre-allo-HSCT, and 3/6/12 months post-allo-HSCT, with data collected at each time point.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13054397", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13054397/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6778105adbc8640ffa7b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nA rapid assessment of manufacturability for drug candidates is crucial for advancing a prospective biotherapeutic from a candidate to a bulk drug substance. A lot‐to‐lot approach to manufacturability is adopted where each biologic batch is assessed for manufacturability as a bulk, unfractionated pool. Manufacturers may explore a more granular approach, independently enriching and evaluating the filterability of antibody variants within each lot, especially within the confines of relative hydrophobicity and surface charge. This study examined the use of bind‐and‐elute chromatography to alter the proportions of monoclonal antibody (mAb) proteoforms in eluate sub‐pools from a mixed‐mode chromatography resin‐packed column. Filterability of each sub‐pool through a virus‐retaining filter was subsequently examined. Circular dichroism and Fourier transform infrared spectroscopy were performed for each sub‐pool to probe for higher‐order structure differences between mAb variants enriched therein. Bioanalytical techniques were also used to assess colloidal stability, surface hydrophobicity, surface charge, and size differences. Results showed that basic charge variants, high‐mannose glycovariants, high relative hydrophobicity proteoforms, and high‐molecular‐weight species were enriched in the last‐eluting (terminal) sub‐pools. The first sub‐pool and the final sub‐pool showed the most fouling propensity on VPro virus filters. Circular dichroism showed that enriched proteoforms in the last sub‐pool possessed a higher percentage of bends. Most secondary structures did not vary significantly between sub‐pools. Diffusion interaction parameter was highly negative across all sub‐pools and the bulk unfractionated pool. These results provide a design space for identifying and depleting problematic mAb variants before the crucial virus filtration step. Keywords: chromatography, fractionation, mAbs, proteoforms, relative hydrophobicity variants\n\nCandidates:\nA. Manufacturers may explore a more granular approach, independently enriching and evaluating the filterability of antibody variants within each lot, especially within the confines of relative hydrophobicity and surface charge.\nB. The evidence does not state that this study examined the use of bind‐and‐elute chromatography to alter the proportions of monoclonal antibody (mAb) proteoforms in eluate sub‐pools from a mixed‐mode chromatography resin‐packed column.\nC. A rapid assessment of manufacturability for drug candidates is crucial for advancing a prospective biotherapeutic from a candidate to a bulk drug substance.\nD. A rapid assessment of manufacturability for drug candidates is not crucial for advancing a prospective biotherapeutic from a candidate to a bulk drug substance.\nE. A lot‐to‐lot approach to manufacturability is not adopted where each biologic batch is assessed for manufacturability as a bulk, unfractionated pool.\nF. A lot‐to‐lot approach to manufacturability is adopted where each biologic batch is assessed for manufacturability as a bulk, unfractionated pool.\nG. This study examined the use of bind‐and‐elute chromatography to alter the proportions of monoclonal antibody (mAb) proteoforms in eluate sub‐pools from a mixed‐mode chromatography resin‐packed column.\nH. The evidence does not state that manufacturers may explore a more granular approach, independently enriching and evaluating the filterability of antibody variants within each lot, especially within the confines of relative hydrophobicity and surface charge.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13055118", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13055118/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f98d22625b8cbf745d94", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCas‐CLOVER is an emerging high‐fidelity genome editing system that enables precise and efficient cell engineering. In this study, we applied Cas‐CLOVER to establish a robust, gene‐edited platform in suspension‐adapted CHO‐K1 cells supporting cell line development (CLD) for biopharmaceutical production. An attractive strategy for high‐yield clone selection is the use of glutamine synthetase (GS) knockout CHO cells. The primary GS gene resides on chromosome 5 (GS5), while a recently identified GS pseudogene is located on chromosome 1 (GS1). To compare editing efficiency, we evaluated Cas‐CLOVER and Cas9 at both GS loci using the Neon™ Transfection System. Cas‐CLOVER achieved 84% editing at GS5 and 74% at GS1, markedly higher than Cas9. Leveraging Cas‐CLOVER's dual‐guide RNA design, we generated a GS5 single knockout (GS5‐SKO) and subsequently a double knockout (GS‐DKO) line at both the GS5 and GS1 loci, both with none detected off‐target mutations analyzed in 40 predictably off‐target sites. For functional validation, these cell lines were engineered with the proprietary Harbor‐IN transposase system to stably express trastuzumab. Using an optimized protocol, the resulting GS‐DKO platform, termed CleanCut GS CHO, enabled stringent selection and yielded high‐producing clones with cell‐specific productivity exceeding 100 pg/cell/day and antibody titers greater than 5 g/L in 24 deep well‐plate fed‐batch cultures after 14 days. The antibody titer stability analysis showed consistency over 60 generations. Collectively, these findings establish Cas‐CLOVER as a versatile genome editing tool for developing high‐yield CHO host platforms in CLD. Keywords: bioprocessing, Cas‐CLOVER, cell line development, CHO cells, gene editing, Harbor‐IN transposase, trastuzumab\n\nCandidates:\nA. In this study, we applied Cas‐CLOVER to establish a robust, gene‐edited platform in suspension‐adapted CHO‐K2 cells supporting cell line development (CLD) for biopharmaceutical production.\nB. An attractive strategy for high‐yield clone selection is the use of glutamine synthetase (GS) knockout CHO cells.\nC. Cas‐CLOVER is not an emerging high‐fidelity genome editing system that enables precise and efficient cell engineering.\nD. The primary GS gene resides on chromosome 5 (GS5), while a recently identified GS pseudogene is located on chromosome 1 (GS1).\nE. In this study, we applied Cas‐CLOVER to establish a robust, gene‐edited platform in suspension‐adapted CHO‐K1 cells supporting cell line development (CLD) for biopharmaceutical production.\nF. Cas‐CLOVER is an emerging high‐fidelity genome editing system that enables precise and efficient cell engineering.\nG. An attractive strategy for high‐yield clone selection is not the use of glutamine synthetase (GS) knockout CHO cells.\nH. The primary GS gene resides on chromosome 6 (GS5), while a recently identified GS pseudogene is located on chromosome 1 (GS1).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13055120", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13055120/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fe9c39acd5f3bf75feb1", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"100%\", \"20 nm\", \"51 nm\", \"99%\", \"186 L\", \"50 nm\", \"185 L\", \"21 nm\"]\n\nEvidence:\nAs adeno‐associated viral vectors (AAV) continue to advance through the clinical pipeline, effective downstream purification strategies must be developed to ensure bulk drug purity and safety. AAV are produced within mammalian cells, bringing forth risks associated with viral contamination. Although existing downstream operations provide some degree of viral inactivation and removal, regulatory agencies have recommended the incorporation of a dedicated virus removal filtration step to ensure robust viral clearance. Recently published studies have demonstrated that membrane filters with nominal pore sizes between 35 and 50 nm can provide effective AAV transmission while removing larger viruses, although these results were obtained over a limited range of conditions. This study represents the first investigation into the effects of filtrate flux and process disruptions on virus reduction filtration for AAV. Experiments were performed using purified AAV capsids and carboxylate‐modified polymeric nanoparticles with a nominal diameter of 20 nm. Initial results confirmed that both systems exhibited nearly identical transient transmission profiles during virus filtration. Virus filtration performed at various filtrate fluxes (between 20 and 185 L/m 2 /h) revealed that moderately higher AAV yield may be obtained at lower fluxes. The data were analyzed using a modified internal polarization model, which was extended to account for the effects of process disruptions on transient particle transmission and recovery. Process disruptions were employed to increase AAV yield beyond 99% without compromising overall clearance of large viruses. At least a 4‐log reduction in xenotropic murine leukemia virus (XMuLV) was observed under all conditions tested, even following multiple process pauses. Keywords: adeno‐associated virus (AAV), downstream processing, internal polarization, process disruptions, viral clearance, virus filtration", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13055148", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13055148/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e33c2c4976b21deb007d", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nCAR T-cell therapy has become a transformative modality in oncology, demonstrating sustained clinical efficacy in hematologic malignancies. Recent investigations have expanded its potential beyond cancer to immune-mediated disorders, including autoimmune diseases such as multiple sclerosis and systemic lupus erythematosus, as well as chronic viral infections including HIV and hepatitis B. This review examines the mechanistic foundations of CAR-T cells, advances in universal and allogeneic engineering strategies designed to mitigate graft-versus-host disease and host rejection, and emerging in vivo gene-delivery platforms that aim to bypass conventional ex vivo manufacturing. We further evaluate safety-control architectures, including logic-gated and inducible systems, and discuss translational barriers related to scalability, manufacturing standardization, and long-term immune durability. While technological innovations in genome editing, synthetic biology, and computational design continue to refine CAR-T platforms, substantial biological and logistical challenges remain. A critical synthesis of these evolving strategies is necessary to distinguish incremental optimization from paradigm-shifting advances and to define the future trajectory of CAR-based immunotherapy across oncology and immune-mediated diseases.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13055603", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13055603/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ac3a2d244ed3f271e9b3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nImmune responses against recombinant adeno-associated virus (rAAV) are one of the major obstacles in gene therapy. We investigated the potential of Programmed Death 1 ligands 1 and 2 (PD-L1/2) to protect AAV-transduced cells from immunological clearance. Ligand compatibility for co-delivery was first evaluated using two transgenes, VEGF-B186 and muSEAP , separated from PD-L1/2 by a self-cleaving P2A peptide. After proper cleavage and biological activity of the co-produced proteins were demonstrated in vitro, the effect of PD-L1/2 co-expression on muSEAP production and persistence was studied in naïve and vector pre-immunized mice. Vectors (rAAV6-muSEAP, rAAV6-muSEAP-PD-L1, or rAAV6-muSEAP-PD-L2) were injected into two sites of the gastrocnemius muscle at a total dose of 1×10 10 vg. Co-delivery of PD-L1, particularly, significantly enhanced muSEAP secretion into the bloodstream up to 12 weeks despite elevated anti-AAV6 responses in pre-immunized mice. muSEAP secretion increased 33.3- and 31.4-fold with the co-delivery of PD-L1, while the increase was only 5.6- and 9.3-fold in the muSEAP control group at 5 and 12 weeks, respectively. Ligand-treated pre-immunized animals also had less T-cell infiltration into the treated muscle compared to naïve animals. In summary, co-delivery of PD-L1/2 alongside a transgene represents a promising strategy for achieving sustained gene expression in individuals pre-exposed to AAV.\n\nCandidates:\nA. Immune responses against recombinant adeno-associated virus (rAAV) are not one of the major obstacles in gene therapy.\nB. After proper cleavage and biological activity of the co-produced proteins were demonstrated in vitro, the effect of PD-L2/2 co-expression on muSEAP production and persistence was studied in naïve and vector pre-immunized mice.\nC. Ligand compatibility for co-delivery was first evaluated using two transgenes, VEGF-B187 and muSEAP , separated from PD-L1/2 by a self-cleaving P2A peptide.\nD. We investigated the potential of Programmed Death 1 ligands 1 and 2 (PD-L1/2) to protect AAV-transduced cells from immunological clearance.\nE. After proper cleavage and biological activity of the co-produced proteins were demonstrated in vitro, the effect of PD-L1/2 co-expression on muSEAP production and persistence was studied in naïve and vector pre-immunized mice.\nF. We investigated the potential of Programmed Death 2 ligands 1 and 2 (PD-L1/2) to protect AAV-transduced cells from immunological clearance.\nG. Ligand compatibility for co-delivery was first evaluated using two transgenes, VEGF-B186 and muSEAP , separated from PD-L1/2 by a self-cleaving P2A peptide.\nH. Immune responses against recombinant adeno-associated virus (rAAV) are one of the major obstacles in gene therapy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13056579", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13056579/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-900a993f77f25b4b9915", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIn this study we investigated the impact of a phage cocktail on Daphnia magna microbiome and the life‐history parameters. A mixture of four phages able to infect strains of Klebsiella pneumoniae , Enterobacter sp. and Pseudomonas aeruginosa was tested on three D. magna clones . The host‐associated microbiome composition in both the examined variants and the control was analysed using 16S rRNA amplicon sequencing. Additionally, the survival, growth rate, age, size at the first reproduction, and neonate per female were assessed. The analysis revealed minor shifts in microbial composition following phage exposure. Nevertheless, results showed that the phage cocktail increased microbiome diversity. None of the life‐history parameters studied were affected by the presence of the phage cocktail, and no adverse effects were observed. The results indicated that under laboratory conditions the phage cocktail is safe for D. magna and its microbiome.\n\nCandidates:\nA. The evidence does not state that a mixture of four phages able to infect strains of Klebsiella pneumoniae , Enterobacter sp.\nB. The host‐associated microbiome composition in both the examined variants and the control was analysed using 17S rRNA amplicon sequencing.\nC. and Pseudomonas aeruginosa was tested on three D.\nD. The evidence does not state that in this study we investigated the impact of a phage cocktail on Daphnia magna microbiome and the life‐history parameters.\nE. The host‐associated microbiome composition in both the examined variants and the control was analysed using 16S rRNA amplicon sequencing.\nF. In this study we investigated the impact of a phage cocktail on Daphnia magna microbiome and the life‐history parameters.\nG. A mixture of four phages able to infect strains of Klebsiella pneumoniae , Enterobacter sp.\nH. and Pseudomonas aeruginosa was not tested on three D.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13058927", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13058927/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b5d3b96ebb302b73c1e9", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSaponins are a structurally diverse plant glycosides with important ecological functions and broad pharmaceutical and industrial value. Recent advances have shifted saponin research from descriptive pathway elucidation toward predictive and programmable biomanufacturing. High-quality genome assemblies, integrated multi-omics profiling, and metabolic gene cluster analyses have clarified the enzymatic logic and regulatory architecture underlying saponin biosynthesis and structural diversification, enabling quantitative modeling of pathway flux and identification of key regulatory bottlenecks. Building on these foundations, synthetic biology tools, including CRISPR-based transcriptional modulation, synthetic promoters, and transcription factor rewiring, allow precise and programmable control of biosynthetic networks. In parallel, structure-guided enzyme engineering and AI-assisted protein design accelerate the optimization of cytochrome P450s and glycosyltransferases, improving catalytic efficiency and pathway robustness. These strategies are implemented across multiple production platforms, including engineered microbes, plant suspension cells, hairy roots, and adventitious root systems, enabling iterative optimization through Design-Build-Tes-Learn-cycles. Together, this review synthesizes recent conceptual and technological advances, positioning saponins as a model system that bridges gene networks, regulatory logic, and industrial biomanufacturing, and highlighting a generalizable framework for predictive design and scalable production of complex plant natural products.\n\nCandidates:\nA. Saponins are a structurally diverse plant glycosides with important ecological functions and broad pharmaceutical and industrial value.\nB. The evidence does not state that building on these foundations, synthetic biology tools, including CRISPR-based transcriptional modulation, synthetic promoters, and transcription factor rewiring, allow precise and programmable control of biosynthetic networks.\nC. Recent advances have shifted saponin research from descriptive pathway elucidation toward predictive and programmable biomanufacturing.\nD. The evidence does not state that high-quality genome assemblies, integrated multi-omics profiling, and metabolic gene cluster analyses have clarified the enzymatic logic and regulatory architecture underlying saponin biosynthesis and structural diversification, enabling quantitative modeling of pathway flux and identification of key regulatory bottlenecks.\nE. Saponins are not a structurally diverse plant glycosides with important ecological functions and broad pharmaceutical and industrial value.\nF. High-quality genome assemblies, integrated multi-omics profiling, and metabolic gene cluster analyses have clarified the enzymatic logic and regulatory architecture underlying saponin biosynthesis and structural diversification, enabling quantitative modeling of pathway flux and identification of key regulatory bottlenecks.\nG. Building on these foundations, synthetic biology tools, including CRISPR-based transcriptional modulation, synthetic promoters, and transcription factor rewiring, allow precise and programmable control of biosynthetic networks.\nH. The evidence does not state that recent advances have shifted saponin research from descriptive pathway elucidation toward predictive and programmable biomanufacturing.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13062210", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13062210/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f2b694fc1c47275c13f1", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMicroalgae are increasingly recognized as industrial biotechnology platforms for the sustainable production of high-value products (HVPs), including carotenoids, polyunsaturated fatty acids (PUFAs), polysaccharides, and phycobiliproteins. Their transition from exploratory biomass resources to precision metabolite-focused biofactories reflects growing industrial and regulatory consolidation. This review adopts a product-oriented perspective that moves beyond biomass productivity to evaluate how biological regulation, cultivation strategies, downstream processing, safety assessment, and regulatory readiness collectively determine industrial feasibility and commercialization potential. Rather than treating these components independently, the analysis frames them as interconnected determinants within a systems-level bioindustrial design framework. Emphasis is placed on stress-responsive biosynthesis, extraction and purification bottlenecks, mixotrophic cultivation, and strain engineering approaches as key enablers for improving productivity, process robustness, and cost performance. Carotenoids and omega-3 PUFAs are identified as the most industrially mature microalgal HVPs, supported by scalable production systems and regulatory acceptance, whereas polysaccharides and phycobiliproteins are highlighted as emerging products with expanding bioindustrial relevance. This comparative positioning underscores that industrial maturity depends not solely on biological potential, but on coordinated optimization across strain design, cultivation stability, downstream compatibility, and compliance pathways. By integrating technological innovation with safety and regulatory considerations, this review provides a coherent framework to support scalable, compliant, and sustainable microalgal biomanufacturing. Overall, the synthesis advances a rational roadmap for translating microalgal HVPs from laboratory optimization to economically viable and regulation-ready industrial deployment.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13062254", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13062254/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b172d542813ae5e450ff", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"72 %\", \"26 %\", \"233 h\", \"73 %\", \"27 %\", \"22 %\", \"21 %\", \"234 h\"]\n\nEvidence:\nBio-based carboxylic acids are key platform chemicals for a circular economy, offering sustainable alternatives to fossil-derived products. Yet, high substrate and processing costs, along with narrow profit margins, restrict industrial-scale bioproduction. In situ product removal (ISPR) holds the potential to increase productivity and yield in fermentations by circumventing product inhibition. Thus, it presents a promising process intensification measure to bridge the commercial gap between bio-based and petrochemical platform chemicals. One effective method for product recovery is reactive extraction with trioctylamine. In the present study, this method was applied to itaconic acid (ITA) fermentations with Ustilago cynodontis . First, the successful operation of dispersion-based apparatuses for reactive extraction was proven using small-scale mixer–settlers, with 1-octanol and the biocompatible 2-octanone as diluents. We then developed an improved feeding profile, lowering byproduct formation by 72 %. Subsequently, we demonstrated the feasibility of ISPR using a perfusion bioreactor with an external membrane coupled to reactive extraction and back-extraction. After 233 h of fermentation, the total amount of ITA produced was increased by 26 %, and productivity was 21 % higher compared to extended-batch fermentations. However, the yield was only slightly improved by 5 %. Ultimately, we identified product toxicity far below the maximum titer of 80 g \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {L}^{-1}$$\\end{document} L - 1 as a key bottleneck in ISPR fermentations with U. cynodontis . The results underscore the potential of ISPR and warrant further investigation in this field.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13063621", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13063621/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d00cda7a90eeb102ef85", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMicrobial proteases represent critical biocatalysts for industrial applications, yet optimizing their production remains challenging. This study isolated and characterized a high-yield protease-producing bacterium from environmental sources and systematically optimized production parameters. Among 124 bacterial isolates screened from six environmental sources, poultry waste exhibited the highest microbial diversity, yielding 63 morphologically diverse isolates. The most potent proteolytic isolate, P15, was identified through 16 S rRNA gene sequencing as Bacillus velezensis AZH3S (GenBank accession PX417380 ), sharing 99% sequence similarity with B. amyloliquefaciens . Initial screening of eight culture media identified glucose-casein-yeast extract medium as optimal, producing 442 U/mL protease activity. Time-course analysis revealed maximum enzyme production at 24 h (420 U/mL), with yield coefficients increasing to 480 U/g biomass in late stationary phase. Carbon and nitrogen source optimization demonstrated that starch (470 U/mL) and peptone (480 U/mL) were superior substrates. Response surface methodology employing Box-Behnken design across five variables (starch, peptone, pH, temperature, agitation speed) generated a highly predictive model (R² = 0.99) identifying agitation speed as the most influential parameter. Optimized conditions (9.84 g/L starch, 5.46 g/L peptone, pH 7.5, 33.1 °C, 244 rpm) achieved 1160.3 ± 9.20U/mL protease activity, representing a 2.80-fold improvement over initial production. Three-step purification achieved 5.82-fold enrichment with 32.0% yield. The purified enzyme exhibited a molecular mass of approximately 20 kDa with exceptional substrate affinity (Km = 0.207 mM, Vmax = 39.8 µmol/min), following classical Michaelis-Menten kinetics.These findings establish a robust bioprocess for industrial-scale protease production with significant biotechnological potential. The online version contains supplementary material available at 10.1186/s12866-026-04782-6.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13063675", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13063675/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c1ce0752ca3ce7e7aa01", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nSpace-based biomanufacturing has historically focused on long-duration crewed and exploration missions, but its greater potential lies in supporting in-space logistics, manufacturing, and servicing in Earth orbit. This article provides a strategic perspective on how shifting investments, policy, and R&D to orbital biomanufacturing could revolutionize defense, commercial, and civil sectors by enhancing supply chain resiliency, operational flexibility, ethical debris management, and commercial viability in Earth orbit.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13066452", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13066452/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2e169c4f4fdd1938a727", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBackground: Bacterial cellulose (BC), natively synthesized by Komagataeibacter spp., is a biodegradable biomaterial with superior mechanical properties. However, under agitated cultivation, cellulose-producing strains (Cel + ) often transition to nonproducing mutants (Cel − ), restricting scalability and hindering widespread use. Agitation-associated shear stress, elevated oxygen levels, and genetic mutations have been linked to the emergence of the Cel − phenotype. A genome-wide investigation that considers population heterogeneity and dynamics is essential to reveal the mutational landscape and evolutionary processes driving this phenotypic shift. Results: Over successive rounds of agitated cultivation, K. intermedius ENS15 transitioned to a planktonic state, losing BC production. Whole-genome sequencing revealed both structural variations (SVs) and nonstructural variations (NSVs). Contrary to the previous reports, SVs, including insertion sequence-mediated junction events, did not affect the genes related to BC synthesis. Instead, the accumulation and positive selection of NSVs, such as frameshift and replication slippage events in key BC-related genes, strongly correlated with the loss of cellulose synthesis. Conclusions: This study provides the first genome-wide population-level analysis revealing mutational dynamics underlying the BC phenotypic switch in Komagataeibacter spp. cultivated under agitated conditions. We show that the BC-related gene mutations are not solely driven by SVs, with NSVs emerging as equally critical contributors. Furthermore, genomic evidence suggests possible involvement of regulatory mechanisms, such as quorum sensing and cyclic dimeric guanosine monophosphate signaling, prompting future studies on regulatory processes under agitated cultivation conditions. These findings highlight the importance of examining genetic heterogeneity to understand phenotypic adaptation for strain improvement strategies.\n\nCandidates:\nA. A genome-wide investigation that considers population heterogeneity and dynamics is essential to reveal the mutational landscape and evolutionary processes driving this phenotypic shift.\nB. Agitation-associated shear stress, elevated oxygen levels, and genetic mutations have been linked to the emergence of the Cel − phenotype.\nC. Background: Bacterial cellulose (BC), natively synthesized by Komagataeibacter spp., is a biodegradable biomaterial with superior mechanical properties.\nD. The evidence does not state that agitation-associated shear stress, elevated oxygen levels, and genetic mutations have been linked to the emergence of the Cel − phenotype.\nE. The evidence does not state that however, under agitated cultivation, cellulose-producing strains (Cel + ) often transition to nonproducing mutants (Cel − ), restricting scalability and hindering widespread use.\nF. Background: Bacterial cellulose (BC), natively synthesized by Komagataeibacter spp., is not a biodegradable biomaterial with superior mechanical properties.\nG. A genome-wide investigation that considers population heterogeneity and dynamics is not essential to reveal the mutational landscape and evolutionary processes driving this phenotypic shift.\nH. However, under agitated cultivation, cellulose-producing strains (Cel + ) often transition to nonproducing mutants (Cel − ), restricting scalability and hindering widespread use.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13068021", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13068021/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ffc5409bfa48323bb84d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCells integrate exogenous and endogenous signals to grow, repair, or die. This is likely achieved through dynamic functional associations between genes, but measuring these relationships at scale is non-trivial. Here, we evaluate genetic associations in response to cell-cycle interruption, genotoxic perturbation, and nutrient deprivation using conditional genetic interaction (GI) mapping in human cells. In five maps measuring 250,000 GIs or higher-order environmental interactions, we discover widespread rewiring of relationships between genes, complexes, and ontologies across conditions. Specific bioprocesses drive the rewiring signal in each environmental state, as highlighted in our findings that the TIP60 and PP2A complexes radically alter their interaction profiles after inhibition of ATR. This resource reveals numerous genetic relationships for the fields of DNA damage signaling, DNA repair, and cell-cycle control and explores their context specificity. Our work advances a framework for using GI maps to explore environmental rewiring.\n\nCandidates:\nA. Cells integrate exogenous and endogenous signals to grow, repair, or die.\nB. This is likely achieved through dynamic functional associations between genes, but measuring these relationships at scale is non-trivial.\nC. This is not likely achieved through dynamic functional associations between genes, but measuring these relationships at scale is non-trivial.\nD. In five maps measuring 251,000 GIs or higher-order environmental interactions, we discover widespread rewiring of relationships between genes, complexes, and ontologies across conditions.\nE. Here, we evaluate genetic associations in response to cell-cycle interruption, genotoxic perturbation, and nutrient deprivation using conditional genetic interaction (GI) mapping in human cells.\nF. The evidence does not state that here, we evaluate genetic associations in response to cell-cycle interruption, genotoxic perturbation, and nutrient deprivation using conditional genetic interaction (GI) mapping in human cells.\nG. In five maps measuring 250,000 GIs or higher-order environmental interactions, we discover widespread rewiring of relationships between genes, complexes, and ontologies across conditions.\nH. The evidence does not state that cells integrate exogenous and endogenous signals to grow, repair, or die.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13068139", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13068139/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c96f5d345919fcc0cb88", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis data article presents a labelled flow-based network traffic dataset collected from a controlled Internet of Things (IoT) laboratory environment. The dataset captures network communication generated by Raspberry Pi-based IoT nodes configured to emulate service and client roles. Traffic was recorded during normal operations and during the execution of predefined cyberattack scenarios within an isolated experimental network. Network traffic was recorded at the packet level using passive network monitoring and stored in PCAPNG format. The packet captures were subsequently processed into bidirectional network flows, producing flow records with statistical and temporal attributes derived from the observed packet exchanges. Cyberattack-related flows were labelled using the experimental ground-truth markers recorded during each attack campaign, complemented by the fixed attacker node IP address. Flows outside the marked intervals were labelled as benign and corresponded to regular device communication. This combined labelling approach reduces the potential for overlap between benign and attack activities. The dataset covers nine attack scenarios grouped into six attack categories. It is released through a structured repository containing raw packet captures, labelled flow files, and supporting metadata for flow-based IoT traffic analysis, cyberattack detection research, and optional re-labelling.\n\nCandidates:\nA. The evidence does not state that this data article presents a labelled flow-based network traffic dataset collected from a controlled Internet of Things (IoT) laboratory environment.\nB. This data article presents a labelled flow-based network traffic dataset collected from a controlled Internet of Things (IoT) laboratory environment.\nC. The evidence does not state that the dataset captures network communication generated by Raspberry Pi-based IoT nodes configured to emulate service and client roles.\nD. Network traffic was recorded at the packet level using passive network monitoring and stored in PCAPNG format.\nE. Traffic was not recorded during normal operations and during the execution of predefined cyberattack scenarios within an isolated experimental network.\nF. Network traffic was not recorded at the packet level using passive network monitoring and stored in PCAPNG format.\nG. The dataset captures network communication generated by Raspberry Pi-based IoT nodes configured to emulate service and client roles.\nH. Traffic was recorded during normal operations and during the execution of predefined cyberattack scenarios within an isolated experimental network.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13068586", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13068586/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-73c3fc6acc6b05b4d795", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHypocrellin A (HA), a photoactive perylenequinone from the bambusicolous Shiraia fungi , possesses potent photodynamic anticancer and antimicrobial properties. However, the signaling mechanisms governing its biosynthesis remain poorly understood. In this study, we identify spermidine (Spd), a ubiquitous polyamine, as a novel elicitor that significantly enhances HA production in Shiraia sp. S9. Spd activated both nitric oxide synthase (NOS) and nitrate reductase (NR) for nitric oxide (NO) generation, leading to the stimulation of the soluble guanylate cyclase (sGC)–cyclic guanosine monophosphate (cGMP) signaling cascade. Inhibition of NO generation or sGC activity suppressed both cGMP accumulation and HA biosynthesis. Transcriptomic analysis revealed that Spd-induced NO signaling upregulated genes in central carbon metabolism and the hypocrellin biosynthetic gene cluster. The dual elicitation strategy by the combined addition of Spd and the NO donor sodium nitroprusside (SNP) exhibited a strong enhancing effect, increasing HA yield by 4.6-fold compared with control cultures. These results demonstrate that Spd regulates HA biosynthesis through a NO–cGMP–mediated signaling pathway, unveiling polyamines as new metabolic elicitors and providing an efficient dual-elicitation strategy for large-scale hypocrellin production. The online version contains supplementary material available at 10.1186/s40643-026-01051-2.\n\nCandidates:\nA. The evidence does not state that hypocrellin A (HA), a photoactive perylenequinone from the bambusicolous Shiraia fungi , possesses potent photodynamic anticancer and antimicrobial properties.\nB. Spd activated both nitric oxide synthase (NOS) and nitrate reductase (NR) for nitric oxide (NO) generation, leading to the stimulation of the soluble guanylate cyclase (sGC)–cyclic guanosine monophosphate (cGMP) signaling cascade.\nC. However, the signaling mechanisms governing its biosynthesis remain poorly understood.\nD. In this study, we identify spermidine (Spd), a ubiquitous polyamine, as a novel elicitor that significantly enhances HA production in Shiraia sp.\nE. The evidence does not state that spd activated both nitric oxide synthase (NOS) and nitrate reductase (NR) for nitric oxide (NO) generation, leading to the stimulation of the soluble guanylate cyclase (sGC)–cyclic guanosine monophosphate (cGMP) signaling cascade.\nF. Hypocrellin A (HA), a photoactive perylenequinone from the bambusicolous Shiraia fungi , possesses potent photodynamic anticancer and antimicrobial properties.\nG. The evidence does not state that in this study, we identify spermidine (Spd), a ubiquitous polyamine, as a novel elicitor that significantly enhances HA production in Shiraia sp.\nH. The evidence does not state that however, the signaling mechanisms governing its biosynthesis remain poorly understood.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13070100", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13070100/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-97c760ff6551a0108de3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBioprocesses grant us with broad opportunities to build a circular carbon economy. Using microbes allows us to replace non-renewable resources and change waste management strategies, including recycling and upcycling. However, research still lags on strategies to enable economic viability of these types of bioprocesses relative to the traditional production methods. Tuning the composition of the microbial growth medium is one way to address these issues. This allows us to gain insight into the interactions of nutrients and the focal organisms in order to formulate appropriate media recipes to support the metabolic needs. This step, which is often overlooked in proof-of-concept research, can substantially improve process metrics. After carbon, nitrogen is the most important and costly nutrient and should be given thorough consideration. In this study, we utilized nitrogen sourcing to tackle the biological upcycling of thermally oxo-degraded plastic waste using a previously described non-conventional yeast with the ability to utilize hydrophobic substrates, Candida maltosa. We compare the use of algae extract and casamino acids as organic, amino acid-based nitrogen sources to inorganic ammonium sulfate with the goal of increasing growth metric and biomass production. Our findings show that the use of algae extract and casamino acids promotes 2X–25X higher growth in model compounds and up to 2X on TOD products. Significant changes were observed in elemental composition of the cells in response to the change in nitrogen and carbon source. These changes align with the ~ 3X increase in internal protein content in the case of cells grown on casamino acids and TOD. Membrane properties and fatty acid content of the cells are also largely impacted, with a significant decrease in saturated and unsaturated fatty acid content, resulting in a reduced average lipid length in the case of casamino acid and TOD grown cells. Three proteins, namely the nonspecific lipid transfer protein POX18, the glycine zipper 2 transmembrane domain-containing protein, and the histidine triad nucleotide binding protein HNT1 were expressed in cells grown on TOD. The results from this study highlight the importance of nutrient management in non-model organisms and the biological challenges associated with the utilization of these compounds which can be insightful in future genetic engineering efforts for improved performance. The online version contains supplementary material available at 10.1186/s40643-026-01046-z.\n\nCandidates:\nA. The evidence does not state that however, research still lags on strategies to enable economic viability of these types of bioprocesses relative to the traditional production methods.\nB. However, research still lags on strategies to enable economic viability of these types of bioprocesses relative to the traditional production methods.\nC. The evidence does not state that bioprocesses grant us with broad opportunities to build a circular carbon economy.\nD. Tuning the composition of the microbial growth medium is not one way to address these issues.\nE. The evidence does not state that using microbes allows us to replace non-renewable resources and change waste management strategies, including recycling and upcycling.\nF. Tuning the composition of the microbial growth medium is one way to address these issues.\nG. Using microbes allows us to replace non-renewable resources and change waste management strategies, including recycling and upcycling.\nH. Bioprocesses grant us with broad opportunities to build a circular carbon economy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13070103", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13070103/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cdba548faa921963f05b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nUnlike conventional monoclonal antibodies, complex biologics—such as bispecific antibodies and fusion proteins—often face challenges including lower expression levels, higher mispairing rates, and greater sensitivity to culture conditions, which can collectively limit both titer and product quality. Leveraging process development case studies, this review systematically explores upstream bioprocessing strategies aimed at mitigating these challenges, with a focus on titer enhancement, lactate metabolism regulation, acidic charge variants control, reduction of aggregates and fragments, and glycosylation optimization. Finally, a perspective toward future upstream development strategies is discussed.\n\nCandidates:\nA. Unlike conventional monoclonal antibodies, complex biologics—such as bispecific antibodies and fusion proteins—often face challenges including lower expression levels, higher mispairing rates, and greater sensitivity to culture conditions, which can collectively limit both titer and product quality.\nB. Finally, a perspective toward future upstream development strategies is discussed.\nC. Leveraging process development case studies, this review systematically explores upstream bioprocessing strategies aimed at mitigating these challenges, with a focus on titer enhancement, lactate metabolism regulation, acidic charge variants control, reduction of aggregates and fragments, and glycosylation optimization.\nD. Finally, a perspective toward future upstream development strategies is not discussed.\nE. Unlike conventional monoclonal antibodies, complex biologics—such as bispecific antibodies and fusion proteins—often face challenges including lower expression levels, higher mispairing rates, and greater sensitivity to culture conditions, which cannot collectively limit both titer and product quality.\nF. The evidence does not state that leveraging process development case studies, this review systematically explores upstream bioprocessing strategies aimed at mitigating these challenges, with a focus on titer enhancement, lactate metabolism regulation, acidic charge variants control, reduction of aggregates and fragments, and glycosylation optimization.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13070570", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13070570/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a09b19c34569d641d0af", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis study addresses the low natural yield of milbemycin D, a potent but underutilized insecticidal macrolide, by engineering a high-producing microbial strain. Using an optimized CRISPR/Cas9-AcrIIA4 system, we replaced the aveA3 polyketide synthase gene in Streptomyces avermitilis HU501 with the heterologous milA3 gene from Streptomyces bingchenggensis , effectively redirecting biosynthesis toward milbemycin D. Through fermentation optimization, a titer of 679.03 mg/L was achieved. Bioassays demonstrated that the biosynthesized milbemycin D exhibits enhanced activity against key pests compared to commercial milbemycin A3/A4. This work establishes a high-yield, laboratory-scale platform for milbemycin D production and highlights CRISPR/Cas9-driven combinatorial biosynthesis as a powerful tool for accessing high-value natural products.\n\nCandidates:\nA. Using an optimized CRISPR/Cas10-AcrIIA4 system, we replaced the aveA3 polyketide synthase gene in Streptomyces avermitilis HU501 with the heterologous milA3 gene from Streptomyces bingchenggensis , effectively redirecting biosynthesis toward milbemycin D.\nB. Bioassays demonstrated that the biosynthesized milbemycin D exhibits enhanced activity against key pests compared to commercial milbemycin A4/A4.\nC. Through fermentation optimization, a titer of 679.03 mg/L was achieved.\nD. Using an optimized CRISPR/Cas9-AcrIIA4 system, we replaced the aveA3 polyketide synthase gene in Streptomyces avermitilis HU501 with the heterologous milA3 gene from Streptomyces bingchenggensis , effectively redirecting biosynthesis toward milbemycin D.\nE. Bioassays demonstrated that the biosynthesized milbemycin D exhibits enhanced activity against key pests compared to commercial milbemycin A3/A4.\nF. The evidence does not state that this study addresses the low natural yield of milbemycin D, a potent but underutilized insecticidal macrolide, by engineering a high-producing microbial strain.\nG. This study addresses the low natural yield of milbemycin D, a potent but underutilized insecticidal macrolide, by engineering a high-producing microbial strain.\nH. Through fermentation optimization, a titer of 680.03 mg/L was achieved.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13071997", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13071997/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f234e3858941e30df690", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\n3‐Methyl‐1‐butanol (3‐MB), a promising next‐generation biofuel, has garnered significant interest owing to its superior combustion characteristics and fuel compatibility. However, current 3‐MB biosynthesis faces major challenges, including low production efficiency and severe toxicity‐induced growth inhibition, which significantly limit its industrial feasibility. In this study, we systematically developed an integrated metabolic engineering approach for high‐level 3‐MB production in Escherichia coli . Through semi‐rational engineering of the rate‐limiting enzyme dihydroxyacid dehydratase (DHAD), combined with molecular dynamics simulations, we identified and addressed previously unrecognized catalytic bottlenecks. The engineered strain exhibited a 32.3‐fold increase in 3‐MB production, reaching 2.20 g/L in shake‐flask cultures. Subsequent adaptive laboratory evolution further improved strain robustness, while genomic analysis revealed novel regulatory targets for metabolic optimization. In a scaled‐up bioreactor fermentation system, the final strain achieved a record titer of 6.24 g/L, representing the highest reported titer for engineered microbial systems. This work not only establishes a scalable platform for 3‐MB biosynthesis but also provides a modular engineering framework applicable to other advanced biofuels. Keywords: 3‐methyl‐1‐butanol, Escherichia coli , metabolic engineering, semi‐rational design, system optimization", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13073303", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13073303/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b8e83a5fe48b002ce1bc", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMesenchymal stem cells (MSCs) are multipotent cells that have the ability to mediate cellular repair through a combination of soluble paracrine factors, as well as bioactive cargo packaged within extracellular vesicles (EVs). Although MSC-derived EVs have been widely investigated for their regenerative potential, progress toward translational evaluation has been limited in part by challenges in scalable and reproducible manufacturing. We recently reported that human telomerase reverse transcriptase (hTERT)-immortalized MSCs reproducibly produce EVs that retain key characteristics of EVs derived from primary MSCs. Building on this work, three-dimensional (3D) culture systems have emerged as promising platforms for large-scale manufacturing. In this study, we compared the yield, molecular composition, and functional activity of EVs produced from hTERT-immortalized MSCs cultured in either a fixed-bed bioreactor or conventional two-dimensional (2D) flasks. Our data demonstrate that bioreactor culture results in increased EV yield as compared to an equivalent production from 2D cultures. Molecular analyses indicated that bioreactor-derived EVs were associated with a broader spectrum of cargo and were enriched with molecules that may contribute to enhanced reparative function. Importantly, bioreactor-derived EVs also exerted a more pronounced effect in cellular repair assays in vitro. Collectively, these results highlight the potential of fixed-bed bioreactors as scalable platforms for EV production, offering higher yields while preserving molecular composition and functional activity. This approach represents an important step toward achieving the reproducible, high-quality EV production required for research and future translational applications.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13073658", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13073658/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-47a9c267023fa80d5cf3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAlternative substrates to traditional Camellia sinensis tea are increasingly investigated to diversify kombucha and enhance its functional properties. This review synthesizes evidence (2020–2025) on how non-tea substrates influence microbial ecology, metabolite composition, and bioactivity of kombucha. A semi-systematic search of PubMed, Scopus, Web of Science, and publisher platforms identified studies on fruit, vegetable, herbal, algal, cereal, dairy, and food-industry by-product substrates reporting compositional or functional outcomes. Extracted data included substrate characteristics, fermentation conditions, SCOBY features, analytical methods, and reported antioxidant, anti-inflammatory, metabolic, probiotic, and dermatological effects. Fermentation often leads to an increase in total phenolic content and antioxidant capacity. These effects are highly dependent on fermentation conditions, particularly duration and substrate composition. In some cases, prolonged fermentation may result in phenolic degradation or transformation, leading to reduced levels of certain compounds. Fruit- and hibiscus-based systems enhanced anthocyanin-driven antioxidant and anti-inflammatory activity. Vegetable and cereal substrates supplied phenolic acids and β -glucans associated with metabolic regulation and gut health, whereas by-products and algal fermentations supported waste valorization and enrichment in chlorogenic acids, pigments, fibers, and peptides. Despite promising functionality, substantial inter-study variability and limited in vivo validation and the lack of standardized fermentation protocols constrain translational application. In addition, the inherent variability in SCOBY microbial composition represents a major source of inconsistency, as differences in microbial communities can significantly influence fermentation dynamics, metabolite profiles, and functional outcomes.\n\nCandidates:\nA. A semi-systematic search of PubMed, Scopus, Web of Science, and publisher platforms identified studies on fruit, vegetable, herbal, algal, cereal, dairy, and food-industry by-product substrates reporting compositional or functional outcomes.\nB. This review synthesizes evidence (2021–2025) on how non-tea substrates influence microbial ecology, metabolite composition, and bioactivity of kombucha.\nC. The evidence does not state that extracted data included substrate characteristics, fermentation conditions, SCOBY features, analytical methods, and reported antioxidant, anti-inflammatory, metabolic, probiotic, and dermatological effects.\nD. The evidence does not state that a semi-systematic search of PubMed, Scopus, Web of Science, and publisher platforms identified studies on fruit, vegetable, herbal, algal, cereal, dairy, and food-industry by-product substrates reporting compositional or functional outcomes.\nE. Extracted data included substrate characteristics, fermentation conditions, SCOBY features, analytical methods, and reported antioxidant, anti-inflammatory, metabolic, probiotic, and dermatological effects.\nF. Alternative substrates to traditional Camellia sinensis tea are not increasingly investigated to diversify kombucha and enhance its functional properties.\nG. Alternative substrates to traditional Camellia sinensis tea are increasingly investigated to diversify kombucha and enhance its functional properties.\nH. This review synthesizes evidence (2020–2025) on how non-tea substrates influence microbial ecology, metabolite composition, and bioactivity of kombucha.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13074635", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13074635/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4d4524015478481c7587", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAmines are indispensable structural motifs in pharmaceuticals, agrochemicals, functional materials, and beyond, driving continuous demand for efficient synthetic methods. While established strategies like cross-coupling and reductive amination are prevalent, traditional batch processes often suffer from limitations in mixing, heat/mass transfer, safety, and scalability. Flow chemistry emerges as a powerful process intensification technology, offering enhanced transport properties, precise parameter control, and improved safety profiles, thereby presenting a highly efficient approach for amine synthesis. This review systematically summarizes representative advances in flow chemistry for amination reactions from 2015 onward. It encompasses a broad range of enabling scenarios (e.g., heterogeneous, thermally activated, and enzymatic amination, among others), analyzed through the lens of process intensification. This review also examines the development of novel continuous-flow amination processes and the study of reaction kinetics leveraging flow chemistry. By providing a consolidated reference on the field’s evolution over the past decade, this review aims to guide researchers toward developing more efficient, sustainable, and scalable flow-based amination processes.\n\nCandidates:\nA. Amines are indispensable structural motifs in pharmaceuticals, agrochemicals, functional materials, and beyond, driving continuous demand for efficient synthetic methods.\nB. This review systematically summarizes representative advances in flow chemistry for amination reactions from 2016 onward.\nC. Flow chemistry emerges as a powerful process intensification technology, offering enhanced transport properties, precise parameter control, and improved safety profiles, thereby presenting a highly efficient approach for amine synthesis.\nD. The evidence does not state that flow chemistry emerges as a powerful process intensification technology, offering enhanced transport properties, precise parameter control, and improved safety profiles, thereby presenting a highly efficient approach for amine synthesis.\nE. While established strategies like cross-coupling and reductive amination are not prevalent, traditional batch processes often suffer from limitations in mixing, heat/mass transfer, safety, and scalability.\nF. This review systematically summarizes representative advances in flow chemistry for amination reactions from 2015 onward.\nG. While established strategies like cross-coupling and reductive amination are prevalent, traditional batch processes often suffer from limitations in mixing, heat/mass transfer, safety, and scalability.\nH. Amines are not indispensable structural motifs in pharmaceuticals, agrochemicals, functional materials, and beyond, driving continuous demand for efficient synthetic methods.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13074803", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13074803/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2bb6deb9405e9637ed5f", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe widespread utilization of synthetic dyes within the textile industry, driven by their chemical recalcitrance and diverse chromatic spectra, constitutes a significant global environmental challenge. Improper discharge of these highly stable effluents into natural water bodies leads to severe ecological imbalances, affecting aquatic life and soil integrity while posing indirect risks to human health due to their mutagenic potential. Conventional physicochemical treatment methods are often hindered by prohibitive operational costs and the frequent generation of hazardous secondary pollutants. Consequently, there is an urgent demand for sustainable biotechnological alternatives to mitigate these industrial impacts. Bioremediation, specifically using white-rot fungi, represents a robust and eco-friendly strategy for the degradation of complex aromatic structures. Species such as Trametes versicolor , Pleurotus ostreatus , and Phanerochaete chrysosporium utilize a specialized extracellular enzymatic complex to mineralize toxic compounds effectively. Here we review the ligninolytic capacity of white-rot fungi and their specialized enzymatic systems for environmental sustainability. The primary points are: (i) the biochemical mechanisms of the ligninolytic system of laccases and peroxidases during dye degradation; (ii) the influence of operational parameters such as pH, temperature, and nutrient availability on fungal metabolic efficiency; (iii) the diverse environmental applications of these microorganisms in treating real textile effluents; (iv) the current biotechnological challenges, including maintaining enzymatic stability in non-sterile industrial environments; and (v) the future perspectives for scaling up fungal treatment systems from laboratory research to large-scale industrial implementation.\n\nCandidates:\nA. Improper discharge of these highly stable effluents into natural water bodies leads to severe ecological imbalances, affecting aquatic life and soil integrity while posing indirect risks to human health due to their mutagenic potential.\nB. The widespread utilization of synthetic dyes within the textile industry, driven by their chemical recalcitrance and diverse chromatic spectra, constitutes a significant global environmental challenge.\nC. Conventional physicochemical treatment methods are not often hindered by prohibitive operational costs and the frequent generation of hazardous secondary pollutants.\nD. Consequently, there is an urgent demand for sustainable biotechnological alternatives to mitigate these industrial impacts.\nE. Conventional physicochemical treatment methods are often hindered by prohibitive operational costs and the frequent generation of hazardous secondary pollutants.\nF. Consequently, there is not an urgent demand for sustainable biotechnological alternatives to mitigate these industrial impacts.\nG. The evidence does not state that the widespread utilization of synthetic dyes within the textile industry, driven by their chemical recalcitrance and diverse chromatic spectra, constitutes a significant global environmental challenge.\nH. The evidence does not state that improper discharge of these highly stable effluents into natural water bodies leads to severe ecological imbalances, affecting aquatic life and soil integrity while posing indirect risks to human health due to their mutagenic potential.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13074889", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13074889/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-eec54b3cc1cdcf029ca7", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHydrogen/deuterium exchange-mass spectrometry (HX-MS) is a rapidly expanding technique used to investigate protein conformational ensembles. The growing popularity and utility of HX-MS has driven the development of diverse instrumentation and software, resulting in inconsistent, non-standardized data analysis and representation. Most HX-MS data formats also employ only mean deuteration representations of the data rather than full isotopic mass spectra, which reduces the information content of the data and limits downstream quantitative analysis. Inspired by reliable protein structure and genomics data formats, we present HXMS, a unified, lightweight, scalable, and human-readable file format for HX-MS data. The HXMS format preserves the isotopic mass envelopes for all peptides, captures the full experimental time-course including fully deuterated control samples, and contains all other key information. It supports multimodal distributions, post-translational modifications (PTMs), and experimental replicates. To promote compatibility with existing HX-MS workflows, we also developed PFLink, a Python package that converts exported data files from commonly used HX-MS software to the HXMS format. PFLink and the HXMS format will enable quantitative, higher-resolution data processing, improved data sharing and storage among HX-MS practitioners, future machine learning applications, and further developments in HX-MS analysis. PFLink is publicly available to install locally on HuggingFace, alongside documentation, or use online at HuggingFace ( https://huggingface.co/spaces/glasgow-lab/PFlink ). The supplementary information includes sample input files, sample HXMS files, and a generic unfilled PFlink custom CSV file that users may populate with key experimental conditions and results, which can then be read and converted into the HXMS format.\n\nCandidates:\nA. The evidence does not state that the growing popularity and utility of HX-MS has driven the development of diverse instrumentation and software, resulting in inconsistent, non-standardized data analysis and representation.\nB. Hydrogen/deuterium exchange-mass spectrometry (HX-MS) is a rapidly expanding technique used to investigate protein conformational ensembles.\nC. The evidence does not state that most HX-MS data formats also employ only mean deuteration representations of the data rather than full isotopic mass spectra, which reduces the information content of the data and limits downstream quantitative analysis.\nD. The growing popularity and utility of HX-MS has driven the development of diverse instrumentation and software, resulting in inconsistent, non-standardized data analysis and representation.\nE. Hydrogen/deuterium exchange-mass spectrometry (HX-MS) is not a rapidly expanding technique used to investigate protein conformational ensembles.\nF. Inspired by reliable protein structure and genomics data formats, we present HXMS, a unified, lightweight, scalable, and human-readable file format for HX-MS data.\nG. The evidence does not state that inspired by reliable protein structure and genomics data formats, we present HXMS, a unified, lightweight, scalable, and human-readable file format for HX-MS data.\nH. Most HX-MS data formats also employ only mean deuteration representations of the data rather than full isotopic mass spectra, which reduces the information content of the data and limits downstream quantitative analysis.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13075950", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13075950/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d419a8a3f347aab88c8c", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nThe magnetotactic bacterium Magnetospirillum gryphiswaldense MSR‐1 synthesizes membrane‐enclosed magnetite (Fe 3 O 4 ) nanocrystals, known as magnetosomes. Owing to their uniform size, purity and superior magnetic properties, magnetosomes represent highly attractive nanomaterials for biotechnological and biomedical applications. However, their bioproduction is limited by demanding cultivation requirements, largely because magnetite biomineralization is highly sensitive to environmental parameters, particularly oxygen. While elevated oxygen concentrations are known to inhibit magnetosome formation, quantitative analyses under defined low‐oxygen conditions are scarce. Here, we cultivated MSR‐1 in bioreactors under precisely controlled dissolved oxygen (DO) levels and quantified growth behaviour, substrate uptake and magnetosome characteristics. Cells harvested during late exponential growth revealed that magnetite crystal numbers per cell were similar across a wide DO range (0%–5%), whereas crystal sizes decreased with increasing oxygen levels. The data further indicate that oxygen inhibits biomineralization primarily through direct oxidative interference rather than indirect metabolic effects. These findings provide a mechanistic basis for optimizing oxygen control strategies in MTB cultivation and demonstrate that fine‐tuning DO levels enables targeted modulation of magnetosome size and properties. This advances both the bioprocess development of high‐yield magnetosome production and the application of tailored magnetic nanoparticles in biotechnology and medicine.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13077289", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13077289/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2c857e84cd26928c6ae1", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nTobacco aging is a critical for developing desirable flavor profiles, driven primarily by microbial and enzymatic activities. This study systematically evaluated the effects of Bacillus clausii inoculation (FJ) and B. clausii -cellulase co-treatment (JM) on the surface microbial communities and aroma compounds of Yunyan 87 tobacco leaves during 9 months of aging, with natural aging as the control (CK). High-throughput 16S rRNA sequencing revealed that the JM treatment significantly increased microbial diversity and enhanced the structural similarity of microbial communities across different aging stages compared with the CK and FJ treatments, while also promoting the proliferation of beneficial taxa (e.g., Bacteroidota , Cyanobacteria ). PICRUSt functional prediction revealed that JM enriched metabolic pathways related to carbohydrate metabolism (18.2–20.6%), amino acid metabolism (15.1–16.7%), and metabolism of cofactors and vitamins (16.5–17.7%)–key pathways for flavor precursor conversion. A total of 29 key aroma compounds (odor activity value > 1) were significantly influenced the quality of tobacco leaves, with JM significantly increasing total volatile content (317.1–388.8 μg/g vs. 124.7–143.0 μg/g in CK and 283.2–300.8 μg/g in FJ). Notably, JM promoted the accumulation of esters (ethyl palmitate, ethyl linoleate) and ketones (4,7,9-megastigmatrien-3-one, damascenone), contributing desirable fruity, floral, and sweet notes. Correlation analysis linked Pseudomonas , Bacillus , and Acinetobacter to the formation of key volatiles. These findings demonstrate that microbial-enzyme co-fermentation enriched desirable aromas, and stabilized microbial communities, providing a practical and efficient strategy for industrial production of high-quality aged tobacco. The online version contains supplementary material available at 10.1186/s40643-026-01052-1.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13079249", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13079249/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dc9d7e362be7ba5846f7", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe transition to a forest-based economy relies on sustainable alternatives that can efficiently convert renewable resources into chemical and material products. Sugar-centered wood fractionation processes, also called modern biorefineries, offer significant potential as a renewable precursor for the substitution of petrochemical derivatives. However, unlocking the full potential of advanced biorefineries requires optimized pretreatment strategies to valorize all biomass-side streams. This work evaluated the distinctive and connective applications of nitrogen explosive decompression (NED), protic ionic liquid (PIL), and alkaline pretreatments for the production of monosaccharides, oligosaccharides, and high value lignin from aspen ( Populus tremula ) biomass within a biorefinery context. Each pretreatment method uniquely influenced biomass fractionation, affecting the hemicellulose and cellulose dissolution, delignification, and the physicochemical properties of the remaining cellulose and lignin components. The findings indicate that PIL pretreatment alone yielded 56% monomeric sugars upon saccharification. Additionally, the combinatorial PIL-NED and Alkaline-NED pretreatment was superior for lignin removal, achieving 68% and 85% delignification while concurrently generating valuable side streams. Henceforth, integrated PIL-NED collaborative approach presents a considerable cost and efficiency advantage over conventional biorefinery pretreatments, offering a promising pathway for the co-production of monosaccharides and high-quality lignin. The online version contains supplementary material available at 10.1186/s40643-026-01040-5.\n\nCandidates:\nA. The evidence does not state that sugar-centered wood fractionation processes, also called modern biorefineries, offer significant potential as a renewable precursor for the substitution of petrochemical derivatives.\nB. The evidence does not state that however, unlocking the full potential of advanced biorefineries requires optimized pretreatment strategies to valorize all biomass-side streams.\nC. Sugar-centered wood fractionation processes, also called modern biorefineries, offer significant potential as a renewable precursor for the substitution of petrochemical derivatives.\nD. The evidence does not state that this work evaluated the distinctive and connective applications of nitrogen explosive decompression (NED), protic ionic liquid (PIL), and alkaline pretreatments for the production of monosaccharides, oligosaccharides, and high value lignin from aspen ( Populus tremula ) biomass within a biorefinery context.\nE. This work evaluated the distinctive and connective applications of nitrogen explosive decompression (NED), protic ionic liquid (PIL), and alkaline pretreatments for the production of monosaccharides, oligosaccharides, and high value lignin from aspen ( Populus tremula ) biomass within a biorefinery context.\nF. However, unlocking the full potential of advanced biorefineries requires optimized pretreatment strategies to valorize all biomass-side streams.\nG. The transition to a forest-based economy relies on sustainable alternatives that can efficiently convert renewable resources into chemical and material products.\nH. The transition to a forest-based economy relies on sustainable alternatives that cannot efficiently convert renewable resources into chemical and material products.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13079260", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13079260/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-66392eae89137176ed9b", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"46.9%\", \"47.9%\"]\n\nEvidence:\nDiabetes, particularly type 2 diabetes mellitus (T2DM), has emerged as a major global health challenge, while currently available therapeutic drugs are frequently associated with drug resistance and adverse side effects. In recent years, bioactive peptides have attracted increasing attention as promising hypoglycaemic candidates due to their favorable safety profiles, good tolerability, and multi‐target physiological regulatory activities. As a GRAS‐designated unicellular green alga, Chlamydomonas reinhardtii is characterized by a high protein content, with approximately 46.9% of its dry biomass composed of protein, making it a rich source for the development of hypoglycaemic short peptides. Previous studies have shown that proteolytic products derived from C. reinhardtii markedly inhibit key glucose‐regulating enzymes, including α‐amylase and α‐glucosidase, exhibiting superior inhibitory potential compared with various terrestrial protein sources. Nevertheless, challenges remain in the large‐scale production of C. reinhardtii ‐derived hypoglycaemic peptides, precise control of the proteolytic process, and validation of their in vivo efficacy. This review summarizes the major sources of hypoglycaemic peptides, current extraction and characterization strategies, and their underlying mechanisms of action, while highlighting the application potential and key limitations of C. reinhardtii ‐derived hypoglycaemic peptides. Addressing these challenges is expected to facilitate the development and application of C. reinhardtii ‐based functional foods and nutraceuticals for diabetes management.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13079433", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13079433/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c36453a081ebcffddf15", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAdvances in live-cell fluorescence microscopy have enabled us to visualize single molecules (such as mRNAs and nascent proteins) in real time with high spatiotemporal resolution. However, these experiments generate large datasets that require complex computational processing pipelines to derive meaningful and quantitative information, which is a technical barrier for many researchers. Here, we introduce MicroLive, an open-source Python-based application for quantifying live-cell microscopy images. MicroLive provides an interactive Graphical User Interface (GUI) to perform key tasks, including cell segmentation, photobleaching correction, single-particle detection/tracking, spot intensity quantification, inter-channel colocalization, and time-series correlation analysis. As a ground-truth testing dataset, we used synthetic live-cell imaging data generated with the rSNAPed toolkit, demonstrating accurate extraction of biologically relevant parameters. Microscopy images of U-2 OS cells expressing a gene construct smHA-KDM5B-BoxB-MS2 were used to demonstrate the use of this software. MicroLive is distributed under a GPLv3 license and available on GitHub https://github.com/ningzhaoAnschutz/microlive . It can be installed via pip: pip install microlive .\n\nCandidates:\nA. MicroLive provides an interactive Graphical User Interface (GUI) to perform key tasks, including cell segmentation, photobleaching correction, single-particle detection/tracking, spot intensity quantification, inter-channel colocalization, and time-series correlation analysis.\nB. However, these experiments generate large datasets that require complex computational processing pipelines to derive meaningful and quantitative information, which is not a technical barrier for many researchers.\nC. Here, we introduce MicroLive, an open-source Python-based application for quantifying live-cell microscopy images.\nD. The evidence does not state that advances in live-cell fluorescence microscopy have enabled us to visualize single molecules (such as mRNAs and nascent proteins) in real time with high spatiotemporal resolution.\nE. The evidence does not state that here, we introduce MicroLive, an open-source Python-based application for quantifying live-cell microscopy images.\nF. The evidence does not state that microLive provides an interactive Graphical User Interface (GUI) to perform key tasks, including cell segmentation, photobleaching correction, single-particle detection/tracking, spot intensity quantification, inter-channel colocalization, and time-series correlation analysis.\nG. However, these experiments generate large datasets that require complex computational processing pipelines to derive meaningful and quantitative information, which is a technical barrier for many researchers.\nH. Advances in live-cell fluorescence microscopy have enabled us to visualize single molecules (such as mRNAs and nascent proteins) in real time with high spatiotemporal resolution.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13080936", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13080936/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8136314f1df6eb3803cd", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"621 kPa\", \"100%\", \"620 kPa\", \"2 h\", \"99%\", \"1 h\", \"89%\", \"88%\"]\n\nEvidence:\nPhotosynthetic microorganisms are capable of oxygenic photosynthesis, delivering both oxygen and cofactors to drive enzymatic redox reactions. However, their dependence on visible light limits the tolerable cell densities to achieve high reaction rates. Immobilizing cells within a matrix often increases biocatalyst productivity while allowing facile retainment but also creates mass transfer limitations across the solid–liquid interface. Herein, we address these challenges and present the immobilization of recombinant cyanobacteria in 3D-printed hydrogels of varying geometries. In particular, whole cells of the cyanobacterium Synechocystis sp. PCC 6803, engineered to express the gene of the ene-reductase YqjM, were implemented in biocompatible hydrogels made out of nanofibrillated cellulose and alginate. The hydrogels were 3D-printed via extrusion into different geometries to alleviate light and mass transfer limitations and were applied for the reduction of prochiral 2-methylmaleimide to ( R )-2-methylsuccinimide. The obtained reactors exhibit high mechanical stability (620 kPa), efficient flow and mass transfer characteristics, high specific surface area (up to 2129 mm 2 g –1 ), and retention times favorable to achieve high product formation. ( R )-2-methylsuccinimide was obtained with a space–time yield of 0.28 g L –1 h –1 and a high enantiomeric purity (>99%). The highly atom-efficient chemical process (88%) using only water to provide electrons for NADPH regeneration could be upscaled and can potentially be operated in extended periods to reduce wastewater associated with cell cultivation. Overall, 3D printing of photosynthetic microorganisms embedded in a hydrogel matrix holds significant promise for advancing the development of whole-cell solid-state photosynthetic cell factories. These are important steps toward improved reactor designs and higher efficiencies to improve crucial redox biotransformations.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13081222", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13081222/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-049fc679e08d506f606c", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nNatural killer cells are emerging as promising “off-the-shelf” effectors for cancer immunotherapy, yet expansion of the NK-92 cell line in batch cultivation leads to rapid loss of cytotoxicity concomitant with lactate accumulation. In this study we developed and validated a two-phase manufacturing strategy that decouples cell proliferation from functional recovery in order to obtain an improved final product potency. Our 8-day kinetic survey determined declines in viability, metabolite profiles and cytotoxicity during static batch expansion. Guided by these data, in a 32-run full-factorial design-of-experiments approach we varied fresh cultivation medium proportion, temperature, dissolved oxygen, and recovery duration; partial least squares modeling identified fresh-medium ratio and recovery time as the primary drivers of cytotoxicity restoration. Identified optimal conditions (90% fresh medium, 37.2 °C, 3.7 days recovery) recovered cytotoxicity and maximized cytotoxic capacity. These setpoints were then translated to a 2 L stirred-tank bioreactor, where a fed-batch expansion under controlled pH and lactate levels produced 2.0 × 10 9 cells, followed by recovery that achieved 43% ± 8% cytotoxicity. This scalable, two-phase paradigm minimizes medium usage and obviates continuous perfusion, offering a potential workflow to increase NK-92 potency and a base for manufacturing high-quality advanced therapy medicinal products.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13083201", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13083201/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1fed24112db161c6c729", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nLacticaseibacillus rhamnosus is widely studied for strain‐specific antimicrobial and immunomodulatory properties, but comparative data for viable and heat‐inactivated preparations remain limited. This study compared the anti‐pathogenic activity of live L. rhamnosus IDCC 3201 and its commercially prepared heat‐inactivated formulation, RHT 3201, and examined their associated microbiota and metabolite profiles in a single‐donor ex vivo fecal fermentation model. Transcript‐level immunomodulatory activity was evaluated separately for RHT 3201 in LPS‐stimulated RAW 264.7 cells. Both preparations inhibited key intestinal pathogens, including Escherichia coli , Staphylococcus aureus , Enterococcus faecalis , and Salmonella Typhimurium, although live IDCC 3201 showed stronger inhibition against E. faecalis and S. Typhimurium . In LPS‐stimulated RAW 264.7 cells, RHT 3201 reduced IL‐1β, IL‐6, COX‐2, and TNF‐α mRNA expression by approximately 39%, 23%, 17%, and 16%, respectively, at 10 × 10 7 CFU/mL. At the species level in the ex vivo fermentation model, RHT 3201 was associated with a higher relative abundance of Bifidobacterium pseudocatenulatum and Lactobacillus ruminis , whereas L. rhamnosus was detected only in the IDCC 3201 group during the 24‐h culture period. Metabolite profiling also showed distinct product‐associated signatures between the two preparations, with higher lactic acid detected in RHT 3201. Overall, the two preparations showed comparable activity against some pathogens but distinct microbiota‐ and metabolite‐associated profiles in this single‐donor ex vivo system, while anti‐inflammatory activity was assessed only for RHT 3201 at the mRNA level. Additional multi‐donor, protein‐level, and in vivo studies are required to confirm the reproducibility and physiological relevance of these findings. Keywords: anti‐pathogenic, ex vivo fermentation, heat‐inactivated, Lacticaseibacillus rhamnosus , metabolite profiling, microbiota", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13083229", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13083229/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-19c83f04890e5efa8768", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"31 °C\", \"28.3 °C\", \"38 °C\", \"30 °C\", \"15%\", \"16%\", \"37 °C\", \"27.3 °C\"]\n\nEvidence:\nEnzymatic depolymerization of polyethylene terephthalate (PET), the world’s most widely used polyester, in seawater at ambient temperature offers a promising and energy-efficient route for freshwater-free plastic recycling. While a number of PET hydrolases have been reported in recent years, their potential under saline conditions remains largely unexplored. Here, we screened eight enzymes in artificial seawater at 30 °C and engineered the most active one, Is PETase, using a semi-rational strategy focused on rigidifying flexible sites. The resulting variant M8 showed simultaneous enhancementsin thermostability (ΔT m = + 27.3 °C), activity (1.14-fold increase) and soluble expression yield (14.3-fold increase). The overall depolymerization efficiency of M8 surpassed that of the thermostable benchmark enzymes DuraPETase and LCC-ICCG by 32.2- and 10.4-fold, respectively. Notably, M8 achieved continuous and efficient depolymerization of 15% (w/v) PET powder in natural seawater at 37 °C, yielding monomers at a rate of 15.4 mM/day, a concentration sufficient to support downstream bacterial assimilation. This work provides an efficient enzymatic platform and paves the way for fully integrated, seawater-based plastic bioconversion processes. The online version contains supplementary material available at 10.1007/s44307-026-00104-z.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13083688", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13083688/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f63e1d918bcfa6496927", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDespite rapid clinical translation, induced pluripotent stem cell (iPSC)-derived therapies face limited global adoption. Harmonized quality control (QC) remains absent, with even fundamental parameters evaluated inconsistently across laboratories. To address this, we conducted two international Quality Assessment Rounds (QARs): QAR 2019 (18 sites, 11 countries) and QAR 2023 (23 sites, 12 countries), evaluating flow cytometry-based assessment of the undifferentiated state and qPCR-based genomic integrity testing. QAR 2019 showed high consistency in genomic integrity testing, while uncovering substantial variability in flow cytometry, prompting QAR 2023 to introduce standardized workflows. These improvements enabled systematic, cross-site evaluation of marker performance across cell states, identifying OCT3/4, TRA-1-60, and SSEA5 as consistently robust pluripotency-associated markers. This global benchmarking effort provides the first empirical multi-site evidence for reproducible iPSC QC and marker-level reliability. Together, these findings establish a foundation for harmonized QC supporting interoperable iPSC banks, regulatory alignment, and scalable manufacturing of globally accessible regenerative therapies. Keywords: induced pluripotent stem cells, regenerative medicine, cell therapy manufacturing, quality control, genomic integrity, flow cytometry, qPCR, inter-laboratory reproducibility, standardization, global harmonization\n\nCandidates:\nA. To address this, we conducted two international Quality Assessment Rounds (QARs): QAR 2020 (18 sites, 11 countries) and QAR 2023 (23 sites, 12 countries), evaluating flow cytometry-based assessment of the undifferentiated state and qPCR-based genomic integrity testing.\nB. QAR 2019 showed high consistency in genomic integrity testing, while uncovering substantial variability in flow cytometry, prompting QAR 2023 to introduce standardized workflows.\nC. Despite rapid clinical translation, induced pluripotent stem cell (iPSC)-derived therapies face limited global adoption.\nD. The evidence does not state that despite rapid clinical translation, induced pluripotent stem cell (iPSC)-derived therapies face limited global adoption.\nE. To address this, we conducted two international Quality Assessment Rounds (QARs): QAR 2019 (18 sites, 11 countries) and QAR 2023 (23 sites, 12 countries), evaluating flow cytometry-based assessment of the undifferentiated state and qPCR-based genomic integrity testing.\nF. Harmonized quality control (QC) remains absent, with even fundamental parameters evaluated inconsistently across laboratories.\nG. QAR 2020 showed high consistency in genomic integrity testing, while uncovering substantial variability in flow cytometry, prompting QAR 2023 to introduce standardized workflows.\nH. The evidence does not state that harmonized quality control (QC) remains absent, with even fundamental parameters evaluated inconsistently across laboratories.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13083784", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13083784/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-34b0a41efa6e0705d72b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHuman induced pluripotent stem cells (hiPSCs) possess broad differentiation potential; however, efficient maturation into functional hepatocytes remains challenging, in part because conventional differentiation strategies rely on 2D culture or partially defined 3D systems that fail to recapitulate key developmental microenvironmental cues. Although 3D culture has been shown to promote hepatic specification and maturation, a fully defined platform that supports the entire differentiation process from hiPSCs in 3D has not been established. Here, we report a scalable synthetic peptide hydrogel (PepGel) platform that enables hiPSCs to self-organize into long-term, proliferative luminal cavity (LC) architectures within 3D colonies (LC-hiPSCs). These LC-hiPSCs exhibited markedly enhanced differentiation efficiency compared to hiPSC aggregates generated via scaffold-free suspension methods. Using a shear-thinning, self-healing hydrogel configuration optimized for suspension culture, we produced large quantities of LC-hiPSCs and directed their differentiation into PepGel-derived hiPSC-derived hepatocyte-like cells (PG-hiHs). PG-hiHs formed polarized, multi-luminal organoid-like structures and exhibited robust expression of hepatocyte-specific genes and proteins, high cytochrome P450 activity, and in vitro metabolic functions comparable to primary human hepatocytes (PHHs). Following live shipment and transplantation into immunocompromised mouse livers, PG-hiHs engrafted efficiently and maintained human-specific albumin and alpha-1 antitrypsin secretion at PHH-equivalent levels. Furthermore, both LC-hiPSCs and PG-hiHs retained viability, structural organization, and phenotype in complex 3D bioprinted constructs for at least 14 days. Together, this work establishes a fully defined, reproducible, and scalable 3D platform that supports the entire differentiation process, overcoming limitations in hiPSC-derived hepatic maturation and enables physiologically relevant hepatocyte-like tissues for disease modeling, regenerative medicine, and biofabrication.\n\nCandidates:\nA. Human induced pluripotent stem cells (hiPSCs) possess broad differentiation potential; however, efficient maturation into functional hepatocytes remains challenging, in part because conventional differentiation strategies rely on 3D culture or partially defined 3D systems that fail to recapitulate key developmental microenvironmental cues.\nB. Human induced pluripotent stem cells (hiPSCs) possess broad differentiation potential; however, efficient maturation into functional hepatocytes remains challenging, in part because conventional differentiation strategies rely on 2D culture or partially defined 3D systems that fail to recapitulate key developmental microenvironmental cues.\nC. Although 4D culture has been shown to promote hepatic specification and maturation, a fully defined platform that supports the entire differentiation process from hiPSCs in 3D has not been established.\nD. Here, we report a scalable synthetic peptide hydrogel (PepGel) platform that enables hiPSCs to self-organize into long-term, proliferative luminal cavity (LC) architectures within 4D colonies (LC-hiPSCs).\nE. Here, we report a scalable synthetic peptide hydrogel (PepGel) platform that enables hiPSCs to self-organize into long-term, proliferative luminal cavity (LC) architectures within 3D colonies (LC-hiPSCs).\nF. Although 3D culture has been shown to promote hepatic specification and maturation, a fully defined platform that supports the entire differentiation process from hiPSCs in 3D has not been established.\nG. The evidence does not state that these LC-hiPSCs exhibited markedly enhanced differentiation efficiency compared to hiPSC aggregates generated via scaffold-free suspension methods.\nH. These LC-hiPSCs exhibited markedly enhanced differentiation efficiency compared to hiPSC aggregates generated via scaffold-free suspension methods.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13084690", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13084690/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-416a5995379e0ee273da", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nNew enzymatic sources need to be explored to meet the increasing demand for bioactives used in the supplementation of non-ruminant animals. Phytase and xylanase are widely used in animal nutrition due to their ability to degrade phytic acid and xylan, respectively. This study aimed to (i) produce phytase and xylanase by culturing Aspergillus japonicus on medium containing wheat bran + soybean bran (1:1 w/w) as alternative carbon sources in a stirred-tank bioreactor and (ii) compare this process with that conducted in Erlenmeyer flasks. The resulting enzymatic cocktails and commercial enzymes were evaluated for their ability to hydrolyze poultry diets (D1–D9). The production peaks of phytase and xylanase in the bioreactor occurred at 120 h (842.30 ± 43.62 U/L) and 144 h (3211.29 ± 274.98 U/L), respectively. After 144 h of cultivation, protein concentration in the bioreactor was approximately twice that observed in Erlenmeyer flasks. ECPAjBr displayed the highest hydrolytic activity for inorganic phosphate in diet D6 (42.34 ± 2.20 mg/g), followed by diet D7 (38.67 ± 0.46 mg/g). Regarding reducing sugars, ECXAjBr exhibited the highest activity in diets D1 and D8, releasing 147.71 ± 18.18 mg/g and 145.83 ± 11.39 mg/g, respectively. All tested diets, regardless of their composition, indicated a synergistic interaction between commercial enzymes and those present in the A. japonicus enzyme cocktail, which exhibits a broad spectrum of hydrolytic activities, including high levels of accessory enzymes. The application of this cocktail in animal diets holds great potential for increasing nutrient bioavailability in non-ruminant animal production.\n\nCandidates:\nA. The evidence does not state that new enzymatic sources need to be explored to meet the increasing demand for bioactives used in the supplementation of non-ruminant animals.\nB. Phytase and xylanase are not widely used in animal nutrition due to their ability to degrade phytic acid and xylan, respectively.\nC. New enzymatic sources need to be explored to meet the increasing demand for bioactives used in the supplementation of non-ruminant animals.\nD. The resulting enzymatic cocktails and commercial enzymes were evaluated for their ability to hydrolyze poultry diets (D2–D9).\nE. This study aimed to (i) produce phytase and xylanase by culturing Aspergillus japonicus on medium containing wheat bran + soybean bran (2:1 w/w) as alternative carbon sources in a stirred-tank bioreactor and (ii) compare this process with that conducted in Erlenmeyer flasks.\nF. The resulting enzymatic cocktails and commercial enzymes were evaluated for their ability to hydrolyze poultry diets (D1–D9).\nG. Phytase and xylanase are widely used in animal nutrition due to their ability to degrade phytic acid and xylan, respectively.\nH. This study aimed to (i) produce phytase and xylanase by culturing Aspergillus japonicus on medium containing wheat bran + soybean bran (1:1 w/w) as alternative carbon sources in a stirred-tank bioreactor and (ii) compare this process with that conducted in Erlenmeyer flasks.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13086882", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13086882/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-000f79ecfa91e8f50a8b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDuckweeds ( Lemnaceae ), the smallest and fastest-growing flowering plants, have emerged as a transformative platform for sustainable biotechnology. This review synthesizes recent advances that underpin their potential as a next-generation plant chassis. We discuss duckweed's unique biology, characterized by reductive evolution, extreme phenotypic plasticity, and a simplified epigenome that favors transgene expression. The decoding of its minimalist genome, along with the establishment of efficient genetic tools including optimized transformation and CRISPR-Cas9 editing, enables precise genetic and metabolic engineering. While traditional uses in phytoremediation and animal feed validate its utility, duckweed's rapid growth in contained, soil-free culture and its edibility offer distinct advantages for molecular farming over established systems like tobacco. We highlight progress in engineering duckweeds to produce vaccines, therapeutic proteins, and high-value metabolites. To transition from proof-of-concept to an industrial workhorse, future efforts must focus on integrated omics databases, universal genetic toolkits, and scalable cultivation. Converging fundamental insights with synthetic biology principles positions duckweed as a versatile and powerful chassis for the bioeconomy.\n\nCandidates:\nA. This review synthesizes recent advances that underpin their potential as a next-generation plant chassis.\nB. We discuss duckweed's unique biology, characterized by reductive evolution, extreme phenotypic plasticity, and a simplified epigenome that favors transgene expression.\nC. The decoding of its minimalist genome, along with the establishment of efficient genetic tools including optimized transformation and CRISPR-Cas9 editing, enables precise genetic and metabolic engineering.\nD. The evidence does not state that this review synthesizes recent advances that underpin their potential as a next-generation plant chassis.\nE. The decoding of its minimalist genome, along with the establishment of efficient genetic tools including optimized transformation and CRISPR-Cas10 editing, enables precise genetic and metabolic engineering.\nF. Duckweeds ( Lemnaceae ), the smallest and fastest-growing flowering plants, have emerged as a transformative platform for sustainable biotechnology.\nG. The evidence does not state that we discuss duckweed's unique biology, characterized by reductive evolution, extreme phenotypic plasticity, and a simplified epigenome that favors transgene expression.\nH. The evidence does not state that duckweeds ( Lemnaceae ), the smallest and fastest-growing flowering plants, have emerged as a transformative platform for sustainable biotechnology.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13087006", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13087006/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0a6d8e7dfacc80555078", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nOptical coherence tomography velocimetry (OCTV) was demonstrated with in-line processing of biologics for the first time. OCTV allowed the velocity of concentrated monoclonal antibodies (mAbs at 39.5–84.7 mg mL –1 ) to be probed in 3.4 pL volumes over distances 0–5 mm from the pipe walls. The large penetration depth is facilitated by the relatively low turbidity of mAbs at near-infrared wavelengths (1300 nm). The mAb solutions could be concentrated in situ and the changes to the viscoelasticity measured. Higher concentration mAb solutions became shear thinning (following the power law fluid model) and the amplitude of their velocity fluctuations decreased. Furthermore, dropping the pH of the mAb solutions induced a gelation phase transition and complex changes to the mAb rheology could be observed with OCTV e.g. thixotropy and the formation of a stationary boundary layer. Thus, in situ formulation of mAbs could be explored with OCTV under industrially relevant conditions.\n\nCandidates:\nA. The large penetration depth is facilitated by the relatively low turbidity of mAbs at near-infrared wavelengths (1300 nm).\nB. The mAb solutions could be concentrated in situ and the changes to the viscoelasticity measured.\nC. The evidence does not state that the mAb solutions could be concentrated in situ and the changes to the viscoelasticity measured.\nD. Optical coherence tomography velocimetry (OCTV) was demonstrated with in-line processing of biologics for the first time.\nE. OCTV allowed the velocity of concentrated monoclonal antibodies (mAbs at 40.5–84.7 mg mL –1 ) to be probed in 3.4 pL volumes over distances 0–5 mm from the pipe walls.\nF. The large penetration depth is facilitated by the relatively low turbidity of mAbs at near-infrared wavelengths (1301 nm).\nG. OCTV allowed the velocity of concentrated monoclonal antibodies (mAbs at 39.5–84.7 mg mL –1 ) to be probed in 3.4 pL volumes over distances 0–5 mm from the pipe walls.\nH. Optical coherence tomography velocimetry (OCTV) was not demonstrated with in-line processing of biologics for the first time.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13088178", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13088178/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fa5720849dd2a4f03c3d", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nAntimicrobial peptides (AMPs) such as pediocin PA-1 are attractive for food biopreservation and infection control, but their broader use is limited by low recombinant yields and high production costs. Corynebacterium glutamicum has emerged as a robust GRAS chassis for heterologous peptide and protein production, yet commonly used shuttle vectors provide only moderate plasmid copy numbers and expression capacities. In particular, existing pediocin PA-1 processes in C. glutamicum rely on standard pBL1 - or pCG1-family vectors that do not yet leverage replication-origin engineering. We rationally redesigned the replication control region of the widely used pClik 5α (pCG1-family) backbone by introducing targeted mutations in the repA gene, an antisense RNA ( cgrI ) promoter, and putative partitioning genes parAB , and constructed a systematic panel of high-copy variants. Using a P tuf -driven mCherry reporter as a quantitative readout, we identified plasmids that supported several-fold higher fluorescence than the parental backbone while maintaining robust growth. Fluorescence-based gene-dosage estimation indicated a strong increase in apparent plasmid copy number. Independent qPCR-based plasmid copy number determination using two plasmid loci confirmed that the lead variant pClik 5α repA mut reached approximately 28–30 copies per chromosome equivalent, compared to approximately 2–3 copies for the parental plasmid, corresponding to an approximately 10-fold increase. Genome-wide transcriptome analysis revealed a defined and adaptive transcriptional response to elevated plasmid copy number and expression burden, characterized by adjustments in membrane-associated transport, respiratory functions, and amino acid-related metabolism, without evidence of collapse of core biosynthetic functions. When the best-performing replicon was applied to episomal expression of a codon-optimized pedACD Cgl operon, pediocin PA-1 titers increased by 2.5-fold compared to the best pXMJ19-based reference under identical, previously optimized process conditions, placing the system, under comparable cultivation formats, within the upper range of reported microbial pediocin production processes. This work demonstrates that rational engineering of pCG1-family replication modules in C. glutamicum can unlock markedly higher plasmid copy numbers and expression capacities while preserving physiological robustness. The resulting high-copy pClik 5α derivatives, exemplified by pClik 5α repA mut , provide a versatile high-copy expression platform with demonstrated utility for recombinant reporter protein and antimicrobial peptide production in C. glutamicum and offer a foundation for further integration with folding, secretion, and process engineering strategies to advance industrial AMP production. The online version contains supplementary material available at 10.1186/s12934-026-03004-y. Keywords: Corynebacterium glutamicum , Pediocin PA-1, Bioprocess, Food additive, Bacteriocin, Antimicrobial peptide, Plasmid copy number, Expression system, Peptide production, mCherry, Listeria spp", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13088423", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13088423/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bb9d81568bd334694c38", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nEscherichia coli is a microbial expression system widely spread in industrial manufacturing systems for the production of biotherapeutics. Most of these recombinant protein production processes are established in fed-batch operation mode. While many sectors of the chemical industry have implemented intensification strategies, the bio-pharmaceutical sector lacks a profound change towards intensified operations due to a decrease of cellular productivity during extended cultivation periods. In this study, intensified bioreactor cultivations of an auto-inducible E. coli strain W3110 as a promising candidate for the production of a recombinant fragment antigen binding (Fab) are demonstrated. The main goal was to investigate the impact of the natural phosphate limitation on the activity of the alkaline phosphatase promoter (phoA) in terms of cell-specific productivity in different processing modes. Following a cell-physiological characterization in fed-batch operation, the transition to intensified upstream processing modes, such as repetitive fed-batch and chemostat was established successfully. A comprehensive understanding of phosphate limitation and precise control of critical parameters, such as growth rate and phosphate-to-substrate uptake enabled the intensified process to match specific product titers and surpass process efficiency (in terms of space-time yield) compared to fed-batch mode. The auto-inducible phoA promoter in E. coli W3110 enabled stable Fab production under intensified bioprocess operations. These findings highlight the suitability of this expression system for repetitive fed-batch and chemostat operations to support broader adoption of intensified strategies in the bio-pharmaceutical industry. The online version contains supplementary material available at 10.1186/s12866-026-04887-y. Keywords: Intensified biomanufacturing, Escherichia coli , phoA expression system, Recombinant protein production, Fragment antigen binding\n\nCandidates:\nA. In this study, intensified bioreactor cultivations of an auto-inducible E.\nB. The evidence does not state that in this study, intensified bioreactor cultivations of an auto-inducible E.\nC. Escherichia coli is a microbial expression system widely spread in industrial manufacturing systems for the production of biotherapeutics.\nD. The evidence does not state that while many sectors of the chemical industry have implemented intensification strategies, the bio-pharmaceutical sector lacks a profound change towards intensified operations due to a decrease of cellular productivity during extended cultivation periods.\nE. Most of these recombinant protein production processes are not established in fed-batch operation mode.\nF. While many sectors of the chemical industry have implemented intensification strategies, the bio-pharmaceutical sector lacks a profound change towards intensified operations due to a decrease of cellular productivity during extended cultivation periods.\nG. Escherichia coli is not a microbial expression system widely spread in industrial manufacturing systems for the production of biotherapeutics.\nH. Most of these recombinant protein production processes are established in fed-batch operation mode.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13088625", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13088625/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7e15c0069316d8e058de", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"1.78 MPa\", \"0.78 MPa\"]\n\nEvidence:\nTendon gel is a translucent gel-like material secreted from the ends of a severed tendon. When mechanical stress is applied to a 3-day in vivo-preserved tendon gel, it matures into type I collagen–dominant tissue similar to normal tendon. This study aimed to evaluate the effects of transplanting a 3-day in vivo-preserved tendon gel into a knee medial collateral ligament (MCL) injury site in rabbits to promote intrinsic ligament regeneration. Tendon gel was prepared from rabbit Achilles tendons using the film model method and harvested after 3 days of in vivo preservation. The 3-day tendon gel was transplanted into the knee MCL injury sites in another set of rabbits ( n = 48). Additionally, the healing process was assessed at 1, 2, and 4 weeks postoperatively using mechanical and histological analyses. Ultimate load, peak stress, and elastic modulus were measured. Histological maturity was semi-quantitatively scored, and collagen type I and III expressions were examined by immunofluorescence staining. At 2 weeks, the tendon gel group demonstrated significantly higher ultimate load than the control group (12.25 ± 4.90 vs. 5.25 ± 2.40 N; p = 0.02). The tendon gel group had greater peak stress than the control group (3.54 ± 1.44 vs. 1.69 ± 0.78 MPa; p = 0.02). Histological scores were higher in the tendon gel group than in the control group (7.25 ± 0.43 vs. 5.50 ± 1.73; p = 0.03). Cells in the tendon gel group were aligned parallel to collagen fibers with elongated nuclei, while type I collagen expression was stronger than that observed in controls. Transplanting a 3-day in vivo-preserved tendon gel into an injured ligament enhanced mechanical strength and histological maturation at 2 weeks postoperatively. These findings suggest that this tendon gel serves as a promising biomaterial for accelerating ligament healing.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13090201", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13090201/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2720c33abfda2db86fc3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSourdough fermentation is a traditional biotechnological process used to improve the nutritional value, flavour profile, and textural attributes of baked goods. Sourdough principal microflora is represented by yeasts, lactic acid bacteria (LAB) and acetic acid bacteria (AAB). The yeasts are primarily responsible for leavening whereas the LABs and AABs carry out the acidification of the dough and both contribute to the flavour of the resulting bread. Our previous work explored the microbial species diversity of sourdoughs in Botswana and showed the unique microbial communities involved in sourdough production from different areas in Botswana. This study is aimed at characterizing yeasts and bacterial isolates from 9 traditional sourdoughs indigenous to Botswana. Here we report the functional characterization of 21 yeasts, 9 LAB, 4 AAB, and 11 other bacteria isolated from traditional sourdough as reported in our previous study. These isolates were characterized based on their ability to assimilate different carbon sources found in flour (maltose, glucose, sucrose, fructose, raffinose and maltotriose), capability to ferment carbon sources (maltose, glucose, sucrose and fructose) in flour and ability to withstand baking associated stresses (thermal stress, osmotic stress, oxidative stress and ethanol stress). This characterization aimed to compare them with conventional baker’s yeast used in modern bread making and to assess their potential for future commercialization. Our results show that the majority of isolated S. cerevisiae strains can utilise different carbon sources, ferment them and withstand baking associated stresses. These are key baking traits poorly expressed in the conventional baker’s yeast. In contrast, the isolated LAB and AAB exhibited limited carbon source utilization. Despite the noted poor utilization of these isolates, we noticed that L. plantarum (ME1-B1), B. cereus (GT1-B1) and B. zhangzhouensis were able to withstand baking associated stresses better than all other bacteria isolates. This study shows how isolated S. cerevisiae strains and non-conventional yeasts have comparable capability to the conventional baker’s yeast, highlighting their potential as sourdough starters and prospects for future commercialization. The online version contains supplementary material available at 10.1007/s00284-026-04880-8.\n\nCandidates:\nA. The yeasts are not primarily responsible for leavening whereas the LABs and AABs carry out the acidification of the dough and both contribute to the flavour of the resulting bread.\nB. The evidence does not state that our previous work explored the microbial species diversity of sourdoughs in Botswana and showed the unique microbial communities involved in sourdough production from different areas in Botswana.\nC. Sourdough principal microflora is represented by yeasts, lactic acid bacteria (LAB) and acetic acid bacteria (AAB).\nD. Our previous work explored the microbial species diversity of sourdoughs in Botswana and showed the unique microbial communities involved in sourdough production from different areas in Botswana.\nE. Sourdough fermentation is not a traditional biotechnological process used to improve the nutritional value, flavour profile, and textural attributes of baked goods.\nF. Sourdough fermentation is a traditional biotechnological process used to improve the nutritional value, flavour profile, and textural attributes of baked goods.\nG. The yeasts are primarily responsible for leavening whereas the LABs and AABs carry out the acidification of the dough and both contribute to the flavour of the resulting bread.\nH. Sourdough principal microflora is not represented by yeasts, lactic acid bacteria (LAB) and acetic acid bacteria (AAB).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13090207", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13090207/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7d7699419f078e50ef34", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGrowing global protein demand has fueled innovation and investment in alternative protein (AP) products, including plant-based, fermentation-derived, and cell-cultivated products. Through interviews with AP stakeholders in the United States (U.S.), we explored the sector’s evolution, challenges, opportunities, and trends. Interviewees described a boom from 2009 to 2021 followed by a decline, which the sector is now working to reverse. Achieving taste and price parity, attracting a broad consumer base, producing at scale, and navigating a charged policy environment remain key sector challenges. Looking forward, stakeholders were optimistic, noting opportunities including collaborations within and across sectors; workforce development; innovative financing, scalability models, and products; and increased policy engagement. Findings indicate that the U.S. AP sector is at a critical inflection point. This study points to future research, financing, and innovation that could help AP products become a mainstay in the food system. Subject terms: Business and management, Business and management, Economics, Economics, Environmental social sciences, Information systems and information technology\n\nCandidates:\nA. The evidence does not state that through interviews with AP stakeholders in the United States (U.S.), we explored the sector’s evolution, challenges, opportunities, and trends.\nB. The evidence does not state that achieving taste and price parity, attracting a broad consumer base, producing at scale, and navigating a charged policy environment remain key sector challenges.\nC. Growing global protein demand has fueled innovation and investment in alternative protein (AP) products, including plant-based, fermentation-derived, and cell-cultivated products.\nD. Interviewees described a boom from 2010 to 2021 followed by a decline, which the sector is now working to reverse.\nE. Interviewees described a boom from 2009 to 2021 followed by a decline, which the sector is now working to reverse.\nF. Through interviews with AP stakeholders in the United States (U.S.), we explored the sector’s evolution, challenges, opportunities, and trends.\nG. The evidence does not state that growing global protein demand has fueled innovation and investment in alternative protein (AP) products, including plant-based, fermentation-derived, and cell-cultivated products.\nH. Achieving taste and price parity, attracting a broad consumer base, producing at scale, and navigating a charged policy environment remain key sector challenges.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13090389", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13090389/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-09f2a1545a2c341dede0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAs chimeric antigen receptor (CAR)‐T cell therapy has expanded rapidly to meet the growing global cancer burden; many challenges have emerged as a critical factor influencing its efficacy. However, due to the complicated mechanisms of CAR‐T cells, human interference alone was insufficient to optimize the outcomes. In parallel, artificial intelligence (AI) has begun to intersect with CAR‐T cells, offering novel computational interferences that can refine therapeutic mechanisms. The literature is still lacking a comprehensive investigation that merges CAR‐T cell mechanistic biology and limitations with the advancing abilities of AI to meet these barriers. This review provides an overview of the mechanistic foundations of CAR‐T cell. It also investigates the various challenges facing the current CAR‐T therapies including toxicity, resistance, and accessibility issues. On this basis, we examined the way AI‐based innovations are being utilized to optimize the CAR‐T engineering and clinical management. Finally, we examined clinical studies and case studies incorporating AI elements, emphasizing both therapeutic mechanisms and outcomes of the study. By integrating mechanistic biology with computational innovation, this review provides a unified unique perspective that can guide the development of safer and more effective CAR‐T therapies.\n\nCandidates:\nA. As chimeric antigen receptor (CAR)‐T cell therapy has expanded rapidly to meet the growing global cancer burden; many challenges have emerged as a critical factor influencing its efficacy.\nB. However, due to the complicated mechanisms of CAR‐T cells, human interference alone was not insufficient to optimize the outcomes.\nC. The literature is not still lacking a comprehensive investigation that merges CAR‐T cell mechanistic biology and limitations with the advancing abilities of AI to meet these barriers.\nD. In parallel, artificial intelligence (AI) has begun to intersect with CAR‐T cells, offering novel computational interferences that can refine therapeutic mechanisms.\nE. The evidence does not state that as chimeric antigen receptor (CAR)‐T cell therapy has expanded rapidly to meet the growing global cancer burden; many challenges have emerged as a critical factor influencing its efficacy.\nF. In parallel, artificial intelligence (AI) has begun to intersect with CAR‐T cells, offering novel computational interferences that cannot refine therapeutic mechanisms.\nG. The literature is still lacking a comprehensive investigation that merges CAR‐T cell mechanistic biology and limitations with the advancing abilities of AI to meet these barriers.\nH. However, due to the complicated mechanisms of CAR‐T cells, human interference alone was insufficient to optimize the outcomes.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13090583", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13090583/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4a0be8231be78fb87d36", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPseudouridine, the C5-ribose epimer of uridine with significant biological functions and clinical applications, was efficiently produced through systematic metabolic strategy of Escherichia coli in this study. Initial overexpression of pseudouridine-5-phosphate glycosylase gene psuG and alkaline phosphatase gene YjjG in E . coli pRSFDuet-1- YjjG - psuG yielded 0.43 g L −1 pseudouridine, which increased 8.56-fold with 5 g L −1 uridine supplementation. Subsequent deletion of thrA , psuT , argF , and pepA enhanced titer by 1.29-fold in E . coli Δ thrA Δ psuT Δ argF Δ pepA pRSFDuet-1- YjjG - psuG , while ribonucleoside hydrolase gene rihA overexpression boosted titer to 5.57 g L −1 . Further optimization through deleting the uridine kinase gene udk and overexpressing the ribokinase gene rbsK in strain E . coli Δ thrA Δ psuT Δ argF Δ pepA Δ udk Δ udp Δ ppnp pRSFDuet-1- YjjG - psuG pCDFDuet-1- rihA-rbsK achieved 6.23 g L −1 pseudouridine, increasing to 11.34 g L −1 with two-stage uridine feeding. Fed-batch fermentation in a 5-L bioreactor yielded a record 102.2 g L −1 pseudouridine. This work provides an efficient and scalable bioprocess for industrial pseudouridine manufacturing to meet the growing demands of mRNA-based applications. Keywords: Rational reconstruction, Pathway optimization, Enhanced production, Escherichia coli , Pseudouridine\n\nCandidates:\nA. coli pRSFDuet-1- YjjG - psuG yielded 0.43 g L −1 pseudouridine, which increased 8.56-fold with 5 g L −1 uridine supplementation.\nB. Initial overexpression of pseudouridine-5-phosphate glycosylase gene psuG and alkaline phosphatase gene YjjG in E .\nC. Pseudouridine, the C6-ribose epimer of uridine with significant biological functions and clinical applications, was efficiently produced through systematic metabolic strategy of Escherichia coli in this study.\nD. Subsequent deletion of thrA , psuT , argF , and pepA enhanced titer by 1.29-fold in E .\nE. Initial overexpression of pseudouridine-6-phosphate glycosylase gene psuG and alkaline phosphatase gene YjjG in E .\nF. Pseudouridine, the C5-ribose epimer of uridine with significant biological functions and clinical applications, was efficiently produced through systematic metabolic strategy of Escherichia coli in this study.\nG. Subsequent deletion of thrA , psuT , argF , and pepA enhanced titer by 2.29-fold in E .\nH. coli pRSFDuet-2- YjjG - psuG yielded 0.43 g L −1 pseudouridine, which increased 8.56-fold with 5 g L −1 uridine supplementation.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13090712", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13090712/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8f73b76c8763ce38333f", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nChimeric antigen receptor (CAR) T cells present a novel and transformative approach to treat certain haematological malignancies. However, CAR‐T cell expansion methods are still under development and often rely on manual, low‐control culture methods. The transition to bioreactors would allow for greater process control and scalability and is a key focus of research in the field. Despite this, there are few methods to determine culture progression without the need for manual cell sampling which risks introducing errors and heightening contamination risks. In this article, we assessed whether capacitance technology could deliver reliable, on‐line cell concentrations by comparing T cell, and CAR‐T cell bioprocesses with Chinese hamster ovary (CHO) cultures—widely used in biotechnology and with established capacitance usage. Finding that capacitance technology could accurately measure CAR‐T cell concentrations, we then demonstrated the automation of feeding using capacitance‐derived triggers which improved bioprocess performance in terms of cell concentration and throughput. We anticipate that this study will expand avenues of investigation regarding capacitance as a suitable process analytical technology (PAT) to enable monitoring and control of CAR‐T cell manufacture, and potentially other cell and gene therapy products. It may also enable remote monitoring of multiple batches, harvest control, and the generation of large collections of process data for modelling, which will further progress the field. Keywords: bioprocess 4.0, cell and gene therapy, chimeric antigen receptor T cells, dielectric spectroscopy, Jurkat, process analytical technology\n\nCandidates:\nA. Chimeric antigen receptor (CAR) T cells present a novel and transformative approach to treat certain haematological malignancies.\nB. However, CAR‐T cell expansion methods are not still under development and often rely on manual, low‐control culture methods.\nC. The transition to bioreactors would allow for greater process control and scalability and is a key focus of research in the field.\nD. The transition to bioreactors would allow for greater process control and scalability and is not a key focus of research in the field.\nE. The evidence does not state that chimeric antigen receptor (CAR) T cells present a novel and transformative approach to treat certain haematological malignancies.\nF. Despite this, there are not few methods to determine culture progression without the need for manual cell sampling which risks introducing errors and heightening contamination risks.\nG. However, CAR‐T cell expansion methods are still under development and often rely on manual, low‐control culture methods.\nH. Despite this, there are few methods to determine culture progression without the need for manual cell sampling which risks introducing errors and heightening contamination risks.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13093239", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13093239/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-06a61a5548e8a73bf242", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe field of biomimetic and bioinspired materials has progressed rapidly by drawing inspiration from nature's intricate structures and multifunctional systems in order to address pressing challenges in modern engineering. This review critically examines the mechanical performance of these materials, focusing on their hierarchical design principles, synthesis strategies, and versatility of application. Natural exemplars such as nacre, spider silk, and bone are emphasized, as their structural efficiency and functional adaptability have informed the development of synthetic analogues with superior strength, toughness, flexibility, and lightweight characteristics. The review also highlights advanced fabrication methods, such as additive manufacturing and precision chemical synthesis, which have enabled researchers to replicate the complexity of nature with ever‐greater accuracy. Key case studies demonstrate how bioinspired strategies have been translated into high‐performance materials for use in the aerospace, construction, and biomedical sectors. The review also discusses challenges related to scalability, reproducibility, and industrial integration. Finally, the review outlines emerging interdisciplinary approaches that are set to further enhance the mechanical properties and practical relevance of biomimetic materials, establishing them as transformative solutions for the next generation of engineering systems. Keywords: bioinspired design, biomimetic materials, engineering applications, mechanical performance, synthesis techniques\n\nCandidates:\nA. This review critically examines the mechanical performance of these materials, focusing on their hierarchical design principles, synthesis strategies, and versatility of application.\nB. The review also highlights advanced fabrication methods, such as additive manufacturing and precision chemical synthesis, which have enabled researchers to replicate the complexity of nature with ever‐greater accuracy.\nC. The evidence does not state that the review also highlights advanced fabrication methods, such as additive manufacturing and precision chemical synthesis, which have enabled researchers to replicate the complexity of nature with ever‐greater accuracy.\nD. The evidence does not state that this review critically examines the mechanical performance of these materials, focusing on their hierarchical design principles, synthesis strategies, and versatility of application.\nE. The field of biomimetic and bioinspired materials has progressed rapidly by drawing inspiration from nature's intricate structures and multifunctional systems in order to address pressing challenges in modern engineering.\nF. The evidence does not state that the field of biomimetic and bioinspired materials has progressed rapidly by drawing inspiration from nature's intricate structures and multifunctional systems in order to address pressing challenges in modern engineering.\nG. Natural exemplars such as nacre, spider silk, and bone are emphasized, as their structural efficiency and functional adaptability have informed the development of synthetic analogues with superior strength, toughness, flexibility, and lightweight characteristics.\nH. Natural exemplars such as nacre, spider silk, and bone are not emphasized, as their structural efficiency and functional adaptability have informed the development of synthetic analogues with superior strength, toughness, flexibility, and lightweight characteristics.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13093749", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13093749/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b9934a123935fa5b9c80", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis study develops and optimizes a high‐cell‐density cultivation (HCDC) of E. coli BL21 (DE3) to improve enzyme production utilizing DASGIP multibioreactor systems. By shifting from traditional shake flask methods to HCDC, we observed a 43‐fold increase in biomass production, with a comparable mass‐specific activity of the cell‐free extract. The HCDC demonstrated a 30‐fold increase in the active, soluble expression of arylmalonate decarboxylase (AMDase) from Bordetella bronchiseptica ( Bb AMDase) compared to conventional methodology. The protocol was designed to be readily adapted to various expression approaches and to provide a robust foundation for broader use in recombinant protein production. The HCDC was achieved with a linear glucose feed to promote robust cell growth. We optimized the glycerol feeding strategy during isopropyl β ‐D‐1‐thiogalactopyranoside (IPTG) induction to maximize AMDase production. Throughout the induction phase, we monitored both soluble expression yield and enzymatic activity, aiming to establish a process that is not only profitable but also efficient and stable. Keywords: arylmalonate decarboxylase, biocatalysis, E. coli BL21 (DE3), enzyme expression, high‐cell‐density cultivation\n\nCandidates:\nA. The HCDC demonstrated a 31‐fold increase in the active, soluble expression of arylmalonate decarboxylase (AMDase) from Bordetella bronchiseptica ( Bb AMDase) compared to conventional methodology.\nB. By shifting from traditional shake flask methods to HCDC, we observed a 44‐fold increase in biomass production, with a comparable mass‐specific activity of the cell‐free extract.\nC. The HCDC demonstrated a 30‐fold increase in the active, soluble expression of arylmalonate decarboxylase (AMDase) from Bordetella bronchiseptica ( Bb AMDase) compared to conventional methodology.\nD. coli BL21 (DE3) to improve enzyme production utilizing DASGIP multibioreactor systems.\nE. This study develops and optimizes a high‐cell‐density cultivation (HCDC) of E.\nF. By shifting from traditional shake flask methods to HCDC, we observed a 43‐fold increase in biomass production, with a comparable mass‐specific activity of the cell‐free extract.\nG. coli BL22 (DE3) to improve enzyme production utilizing DASGIP multibioreactor systems.\nH. The evidence does not state that this study develops and optimizes a high‐cell‐density cultivation (HCDC) of E.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13096762", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13096762/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-71bfb4fa4611b83eba08", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThermal oxo-degradation (TOD) of plastic can transform plastic waste into fermentable feedstocks. Bioconversion of the TOD products could be used as feedstocks in a biorefinery concept and lead to new avenues for plastic waste upcycling. Previous work demonstrated this concept with high-density polyethylene (HDPE), the most abundant type of plastic, and identified the nonconventional yeast Candida maltosa as a promising candidate for this application. Here, we describe the evolution of an improved strain of C. maltosa and characterize the uptake mechanisms of TOD products from HDPE (TOD_HDPE). Batch cultures in series passaged at the mid-exponential growth phase applied a selective pressure for faster growth and resulted in a >100% increase in specific growth rate when using TOD_HDPE as a carbon source compared to the wild-type strain used in previous work. Adaptive improvement in specific growth rate is an important step toward the development of an industrial strain, and it provides a basis for a mechanistic understanding of the TOD_HDPE uptake. The evolved strain was compared to the parent strain to identify the cellular and biochemical changes associated with the improved phenotype and the uptake mechanisms involved in the bioconversion of TOD_HDPE. This comparison found that C. maltosa secretes biosurfactants capable of solubilizing hydrocarbons. The adaptive evolution resulted in changes in biosurfactant production that translated to improved emulsification of alkanes and increased solubilization of fatty alcohols and alkanes. In addition to the changes in metabolites, the study identified increases in membrane permeability associated with a reduction in ergosterol that may also play a role in the improved phenotype. These findings support the development of C. maltosa and other potential microbial cell factories for plastic biorefineries and may inform future design strategies. Plastics are considered non-biodegradable because they take decades to break down into molecules that can enter the various carbon cycles. Thermal oxo-degradation can accelerate this rate-limiting step by turning plastic waste into a fermentable carbon source. In this work, we use Adaptive Laboratory Evolution to optimize the bioconversion of depolymerized HDPE by Candida maltosa . We also investigate the changes that resulted from the evolution process to learn about the molecular and cellular mechanisms involved in the bioconversion of hydrophobic substrates. Our findings show unique mechanisms in C. maltosa to overcome mass transfer limitations and metabolize long-chain hydrocarbons and fatty substrates that can possibly be extrapolated to industrial applications beyond plastic upcycling.\n\nCandidates:\nA. Previous work demonstrated this concept with high-density polyethylene (HDPE), the most abundant type of plastic, and identified the nonconventional yeast Candida maltosa as a promising candidate for this application.\nB. The evidence does not state that previous work demonstrated this concept with high-density polyethylene (HDPE), the most abundant type of plastic, and identified the nonconventional yeast Candida maltosa as a promising candidate for this application.\nC. Bioconversion of the TOD products could be used as feedstocks in a biorefinery concept and lead to new avenues for plastic waste upcycling.\nD. Here, we describe the evolution of an improved strain of C.\nE. The evidence does not state that here, we describe the evolution of an improved strain of C.\nF. Thermal oxo-degradation (TOD) of plastic cannot transform plastic waste into fermentable feedstocks.\nG. Thermal oxo-degradation (TOD) of plastic can transform plastic waste into fermentable feedstocks.\nH. The evidence does not state that bioconversion of the TOD products could be used as feedstocks in a biorefinery concept and lead to new avenues for plastic waste upcycling.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13101509", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13101509/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1e92894d9a7ddfe285aa", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"61 g/L\", \"96 g/L\", \"81%\", \"60 g/L\", \"8 L\", \"80%\", \"95 g/L\", \"7 L\"]\n\nEvidence:\nBioplastic poly(3-hydroxybutyrate) (PHB) production by Halomonas bluephagenesis is typically activated under nitrogen limitation, inevitably restricting biomass formation and overall productivity. Here we identified gene ilvA encoding threonine deaminase as a flux-sensitive node linking branched-chain amino-acid synthesis to nitrogen sensing. Complete ilvA deletion or σ 54 disruption in H. bluephagenesis created a pseudo-nitrogen-limitation state that increased PHB accumulation yet concurrently suppressed microbial growth. To overcome this trade-off, two independent ilvA fine-tuning strategies were evaluated including synthetic sRNA-mediated translational repression and SspB/ClpXP proteolysis. In contrast, complete ilvA deletion created a pseudo-nitrogen-limited state that elevated PHB synthesis however reduced cell growth (cell dry weight or CDW) in 7 L bioreactors, reaching only 60 g/L CDW containing 80% PHB, far below the wild-type grown to 95 g/L CDW containing 60% PHB under same conditions, These results indicate that complete deletion of ilvA disrupts the balance between cell growth and PHB production. Under nitrogen rich fed-batch conditions, however, sRNA-based partial repression maintained CDW at 95 g/L while increasing PHB from 60% to 80 wt%, thereby establishing a growth-PHB production balance. These results suggest that controllable attenuation of ilvA , instead of full gene deletion, provides a potentially scalable approach for improving PHB production without severely compromising cell growth.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13101641", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13101641/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bc7dfe45f2578518fb3a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\n2,4-Dihydroxybutyric acid (DHB) is a promising C 4 platform compound for the synthesis of methionine analogues and biodegradable polymers. However, aerobic DHB production from glucose in Escherichia coli involves transient acetate overflow prior to product synthesis, which could be challenging for process scalability. Therefore, we engineered Escherichia coli K-12 MG1655 for optimized DHB production by replacing the phosphotransferase system mediated glucose uptake with the galactose permease GalP, coupled to ATP-dependent phosphorylation via endogenous glucokinase. In combination with targeted deletions of malate- and fumarate-consuming reactions, we obtained a strain with enhanced flux through the tricarboxylic acid (TCA) cycle and pentose phosphate pathway leading to improved NADPH availability and increased anaplerotic activity, as revealed by 13 C metabolic flux analyses. Deletion of the mdh gene encoding for the cytosolic malate dehydrogenase further promoted DHB formation. The resulting strain achieved DHB yields up to 0.20 mol mol −1 (2.43 g L −1 ), a 4-fold increase compared to the wildtype background (0.05 mol mol −1 , 0.60 g L −1 ), under aerobic conditions while suppressing acetate formation. Together, these results demonstrate that GalP-mediated glucose uptake and engineering of the TCA cycle provide a robust metabolic framework for efficient DHB biosynthesis and establish a foundation for further process and pathway development. Keywords: 2,4-Dihydroxybutyric acid; Escherichia coli ; Metabolic engineering; Phosphotransferase system (PTS); Galactose permease (GalP); 13 C metabolic flux analysis\n\nCandidates:\nA. In combination with targeted deletions of malate- and fumarate-consuming reactions, we obtained a strain with enhanced flux through the tricarboxylic acid (TCA) cycle and pentose phosphate pathway leading to improved NADPH availability and increased anaplerotic activity, as revealed by 14 C metabolic flux analyses.\nB. 3,4-Dihydroxybutyric acid (DHB) is a promising C 4 platform compound for the synthesis of methionine analogues and biodegradable polymers.\nC. 2,4-Dihydroxybutyric acid (DHB) is a promising C 4 platform compound for the synthesis of methionine analogues and biodegradable polymers.\nD. In combination with targeted deletions of malate- and fumarate-consuming reactions, we obtained a strain with enhanced flux through the tricarboxylic acid (TCA) cycle and pentose phosphate pathway leading to improved NADPH availability and increased anaplerotic activity, as revealed by 13 C metabolic flux analyses.\nE. However, aerobic DHB production from glucose in Escherichia coli involves transient acetate overflow prior to product synthesis, which could be challenging for process scalability.\nF. The evidence does not state that however, aerobic DHB production from glucose in Escherichia coli involves transient acetate overflow prior to product synthesis, which could be challenging for process scalability.\nG. Therefore, we engineered Escherichia coli K-12 MG1655 for optimized DHB production by replacing the phosphotransferase system mediated glucose uptake with the galactose permease GalP, coupled to ATP-dependent phosphorylation via endogenous glucokinase.\nH. Therefore, we engineered Escherichia coli K-13 MG1655 for optimized DHB production by replacing the phosphotransferase system mediated glucose uptake with the galactose permease GalP, coupled to ATP-dependent phosphorylation via endogenous glucokinase.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13101720", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13101720/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f2ee210c8729a7e70c9b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPhotobiocatalysis with photoautotrophic whole cells has demonstrated strong potential for producing chiral molecules and platform chemicals using sustainable inputs such as light, water and CO 2 under mild reaction conditions. Coupling enzymatic transformations directly to natural photosynthesis enables higher atom efficiency compared with heterotrophic systems. However, large‐scale application remains challenging, particularly due to light attenuation in photobioreactors. In this review, we summarize recent advances in whole‐cell photobiotransformations with emphasis on process conditions. We also discuss strategies for intensifying photobiocatalysis through improved reactor design and new immobilization materials, along with developments in fast‐growing photoautotrophic strains. Sustainability analyses indicate that organic electron donors represent only one factor influencing environmental performance, and simply replacing them with photosynthetic water splitting does not inherently yield a carbon‐negative process. Nonetheless, our calculations show that when high substrate loadings are combined with wastewater use and optimized downstream processing, photosynthesis‐driven biotechnology can offer substantial reductions in CO 2 emissions.\n\nCandidates:\nA. The evidence does not state that however, large‐scale application remains challenging, particularly due to light attenuation in photobioreactors.\nB. The evidence does not state that in this review, we summarize recent advances in whole‐cell photobiotransformations with emphasis on process conditions.\nC. Photobiocatalysis with photoautotrophic whole cells has demonstrated strong potential for producing chiral molecules and platform chemicals using sustainable inputs such as light, water and CO 3 under mild reaction conditions.\nD. In this review, we summarize recent advances in whole‐cell photobiotransformations with emphasis on process conditions.\nE. However, large‐scale application remains challenging, particularly due to light attenuation in photobioreactors.\nF. Coupling enzymatic transformations directly to natural photosynthesis enables higher atom efficiency compared with heterotrophic systems.\nG. Photobiocatalysis with photoautotrophic whole cells has demonstrated strong potential for producing chiral molecules and platform chemicals using sustainable inputs such as light, water and CO 2 under mild reaction conditions.\nH. The evidence does not state that coupling enzymatic transformations directly to natural photosynthesis enables higher atom efficiency compared with heterotrophic systems.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13101868", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13101868/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d7cce76f2da1d476eda4", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nNaturally occurring in breast milk, human milk oligosaccharides (HMOs) are of great interest as an ingredient for infant nutrition due to numerous associated health benefits. Current commercial production relies mainly on microbial fermentation, while enzymatic synthesis is used to produce milligram scales for scientific studies. Enzymatic synthesis using glycosyltransferases and nucleotide sugars is especially promising due to high reaction yields, but is limited by low activity of glycosyltransferases and the high cost of nucleotide sugars. This study presents a novel approach that uses a single engineered E. coli BL21(DE3) strain to simultaneously express six recombinant enzymes (UMPK, PPK3, GALK, NAHK, GALU, and PPA). This enables dual‐nucleotide sugar synthesis through two integrated multienzyme cascades. The system uses cost‐effective substrates, including uridine 5′‐monophosphate (UMP), N ‐acetylglucosamine (GlcNAc), galactose (Gal), and ATP. In situ ATP regeneration is achieved through polyphosphate (PolyP n ) breakdown. Comparative studies of three different expression strain configurations demonstrated that crude cell lysate could serve as an effective biocatalyst. This eliminates the need for expensive enzyme purification while maintaining high catalytic activity. Using crude cell lysate, conversion yields approaching 100% were obtained. Both UDP‐GlcNAc and UDP‐Gal were successfully purified using anion‐exchange chromatography. Based on the UV spectrum, purities of 85–99% and recovery yields exceeding 90%, respectively, were achieved. The practical application of this system was demonstrated by the successful synthesis of two HMOs: Lacto‐ N ‐triose II (LNTII) and lacto‐ N ‐neotetraose (LNnT), which demonstrates an effective nucleotide sugar recycling in coupled enzymatic reactions and paves the way toward larger scale production.\n\nCandidates:\nA. Naturally occurring in breast milk, human milk oligosaccharides (HMOs) are not of great interest as an ingredient for infant nutrition due to numerous associated health benefits.\nB. Current commercial production relies mainly on microbial fermentation, while enzymatic synthesis is used to produce milligram scales for scientific studies.\nC. The evidence does not state that this study presents a novel approach that uses a single engineered E.\nD. Naturally occurring in breast milk, human milk oligosaccharides (HMOs) are of great interest as an ingredient for infant nutrition due to numerous associated health benefits.\nE. This study presents a novel approach that uses a single engineered E.\nF. Enzymatic synthesis using glycosyltransferases and nucleotide sugars is not especially promising due to high reaction yields, but is limited by low activity of glycosyltransferases and the high cost of nucleotide sugars.\nG. Enzymatic synthesis using glycosyltransferases and nucleotide sugars is especially promising due to high reaction yields, but is limited by low activity of glycosyltransferases and the high cost of nucleotide sugars.\nH. Current commercial production relies mainly on microbial fermentation, while enzymatic synthesis is not used to produce milligram scales for scientific studies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13101873", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13101873/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-56f3216e53c0e420ea1a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe large‐scale use of toxic and environmentally hazardous solvents remains a major challenge in industrial manufacturing and consumer‐goods production. Conventional solubilization processes often depend on harsh conditions, including elevated temperatures and pressures, resulting in high energy consumption, health risks, and environmental pollution. Developing sustainable alternatives is therefore an urgent scientific and societal priority. This article discusses recent advances in green solubilization and emerging strategies aiming to reconcile efficiency with environmental compatibility. We address the future role of classical and “green” solvents, including ionic liquids (ILs) and natural deep eutectic solvents (NADES), and critically assess their benefits and limitations from a sustainability perspective. Particular emphasis is placed on water as the potentially “greenest” solvent, highlighting how its intrinsic tendency to form structured, heterogeneous environments can be advantageous or detrimental for solubilization. In this context, we examine mesoscale structuring, surfactant‐free microemulsions, and dynamic interfaces. Furthermore, naturally derived solubilizers such as hydrotropes, biosurfactants, and proteins are considered promising tools to enhance solubility while maintaining biocompatibility and low environmental impact. Selected examples from our own work illustrate how combining water‐based structuring with bio‐derived or benign additives can create new pathways toward energy‐efficient and sustainable solubilization technologies. Keywords: green solubilization, sustainable solubilization, sustainable chemistry, sustainable solvents\n\nCandidates:\nA. The evidence does not state that conventional solubilization processes often depend on harsh conditions, including elevated temperatures and pressures, resulting in high energy consumption, health risks, and environmental pollution.\nB. Developing sustainable alternatives is therefore an urgent scientific and societal priority.\nC. The evidence does not state that the large‐scale use of toxic and environmentally hazardous solvents remains a major challenge in industrial manufacturing and consumer‐goods production.\nD. Developing sustainable alternatives is not therefore an urgent scientific and societal priority.\nE. This article discusses recent advances in green solubilization and emerging strategies aiming to reconcile efficiency with environmental compatibility.\nF. The evidence does not state that this article discusses recent advances in green solubilization and emerging strategies aiming to reconcile efficiency with environmental compatibility.\nG. Conventional solubilization processes often depend on harsh conditions, including elevated temperatures and pressures, resulting in high energy consumption, health risks, and environmental pollution.\nH. The large‐scale use of toxic and environmentally hazardous solvents remains a major challenge in industrial manufacturing and consumer‐goods production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13102552", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13102552/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9add529e9f050433b647", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nProcessing of biomass residues for material and energy recovery generate effluents containing complex organic mixtures. Analyses of bulk parameters are conventional for characterization and classification of such effluents, where the limited information offered impedes the development of molecular level management practices. This study assessed the potential of solvent-selective complexity reduction of effluent dissolved organic matter (DOM) from anaerobic bioprocessing of biomass residues for 1 H NMR spectroscopy. The DOM were acquired after filtration and drying of samples from seven full scale anaerobic bioreactor facilities with effluents used as biofertilizer. The 1 H NMR spectra of DOM in indigenous solvent (water) revealed source dependent characteristics primarily due to variable abundance of aliphatic CCH in lipids and peptides, OCCH in carbohydrates, and olefinic and aromatic subunits. Dimethyl sulfoxide solubilized larger proportion of nonfunctionalized aliphatic and aromatic molecules, with 1 H NMR features also varying depending on the DOM source. Methanol, however, reduced the 1 H NMR spectral variability and dissolved sets of aliphatic and aromatic molecules from the dried DOM with similar 1 H NMR features irrespective of their origin. Among the other solvents studied, the reactive dissolution by trifluoroacetic acid decomposed aliphatic units while enriching aromatics (i.e., C ar H :CC H of 0.7 compared to 0.2 in water), also forming small (oligo)­saccharides and peptide fragments. Acetone, dichloromethane, and acetonitrile extracted alkyl-rich molecules with varying degrees of functionalization. Acetonitrile separated a fraction enriched in aliphatic carboxylic acids, while dichloromethane mainly dissolved nonfunctionalized aliphatic hydrocarbons. It is proposed that a simple process of filtration, drying, and dissolution of effluent DOM in different solvents substantially reduces the complexity and heterogeneity of the organic mixtures enabling the structural discrimination of diverse molecular classes by 1 H NMR spectroscopy.\n\nCandidates:\nA. The DOM were not acquired after filtration and drying of samples from seven full scale anaerobic bioreactor facilities with effluents used as biofertilizer.\nB. This study assessed the potential of solvent-selective complexity reduction of effluent dissolved organic matter (DOM) from anaerobic bioprocessing of biomass residues for 1 H NMR spectroscopy.\nC. The DOM were acquired after filtration and drying of samples from seven full scale anaerobic bioreactor facilities with effluents used as biofertilizer.\nD. The evidence does not state that processing of biomass residues for material and energy recovery generate effluents containing complex organic mixtures.\nE. Analyses of bulk parameters are not conventional for characterization and classification of such effluents, where the limited information offered impedes the development of molecular level management practices.\nF. This study assessed the potential of solvent-selective complexity reduction of effluent dissolved organic matter (DOM) from anaerobic bioprocessing of biomass residues for 2 H NMR spectroscopy.\nG. Processing of biomass residues for material and energy recovery generate effluents containing complex organic mixtures.\nH. Analyses of bulk parameters are conventional for characterization and classification of such effluents, where the limited information offered impedes the development of molecular level management practices.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13103931", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13103931/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9f6a8183c79585776458", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe in vitro engineering of vascularized cardiac tissues holds transformative potential for disease modeling, drug screening, and regenerative therapy. However, despite rapid advances in stem cell biology, biomaterials, and biofabrication technologies, the reconstruction of functional, perfusable vasculature within engineered myocardial tissues remains a central and unresolved challenge. In this review, we move beyond a descriptive catalog of available techniques and instead present a process‐oriented framework for understanding vascularized cardiac tissue engineering. By systematically analyzing how cellular components, biomaterial design, and biofabrication strategies collectively govern vascular formation, perfusion stability, and myocardial function, we examine self‐assembly, mold‐casting, 3D bioprinting, and microfluidic approaches, to critically evaluate their respective advantages and trade‐offs under cardiac‐specific physiological constraints. Finally, application prospects of vascularized cardiac tissues in disease modeling and drug testing are discussed, and current limitations and future directions are proposed to accelerate translational impact. By reframing vascularized cardiac tissue engineering as an integrated manufacturing challenge rather than a collection of isolated technologies, this review aims to provide a coherent conceptual guide for advancing functional human cardiac models. Keywords: cardiac cells, cardiac organoids, cardiac tissue engineering, regenerative medicine, vascularized cardiac tissues\n\nCandidates:\nA. The evidence does not state that in this review, we move beyond a descriptive catalog of available techniques and instead present a process‐oriented framework for understanding vascularized cardiac tissue engineering.\nB. The in vitro engineering of vascularized cardiac tissues holds transformative potential for disease modeling, drug screening, and regenerative therapy.\nC. By systematically analyzing how cellular components, biomaterial design, and biofabrication strategies collectively govern vascular formation, perfusion stability, and myocardial function, we examine self‐assembly, mold‐casting, 4D bioprinting, and microfluidic approaches, to critically evaluate their respective advantages and trade‐offs under cardiac‐specific physiological constraints.\nD. However, despite rapid advances in stem cell biology, biomaterials, and biofabrication technologies, the reconstruction of functional, perfusable vasculature within engineered myocardial tissues remains a central and unresolved challenge.\nE. The evidence does not state that the in vitro engineering of vascularized cardiac tissues holds transformative potential for disease modeling, drug screening, and regenerative therapy.\nF. In this review, we move beyond a descriptive catalog of available techniques and instead present a process‐oriented framework for understanding vascularized cardiac tissue engineering.\nG. By systematically analyzing how cellular components, biomaterial design, and biofabrication strategies collectively govern vascular formation, perfusion stability, and myocardial function, we examine self‐assembly, mold‐casting, 3D bioprinting, and microfluidic approaches, to critically evaluate their respective advantages and trade‐offs under cardiac‐specific physiological constraints.\nH. The evidence does not state that however, despite rapid advances in stem cell biology, biomaterials, and biofabrication technologies, the reconstruction of functional, perfusable vasculature within engineered myocardial tissues remains a central and unresolved challenge.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13104096", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13104096/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-864586ba8b959e15d2c6", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe composition of muscle fiber types and the development of skeletal muscle are critical determinants of cultured meat quality. L‐carnosine, a dipeptide abundant in ruminant muscle, is known to influence meat quality, yet its regulatory mechanisms in bovine skeletal muscle satellite cells (BSCs) for cultured meat production remain unclear. This study aimed to elucidate the effects of L‐carnosine on the proliferation, differentiation, and muscle fiber type transformation of Yanbian cattle BSCs. We identified 10 mM as the optimal concentration for enhancing cell proliferation ( p < 0.05), a key finding established by screening L‐carnosine treatments from 0 to 40 mm. This enhancement was mediated by the upregulation of cell cycle genes (Pax7, Ki67, CDK1, CDK2, PCNA) and the suppression of inhibitors ( p21, p53, p16 ). Furthermore, L‐carnosine robustly promoted myotube formation and specifically upregulated fast‐twitch muscle fiber markers (MyHC2a, MyHC2b, MyHC2x) while downregulating the slow‐twitch marker MyHC1 ( p < 0.05). Transcriptomic analysis identified 449 differentially expressed genes, which were significantly enriched in the PI3K‐Akt signaling pathway. Western blotting confirmed that L‐carnosine activates the Akt/mTOR/P70S6K signaling pathway to drive myogenesis. Additionally, L‐carnosine demonstrated significant antioxidant capacity by reducing reactive oxygen species (ROS) and lipid peroxidation (MDA) while enhancing antioxidant enzyme activities (SOD and GSH‐Px). In conclusion, this study provides the first evidence that L‐carnosine promotes BSC proliferation and fast‐twitch fiber differentiation via the Akt/mTOR/P70S6K pathway, suggesting its potential as a highly effective, natural additive for cultured meat production. Keywords: Akt/mTOR/P70S6K signaling pathway, antioxidant activity, L‐carnosine, proliferation and differentiation, skeletal muscle satellite cells, yanbian cattle\n\nCandidates:\nA. The composition of muscle fiber types and the development of skeletal muscle are not critical determinants of cultured meat quality.\nB. L‐carnosine, a dipeptide abundant in ruminant muscle, is known to influence meat quality, yet its regulatory mechanisms in bovine skeletal muscle satellite cells (BSCs) for cultured meat production remain unclear.\nC. We identified 11 mM as the optimal concentration for enhancing cell proliferation ( p < 0.05), a key finding established by screening L‐carnosine treatments from 0 to 40 mm.\nD. We identified 10 mM as the optimal concentration for enhancing cell proliferation ( p < 0.05), a key finding established by screening L‐carnosine treatments from 0 to 40 mm.\nE. The evidence does not state that this study aimed to elucidate the effects of L‐carnosine on the proliferation, differentiation, and muscle fiber type transformation of Yanbian cattle BSCs.\nF. This study aimed to elucidate the effects of L‐carnosine on the proliferation, differentiation, and muscle fiber type transformation of Yanbian cattle BSCs.\nG. The composition of muscle fiber types and the development of skeletal muscle are critical determinants of cultured meat quality.\nH. L‐carnosine, a dipeptide abundant in ruminant muscle, is not known to influence meat quality, yet its regulatory mechanisms in bovine skeletal muscle satellite cells (BSCs) for cultured meat production remain unclear.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13107544", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13107544/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4d5c38d0d2345aecbc39", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nEfficient and scalable production of viral antigens remains a key challenge in the development of recombinant subunit vaccines and diagnostic reagents. Microbial expression systems, particularly Pichia pastoris ( P. pastoris ), offer a promising platform for producing complex viral glycoproteins with appropriate folding and post-translational modifications. In this study, the hemagglutinin head domain (HA1) of influenza A (H1N1) virus was expressed as a secreted recombinant protein in P. pastoris GS115. The HA1 gene was codon-optimized and expressed under the control of the methanol-inducible AOX1 promoter with an α-factor signal peptide. Multicopy integrants were enriched using G418 selection, and expression conditions were systematically optimized. Under shake-flask induction, the selected recombinant strain produced up to 0.375 g/L of rHA1 in the culture supernatant. The protein was efficiently purified by Ni-NTA affinity chromatography to a purity exceeding 95%. PNGase F digestion confirmed N-linked glycosylation. Limited functional validation demonstrated that the yeast-expressed rHA1 retained antigenic integrity, as evidenced by the induction of rHA1-specific antibodies and hemagglutination-inhibiting activity in a murine model. These results establish P. pastoris as an effective microbial cell factory for the high-level secretion of influenza HA1 protein. The optimized expression and purification strategy provides a scalable and cost-efficient framework for microbial production of viral antigens and may be applicable to other glycoproteins of biomedical relevance. Keywords: Pichia pastoris , recombinant protein expression, hemagglutinin HA1, secretory expression, microbial cell factory, viral glycoprotein", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13107759", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13107759/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d1be691909dcd705d839", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe quantification of lentiviral vector (LVV) potency via titration is a critical quality control step for cell and gene therapies. However, standard functional titre assays are fundamentally limited by an inability to detect multiple integration events, procedural variations, and, most critically, a mass transport limitation created by the fluid overlay in conventional well plates. These issues, compounded by a lack of standardisation, lead to significant inter-laboratory variability and a systematic underestimation of true vector potency. In this study, we employed a microfluidic approach to create a more precisely engineered assay environment. We systematically evaluated channel depth, incubation time, vector concentration, and multiplicity of infection (MOI) for their impact on assay linearity, sensitivity, limit of detection, and reproducibility. A 0.2 mm deep channel provided linearity and reproducibility comparable to 96-well plates yet offered shorter incubation periods, and enhanced sensitivity, detecting activity down to a MOI of 0.0625 (corroborated by qPCR analysis) - a level at which conventional well plates fail. This establishes our microfluidic platform as a device-based analytical standard that transforms functional titre quantification from a variable protocol into a more reliable engineering solution for quality testing in cell therapy manufacturing.\n\nCandidates:\nA. However, standard functional titre assays are not fundamentally limited by an inability to detect multiple integration events, procedural variations, and, most critically, a mass transport limitation created by the fluid overlay in conventional well plates.\nB. These issues, compounded by a lack of standardisation, lead to significant inter-laboratory variability and a systematic underestimation of true vector potency.\nC. However, standard functional titre assays are fundamentally limited by an inability to detect multiple integration events, procedural variations, and, most critically, a mass transport limitation created by the fluid overlay in conventional well plates.\nD. The quantification of lentiviral vector (LVV) potency via titration is a critical quality control step for cell and gene therapies.\nE. The evidence does not state that these issues, compounded by a lack of standardisation, lead to significant inter-laboratory variability and a systematic underestimation of true vector potency.\nF. In this study, we employed a microfluidic approach to create a more precisely engineered assay environment.\nG. The quantification of lentiviral vector (LVV) potency via titration is not a critical quality control step for cell and gene therapies.\nH. The evidence does not state that in this study, we employed a microfluidic approach to create a more precisely engineered assay environment.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13111257", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13111257/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bc09d7ba824252e97507", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe basement membrane is a specialized extracellular matrix that compartmentalizes epithelial and endothelial tissues and provides essential structural and signaling cues for tissue organization. Whereas fibrillar collagens (Col) of the interstitial matrix (such as Col I and Col III) are widely used in tissue modeling, the networking collagens that scaffold the basement membrane, including human Col IV and Col VI, remain difficult to access. Commercial basement membrane surrogates such as Matrigel® are derived from murine tumors and are ill defined, dilute, variable, and incompatible with animal-free biomanufacturing. Thus, there is a crucial need for human-derived basement membrane matrices that are free of xenogenic contaminants and do not rely on breeding animals. Here, we analyzed whether human mesenchymal stromal cells (MSCs) could serve as a platform to produce self-assembling basement membrane components under chemically defined, xeno-free conditions. MSCs from placental, umbilical cord, bone marrow, and adipose tissues were cultured as three-dimensional spheroids and adherent multilayered sheets. Confocal imaging of whole-mount, decellularized spheroid matrices showed complex networks of fibronectin (FN) and Col IV with topological and organizational features characteristic of basement membrane. Perinatal MSCs produced distinct matrix architectures consisting of apical FN sheets underlaid by continuous Col IV networks. Time-resolved imaging of umbilical cord MSC-derived matrix sheets demonstrated a reproducible sequence of basement membrane assembly that parallels developmental tissue organization. Together, these findings demonstrate that human MSCs cultured entirely without entirely animal-derived components can synthesize functional basement membrane proteins that self-assemble into ordered, tissue-like scaffolds. In this work, we establish MSCs as a scalable, sustainable, and cruelty-free platform for manufacturing human basement membrane matrices for bioengineering and regenerative medicine applications.\n\nCandidates:\nA. The basement membrane is not a specialized extracellular matrix that compartmentalizes epithelial and endothelial tissues and provides essential structural and signaling cues for tissue organization.\nB. The basement membrane is a specialized extracellular matrix that compartmentalizes epithelial and endothelial tissues and provides essential structural and signaling cues for tissue organization.\nC. Commercial basement membrane surrogates such as Matrigel® are not derived from murine tumors and are ill defined, dilute, variable, and incompatible with animal-free biomanufacturing.\nD. Whereas fibrillar collagens (Col) of the interstitial matrix (such as Col I and Col III) are not widely used in tissue modeling, the networking collagens that scaffold the basement membrane, including human Col IV and Col VI, remain difficult to access.\nE. Whereas fibrillar collagens (Col) of the interstitial matrix (such as Col I and Col III) are widely used in tissue modeling, the networking collagens that scaffold the basement membrane, including human Col IV and Col VI, remain difficult to access.\nF. Commercial basement membrane surrogates such as Matrigel® are derived from murine tumors and are ill defined, dilute, variable, and incompatible with animal-free biomanufacturing.\nG. Thus, there is not a crucial need for human-derived basement membrane matrices that are free of xenogenic contaminants and do not rely on breeding animals.\nH. Thus, there is a crucial need for human-derived basement membrane matrices that are free of xenogenic contaminants and do not rely on breeding animals.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13111481", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13111481/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1f940320ab5066c7b2d1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nLaccases are multicopper enzymes capable of oxidizing a wide variety of compounds, standing out as green tools for industrial and environmental applications. However, production from native sources faces limitations that have driven advances in recombinant expression. This scoping review includes studies published between 2019 and 2025, selected from major online databases, describing recombinant fungal and bacterial laccases for environmental applications. Strategies to optimize expression are discussed, including the use of efficient vectors, codon optimization, His-tag addition, mutagenesis, and computational approaches, with an emphasis on their advantages, trade-offs, and limitations. Pichia pastoris is widely used for the expression of fungal laccases, while Escherichia coli is preferred for the expression of bacterial laccases. However, significant variability in expression efficiency and enzyme performance is observed across hosts and constructs. The use of alternative culture media, such as agro-industrial residues, is also explored as a sustainability-driven strategy; however, its applicability remains limited for certain heterologous expression systems. In the environmental field, recombinant laccases demonstrate high efficiency in the degradation of textile dyes, the treatment of lignocellulosic waste, the biodegradation of pharmaceuticals, and the degradation of various toxic compounds, often requiring redox mediators to achieve high conversion rates. Despite significant advances, challenges remain, such as inconsistent catalytic performance among studies and limited stability under extreme temperature and pH conditions. Overall, this review highlights the key challenges in developing recombinant laccase and demonstrates that advances in protein engineering, expression systems, and process optimization are crucial for environmental applications.\n\nCandidates:\nA. Laccases are not multicopper enzymes capable of oxidizing a wide variety of compounds, standing out as green tools for industrial and environmental applications.\nB. This scoping review includes studies published between 2020 and 2025, selected from major online databases, describing recombinant fungal and bacterial laccases for environmental applications.\nC. Strategies to optimize expression are not discussed, including the use of efficient vectors, codon optimization, His-tag addition, mutagenesis, and computational approaches, with an emphasis on their advantages, trade-offs, and limitations.\nD. This scoping review includes studies published between 2019 and 2025, selected from major online databases, describing recombinant fungal and bacterial laccases for environmental applications.\nE. The evidence does not state that however, production from native sources faces limitations that have driven advances in recombinant expression.\nF. However, production from native sources faces limitations that have driven advances in recombinant expression.\nG. Laccases are multicopper enzymes capable of oxidizing a wide variety of compounds, standing out as green tools for industrial and environmental applications.\nH. Strategies to optimize expression are discussed, including the use of efficient vectors, codon optimization, His-tag addition, mutagenesis, and computational approaches, with an emphasis on their advantages, trade-offs, and limitations.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13111515", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13111515/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0416607a54981142dcf1", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"+54%\", \"+66%\", \"+53%\", \"+65%\"]\n\nEvidence:\nIn large‐scale bioprocesses, mixing limitations and design constraints cause the onset of heterogeneous environments, subjecting the cells to continuously changing external conditions, often reducing their performance compared to laboratory conditions. This study evaluated the performance in producing a heterologous transaminase (TA) of a genome‐reduced Escherichia coli strain (RM214) in a STR‐PFR scale‐down system, benchmarking it against a wild‐type strain. Under cycles of glycerol limitation and starvation, combined with oxygen limitation in later process stages, RM214 outperformed the wild‐type strain. Due to its lower maintenance coefficient, RM214 showed a remarkable biomass increase of +53% and a boosted final volumetric activity with a +65% increase. These results were achieved with significantly reduced biomass‐specific substrate uptake rates and respiratory parameters, both crucial for optimizing large‐scale processes. This study underscores the applicability and enhanced robustness of genome‐reduced strains in heterogeneous large‐scale environments. Keywords: bioprocess scale‐up, bioreactor gradients, genome‐reduced strain, heterologous enzyme production, scale‐down system, transaminase", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13112000", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13112000/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b5822de731178aa97e2c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nFish waste disposal poses significant environmental and economic challenges, limiting sustainability in the marine food industry. Hence, sustainable valorization strategies are needed to enhance resource recovery while minimizing waste. As an approach, this study aimed to evaluate the potential of converting chum salmon ( Oncorhynchus keta ) head (CSH) waste into a high-value peptone for microbiological applications. Various proteolytic enzymes were screened for CSH hydrolysis, among which Protamex achieved the highest hydrolysis and recovery rates. The resulting chum salmon head peptone (CSHP) exhibited favorable characteristics, including a low average molecular mass (557 Da) and a high amino nitrogen content (4.9%), outperforming commercial animal (AP) and vegetable (VP) peptones. To assess its biotechnological potential, CSHP was evaluated as a nitrogen source for recombinant protein production and supported higher expression of human superoxide dismutase (hSOD) and human growth hormone (hGH) in Escherichia coli BL21(DE3), compared with AP, VP, and Luria–Bertani (LB) media. Furthermore, life cycle assessment revealed a substantially lower carbon footprint for CSHP production than that of conventional peptone sources. These findings suggest that CSHP is a reliable and sustainable alternative to traditional peptones, offering both therapeutic and industrial applications while contributing to marine waste reduction and circular bioeconomy strategies.\n\nCandidates:\nA. Various proteolytic enzymes were not screened for CSH hydrolysis, among which Protamex achieved the highest hydrolysis and recovery rates.\nB. The evidence does not state that as an approach, this study aimed to evaluate the potential of converting chum salmon ( Oncorhynchus keta ) head (CSH) waste into a high-value peptone for microbiological applications.\nC. Various proteolytic enzymes were screened for CSH hydrolysis, among which Protamex achieved the highest hydrolysis and recovery rates.\nD. Hence, sustainable valorization strategies are not needed to enhance resource recovery while minimizing waste.\nE. The evidence does not state that fish waste disposal poses significant environmental and economic challenges, limiting sustainability in the marine food industry.\nF. Fish waste disposal poses significant environmental and economic challenges, limiting sustainability in the marine food industry.\nG. Hence, sustainable valorization strategies are needed to enhance resource recovery while minimizing waste.\nH. As an approach, this study aimed to evaluate the potential of converting chum salmon ( Oncorhynchus keta ) head (CSH) waste into a high-value peptone for microbiological applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13113008", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13113008/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7d043d3412c46f4d419e", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe growing global population and increasing pressure on conventional food systems have intensified the search for sustainable and nutrient-rich protein sources. Blue foods derived from marine and freshwater organisms offer significant nutritional advantages and lower environmental footprints compared with many terrestrial animal proteins. However, challenges related to resource sustainability, processing, preservation, and product traceability limit their full potential. This review provides a broad overview of emerging technologies shaping the future of blue food systems, covering innovative production strategies, advanced processing techniques, and omics-based analytical approaches. Key developments in cellular aquaculture and cellular mariculture are discussed as promising alternatives to traditional fisheries and aquaculture, enabling the production of blue food through controlled cell cultivation. Additionally, alternative protein platforms including plant-based, fermentation-derived, and cultivated blue food analogues are assessed for their potential to enhance sustainability and diversify aquatic protein sources. Advanced structuring technologies such as extrusion, electrospinning, wet spinning, and 3D printing are highlighted for their roles in developing blue food analogues with improved texture and sensory attributes. Furthermore, non-thermal preservation techniques, including cold plasma (CP), high-pressure processing (HPP), pulsed electric fields (PEFs), and ultraviolet-based treatments, are reviewed for their effectiveness in improving microbial safety and extending shelf life while maintaining nutritional quality. The integration of omics technologies (proteomics, metabolomics, and lipidomics) provides deeper molecular insights into product quality, authenticity, and traceability within blue food supply chains. Collectively, these interdisciplinary advancements demonstrate strong potential to transform blue food production into a more resilient, sustainable, and technology-driven sector. Future progress will depend on overcoming challenges related to scalability, regulatory frameworks, and consumer acceptance to enable the successful commercialization of next-generation blue food products.\n\nCandidates:\nA. The evidence does not state that this review provides a broad overview of emerging technologies shaping the future of blue food systems, covering innovative production strategies, advanced processing techniques, and omics-based analytical approaches.\nB. Blue foods derived from marine and freshwater organisms offer significant nutritional advantages and lower environmental footprints compared with many terrestrial animal proteins.\nC. The evidence does not state that however, challenges related to resource sustainability, processing, preservation, and product traceability limit their full potential.\nD. This review provides a broad overview of emerging technologies shaping the future of blue food systems, covering innovative production strategies, advanced processing techniques, and omics-based analytical approaches.\nE. However, challenges related to resource sustainability, processing, preservation, and product traceability limit their full potential.\nF. The growing global population and increasing pressure on conventional food systems have intensified the search for sustainable and nutrient-rich protein sources.\nG. The evidence does not state that blue foods derived from marine and freshwater organisms offer significant nutritional advantages and lower environmental footprints compared with many terrestrial animal proteins.\nH. The evidence does not state that the growing global population and increasing pressure on conventional food systems have intensified the search for sustainable and nutrient-rich protein sources.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13115422", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13115422/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f69da6ed2e7454dbf950", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nPolyphenols are structurally diverse plant secondary metabolites with broad biological activities and growing applications across the food, health, and materials sectors. Conventional extraction based on organic solvents (e.g., methanol, ethanol) is often energy-intensive, inefficient, and environmentally burdensome. Ionic liquids (ILs) and deep eutectic solvents (DESs) have therefore emerged as greener alternatives for polyphenol extraction. This review evaluates recent advances in solvent design, extraction performance, and process sustainability. Imidazolium-based ILs frequently achieve high yields and selectivity, particularly when coupled with ultrasound or microwave-assisted extraction, but high cost, synthetic complexity, viscosity-related constraints, and potential toxicity hinder scaleup. By contrast, DESs—especially those derived from choline chloride or lactic acid—are easier to prepare, less costly, and more compatible with industrial implementation, with efficiency enhanced by tailoring hydrogen bond networks, water content, and process intensification. Critical downstream challenges persist for both solvent classes, notably in extract purification and solvent recovery due to low volatility; approaches such as resin adsorption, antisolvent precipitation, and direct formulation have been explored. Overall, ILs and DESs represent compelling alternatives to conventional solvents, and future progress will depend on integrated extraction–recovery strategies, systematic solvent selection, and validation under scalable, sustainable processing conditions.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13115927", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13115927/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3a3fafb149c804f7addd", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"0.3%\", \"1.3%\"]\n\nEvidence:\nPassiflora edulis (passion fruit) seed waste, an abundant by-product of the juice industry, is a promising source of piceatannol (PIC), a hydroxystilbene with superior antioxidant activity compared to resveratrol. However, its translation into a skin-targeted ingredient remains hindered by a lack of standardization and clinical validation. This review synthesizes current evidence on the dermatological potential of PIC and proposes a translational roadmap within a circular bioeconomy framework. Preclinical studies demonstrate that PIC exerts multi-target effects relevant to skin aging and acne, including ROS scavenging, anti-inflammatory activity via NF-κB/MAPK inhibition, suppression of melanogenesis, enhancement of hyaluronic acid and collagen synthesis, and antibacterial action against Cutibacterium acnes . However, clinical data are limited and methodologically inconsistent. To bridge this translational gap, we propose a development strategy focused on: (i) extract standardization with a proposed minimum PIC content (e.g., ≥0.3% w / w ); (ii) an integrated biorefinery approach for the co-production of seed oil and phenolic fractions; and (iii) a phase-gate pipeline encompassing dermal safety assessment, advanced delivery optimization, and biomarker-correlated clinical trials.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13116525", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13116525/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6c574f8c80332cea8dbf", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nManure-rearing insects offer a sustainable protein source for feed, but managing microbial risks is crucial. To identify an optimal harvest stage balancing yield and safety, we tracked internal bacterial communities in two fly species across development. Bacterial communities shifted significantly, with specific taxa dominating later pupal and adult stages. In contrast, dispersing-stage larvae—which stop feeding and leave the manure—maintained a more stable bacterial profile and were easier to collect. Harvesting at this dispersing stage therefore provides a practical balance between biomass production and microbial safety, supporting the design of safer manure-to-feed systems.\n\nCandidates:\nA. Manure-rearing insects offer a sustainable protein source for feed, but managing microbial risks is crucial.\nB. Bacterial communities shifted significantly, with specific taxa dominating later pupal and adult stages.\nC. The evidence does not state that to identify an optimal harvest stage balancing yield and safety, we tracked internal bacterial communities in two fly species across development.\nD. To identify an optimal harvest stage balancing yield and safety, we tracked internal bacterial communities in two fly species across development.\nE. In contrast, dispersing-stage larvae—which stop feeding and leave the manure—maintained a more stable bacterial profile and were not easier to collect.\nF. The evidence does not state that bacterial communities shifted significantly, with specific taxa dominating later pupal and adult stages.\nG. Manure-rearing insects offer a sustainable protein source for feed, but managing microbial risks is not crucial.\nH. In contrast, dispersing-stage larvae—which stop feeding and leave the manure—maintained a more stable bacterial profile and were easier to collect.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13116670", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13116670/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b8e0926f2f24171e07c1", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nHeterologous protein secretion in filamentous fungi is often constrained by limitations in signal peptide recognition and intracellular trafficking. Aspergillus oryzae , a food-grade industrial fungus, has a robust native secretory system. However, its capacity for recombinant protein secretion remains suboptimal. Here, we developed a two-step, carrier-free engineering strategy to enhance protein secretion in A. oryzae . We identified endogenous signal peptides among highly secreted proteins using a green fluorescent protein (GFP) reporter. The oryzin signal peptide SPAoalp1 increased GFP secretion 5.50-fold compared with a no-signal-peptide control. We co-overexpressed Aosly1 , a Sec1/Munc18 family protein that regulates soluble N-ethylmaleimide-sensitive factor attachment protein receptor–mediated vesicle trafficking, which, in combination with SPAoalp1 , increased secretion approximately two-fold compared with SPAlp1 control and ten-fold with no-SP control. Applying the engineered platform for genetic improvement of heterologous bovine κ-casein increased secretion from 0.11 to 0.24 mg/L. Physiological optimization further increased secretion. The developed system provided initial evidence for secretion of a ~12 kDa band consistent with Aopafb transcription, with MIC 90 values of 4.56–8.24% ( v / v ) against two Candida albicans strains and 4.68% ( v / v ) against Aspergillus niger . The system offers a modular framework for engineering fungal secretion and expands the utility of A. oryzae for recombinant protein production.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13117078", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13117078/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dca8fda14bbba6f4ccc5", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nChronic wounds represent a significant global healthcare challenge, affecting millions of patients and imposing substantial economic burdens on healthcare systems. Traditional wound management approaches often fail to address the complex pathophysiology underlying chronic wounds, including persistent inflammation, impaired angiogenesis, and disrupted extracellular matrix remodeling. Three-dimensional (3D) bioprinting has emerged as a transformative technology that enables the fabrication of patient-specific, biomimetic tissue constructs capable of addressing these intricate challenges. This comprehensive review synthesizes recent advances in 3D bioprinting for chronic wound treatment, examining bioprinting technologies, biomaterial innovations, mechanisms of wound healing, and clinical applications. Recent studies demonstrate that bioprinted constructs incorporating living cells, growth factors, and bioactive molecules can significantly accelerate wound closure, enhance vascularization, and restore functional skin architecture. Notable innovations include in situ bioprinting systems, photosynthetic scaffolds for oxygen delivery, and immunomodulatory bioinks. While significant technical challenges remain—including vascularization, scalability, and regulatory approval—the integration of advanced bioprinting techniques with regenerative medicine principles offers unprecedented opportunities for personalized chronic wound care and improved patient outcomes.\n\nCandidates:\nA. Traditional wound management approaches often fail to address the complex pathophysiology underlying chronic wounds, including persistent inflammation, impaired angiogenesis, and disrupted extracellular matrix remodeling.\nB. This comprehensive review synthesizes recent advances in 3D bioprinting for chronic wound treatment, examining bioprinting technologies, biomaterial innovations, mechanisms of wound healing, and clinical applications.\nC. The evidence does not state that chronic wounds represent a significant global healthcare challenge, affecting millions of patients and imposing substantial economic burdens on healthcare systems.\nD. Chronic wounds represent a significant global healthcare challenge, affecting millions of patients and imposing substantial economic burdens on healthcare systems.\nE. Three-dimensional (3D) bioprinting has emerged as a transformative technology that enables the fabrication of patient-specific, biomimetic tissue constructs capable of addressing these intricate challenges.\nF. The evidence does not state that traditional wound management approaches often fail to address the complex pathophysiology underlying chronic wounds, including persistent inflammation, impaired angiogenesis, and disrupted extracellular matrix remodeling.\nG. Three-dimensional (4D) bioprinting has emerged as a transformative technology that enables the fabrication of patient-specific, biomimetic tissue constructs capable of addressing these intricate challenges.\nH. This comprehensive review synthesizes recent advances in 4D bioprinting for chronic wound treatment, examining bioprinting technologies, biomaterial innovations, mechanisms of wound healing, and clinical applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13117182", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13117182/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a663d3178f880e1e868d", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"20%\", \"56.2%\", \"55.2%\", \"21%\"]\n\nEvidence:\nDocosahexaenoic acid (DHA) is an essential ω-3 polyunsaturated fatty acid (PUFA) with high nutritional and pharmaceutical value. The marine protist Aurantiochytrium is a promising industrial DHA producer; however, its DHA biosynthesis via the PUFA synthase pathway co-produces ω-6 docosapentaenoic acid (DPA), limiting DHA purity. Here, we introduced an ω-3 desaturase from Phytophthora infestans (Pin-O3D) into Aurantiochytrium sp. SD116. Functional validation in an Escherichia coli system co-expressing the native PUFA synthase confirmed that Pin-O3D converts DPA to DHA, shifting the DHA/DPA ratio from 1:1 to 2:1. Pin-O3D was then integrated into the fatty acid synthase (FAS) locus, simultaneously attenuating FAS activity and enabling heterologous gene expression. The engineered strain ΔFAS-Pin-O3D exhibited significantly ( p < 0.0001 in t -test) increased DHA content (55.2% of total fatty acids) and DHA/DPA ratio (5.91) in shake flasks, with no negative impact on biomass or lipid accumulation. Fed-batch fermentation confirmed the scalability of this strategy, achieving a >20% increase in DHA/DPA ratio. This study demonstrates that combining heterologous ω-3 desaturase expression with FAS attenuation is an effective approach for optimizing PUFA profiles in Aurantiochytrium .", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13117593", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13117593/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-acbae604f19acbea7bd2", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicroalgae are increasingly recognized as renewable biofactories for producing high-value bioactive molecules. However, their industrial exploitation is limited by their rigid cell walls, metabolite heterogeneity, and the energy-intensive nature of the extraction processes. Recent advances in process-intensification technologies, including microwave-assisted, ultrasound-assisted, enzymatic, pressurized liquid, and supercritical CO 2 -based methods, have significantly improved extraction efficiency and selectivity, with reported lipid recoveries exceeding 40–50% in some microalgal systems and carotenoid recoveries approaching 90% under optimized conditions. NADES-assisted systems further enhance mass transfer and solubilization through tailored hydrogen-bonding interactions, enabling selective extraction of polar and semi-polar metabolites under mild conditions. However, limitations remain, including high viscosity, variability in extraction performance, and challenges in solvent recovery and scale-up. This review critically evaluates the extraction efficiency, mechanistic basis, and sustainability of NADES-assisted processes, highlighting key limitations and identifying research priorities for their integration into scalable microalgal biorefinery systems.\n\nCandidates:\nA. However, their industrial exploitation is not limited by their rigid cell walls, metabolite heterogeneity, and the energy-intensive nature of the extraction processes.\nB. The evidence does not state that nADES-assisted systems further enhance mass transfer and solubilization through tailored hydrogen-bonding interactions, enabling selective extraction of polar and semi-polar metabolites under mild conditions.\nC. NADES-assisted systems further enhance mass transfer and solubilization through tailored hydrogen-bonding interactions, enabling selective extraction of polar and semi-polar metabolites under mild conditions.\nD. Recent advances in process-intensification technologies, including microwave-assisted, ultrasound-assisted, enzymatic, pressurized liquid, and supercritical CO 2 -based methods, have significantly improved extraction efficiency and selectivity, with reported lipid recoveries exceeding 40–50% in some microalgal systems and carotenoid recoveries approaching 90% under optimized conditions.\nE. Recent advances in process-intensification technologies, including microwave-assisted, ultrasound-assisted, enzymatic, pressurized liquid, and supercritical CO 3 -based methods, have significantly improved extraction efficiency and selectivity, with reported lipid recoveries exceeding 40–50% in some microalgal systems and carotenoid recoveries approaching 90% under optimized conditions.\nF. Microalgae are not increasingly recognized as renewable biofactories for producing high-value bioactive molecules.\nG. However, their industrial exploitation is limited by their rigid cell walls, metabolite heterogeneity, and the energy-intensive nature of the extraction processes.\nH. Microalgae are increasingly recognized as renewable biofactories for producing high-value bioactive molecules.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13118078", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13118078/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c811ec0a1e7078043980", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSustainable nanotechnologies derived from renewable resources are increasingly being positioned at the interface of green chemistry, advanced drug delivery, and translational pharmaceutics. Over the past decade, lignocellulosic nanomaterials, chitin/chitosan platforms, polysaccharide-based nanogels and nano-enabled hydrogels, lignin- and polyphenol-derived nanostructures, and bio-based lipid nanocarriers have been engineered through progressively eco-efficient routes, including solvent-minimized self-assembly, nanoprecipitation, spray drying, hot-melt extrusion, and microfluidic-assisted fabrication. This work provides a structured evidence map of nano-enabled drug delivery and therapeutic platforms derived from renewable biological resources. Specifically, we aim to (i) identify and classify nanoplatform classes and renewable feedstocks; (ii) summarize reported pharmaceutical critical quality attributes (CQAs) and performance and safety endpoints; and (iii) appraise how “renewability” and “green” claims are evidenced (feedstock origin vs. process sustainability) and how frequently translational readiness factors (scalability, quality control, regulatory alignment) are addressed. We critically compare renewable and conventional nanomaterial platforms across key translational dimensions, including carbon footprint, batch consistency, biodegradability, functional tunability, safety/persistence, and scale-up maturity. Finally, we delineate a practical translational pathway—from biomass sourcing and fractionation to nanoformulation, characterization/stability, and GMP scale-up—highlighting cross-cutting enablers such as lifecycle assessment, EHS/toxicology risk assessment, quality-by-design, and regulatory alignment. Collectively, the evidence supports renewable nanomaterials as viable, scalable candidates for next-generation therapeutics, provided that variability control, standardized characterization, and safety-by-design principles are embedded early in development.\n\nCandidates:\nA. The evidence does not state that this work provides a structured evidence map of nano-enabled drug delivery and therapeutic platforms derived from renewable biological resources.\nB. This work provides a structured evidence map of nano-enabled drug delivery and therapeutic platforms derived from renewable biological resources.\nC. Sustainable nanotechnologies derived from renewable resources are not increasingly being positioned at the interface of green chemistry, advanced drug delivery, and translational pharmaceutics.\nD. Specifically, we aim to (i) identify and classify nanoplatform classes and renewable feedstocks; (ii) summarize reported pharmaceutical critical quality attributes (CQAs) and performance and safety endpoints; and (iii) appraise how “renewability” and “green” claims are not evidenced (feedstock origin vs.\nE. Sustainable nanotechnologies derived from renewable resources are increasingly being positioned at the interface of green chemistry, advanced drug delivery, and translational pharmaceutics.\nF. Specifically, we aim to (i) identify and classify nanoplatform classes and renewable feedstocks; (ii) summarize reported pharmaceutical critical quality attributes (CQAs) and performance and safety endpoints; and (iii) appraise how “renewability” and “green” claims are evidenced (feedstock origin vs.\nG. Over the past decade, lignocellulosic nanomaterials, chitin/chitosan platforms, polysaccharide-based nanogels and nano-enabled hydrogels, lignin- and polyphenol-derived nanostructures, and bio-based lipid nanocarriers have been engineered through progressively eco-efficient routes, including solvent-minimized self-assembly, nanoprecipitation, spray drying, hot-melt extrusion, and microfluidic-assisted fabrication.\nH. The evidence does not state that over the past decade, lignocellulosic nanomaterials, chitin/chitosan platforms, polysaccharide-based nanogels and nano-enabled hydrogels, lignin- and polyphenol-derived nanostructures, and bio-based lipid nanocarriers have been engineered through progressively eco-efficient routes, including solvent-minimized self-assembly, nanoprecipitation, spray drying, hot-melt extrusion, and microfluidic-assisted fabrication.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13118287", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13118287/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7791efcec17adb4600fb", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicroorganisms represent the Earth’s most abundant biomass and a vast reservoir of genetic diversity. However, traditional agar plate methods fail to recover the vast majority of these species, leaving a “microbial dark matter” that holds immense potential for the discovery of novel antibiotics and bioactive compounds. While conventional techniques such as selective media and enrichment culture remain foundational, they are inherently limited by community biases and the inability to support low-abundance, oligotrophic species. To address these bottlenecks, a diverse array of innovative isolation strategies has emerged. This review systematically categorizes and evaluates these methodologies, ranging from in situ cultivation to high-resolution single-cell manipulation. We first examine membrane diffusion-based cultivation (e.g., iChip), which mimics natural microenvironments to resuscitate recalcitrant microbes. Subsequently, we explore high-throughput single-cell technologies, including microfluidics for physicochemical separation, optical tweezers for precise manipulation, and fluorescence-activated cell sorting (FACS). Special attention is given to Raman-activated cell sorting (RACS) as a label-free functional screening tool and reverse genomics for targeted capture. By synthesizing the strengths and limitations of these approaches, we propose integrated workflows designed to accelerate the mining of untapped microbial resources.\n\nCandidates:\nA. The evidence does not state that to address these bottlenecks, a diverse array of innovative isolation strategies has emerged.\nB. However, traditional agar plate methods fail to recover the vast majority of these species, leaving a “microbial dark matter” that holds immense potential for the discovery of novel antibiotics and bioactive compounds.\nC. To address these bottlenecks, a diverse array of innovative isolation strategies has emerged.\nD. Microorganisms represent the Earth’s most abundant biomass and a vast reservoir of genetic diversity.\nE. While conventional techniques such as selective media and enrichment culture remain foundational, they are inherently limited by community biases and the inability to support low-abundance, oligotrophic species.\nF. While conventional techniques such as selective media and enrichment culture remain foundational, they are not inherently limited by community biases and the inability to support low-abundance, oligotrophic species.\nG. The evidence does not state that microorganisms represent the Earth’s most abundant biomass and a vast reservoir of genetic diversity.\nH. The evidence does not state that however, traditional agar plate methods fail to recover the vast majority of these species, leaving a “microbial dark matter” that holds immense potential for the discovery of novel antibiotics and bioactive compounds.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13119467", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13119467/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-16aaab067fdaa7b6bd1c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAs the primary threat to the global pig industry, ASFV is a giant DNA virus encoding numerous proteins that are of unknown function yet essential for viral replication and virulence. Here, we developed two monoclonal antibodies targeting the replication-essential protein pE120R and mapped its linear epitopes. Notably, these epitopes are surface-accessible and highly conserved among ASFV strains. This work offers potent tools for investigating pE120R’s role in the ASFV life cycle and contributes to a deeper understanding of viral pathogenesis.\n\nCandidates:\nA. This work offers potent tools for investigating pE121R’s role in the ASFV life cycle and contributes to a deeper understanding of viral pathogenesis.\nB. Here, we developed two monoclonal antibodies targeting the replication-essential protein pE120R and mapped its linear epitopes.\nC. As the primary threat to the global pig industry, ASFV is a giant DNA virus encoding numerous proteins that are of unknown function yet essential for viral replication and virulence.\nD. Notably, these epitopes are surface-accessible and highly conserved among ASFV strains.\nE. As the primary threat to the global pig industry, ASFV is not a giant DNA virus encoding numerous proteins that are of unknown function yet essential for viral replication and virulence.\nF. Here, we developed two monoclonal antibodies targeting the replication-essential protein pE121R and mapped its linear epitopes.\nG. Notably, these epitopes are not surface-accessible and highly conserved among ASFV strains.\nH. This work offers potent tools for investigating pE120R’s role in the ASFV life cycle and contributes to a deeper understanding of viral pathogenesis.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13120246", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13120246/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a75aaaefe10a2b98626a", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"252 g/L\", \"10 g/L\", \"11 g/L\", \"93%\", \"253 g/L\", \"94%\", \"201 g/L\", \"200 g/L\"]\n\nEvidence:\nTrehalose 6-phosphate (T6P), often referred to as plant insulin, serves as a central signaling molecule that regulates carbon partitioning and sucrose flux in plants, thereby influencing key agronomic traits, such as grain yield and drought resilience. Foliar spraying of T6P has been shown to significantly enhance yield and stress tolerance of numerous grains and vegetables. To circumvent the dependency on coenzymes in natural T6P synthesis, we designed and validated an in vitro new-to-nature, coenzyme-free, minimal enzymatic pathway for the biosynthesis of T6P from maltose and polyphosphate. This three-enzyme cocktail contained maltose phosphorylase, trehalose 6-phosphate phosphorylase, and polyphosphate glucokinase, and did not involve any costly coenzymes, such as ATP or UDP. Through systematic optimization of experimental parameters (including pH, temperature, Mg²⁺, phosphate concentration, and enzyme ratios), a 93% molar yield of T6P was achieved from 10 g/L maltose. The scale-up of this in vitro bioprocess to a 100-mL bioreactor with 200 g/L maltose enabled the production of up to 541 mM T6P (i.e., 252 g/L T6P disodium salt) within two hours, corresponding to a very high volumetric productivity of 126 g/L/h. This study established a scalable and cost-competitive in vitro biomanufacturing of T6P. Chemical intervention based on timed foliar spraying of T6P to cultivated crops offers a simpler and safer agricultural practice compared to genetic modification of crops. The online version contains supplementary material available at 10.1186/s40643-026-01057-w. Keywords: Chemical intervention, Food security, In vitro BioTransformation, In vitro synthetic biology, Multi-enzyme molecular machine, Trehalose-6-phosphate", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13121652", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13121652/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-49942296d9be8d2515aa", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCurrent therapies for prostate cancer are limited by toxicity and acquired resistance, motivating development of biocompatible nanotherapeutics. Here, Solanum nigrum L.-derived nanovesicles (SDNVs) were isolated and characterized, showing a mean diameter of 93.4 ± 0.2 nm and a particle concentration of (4.2 ± 0.4) × 10 11 /mL. SDNV uptake, antitumor activity, and safety were assessed in PC-3 and RWPE-1 cells, while efficacy and biodistribution were further evaluated in a subcutaneous PC-3 xenograft mouse model following oral administration. SDNVs were readily internalized by PC-3 cells and suppressed viability, proliferation, and migration while inducing apoptosis. Senescence and cytotoxicity were not observed in RWPE-1 cells, indicating tumor-selective activity. The underlying mechanism was further investigated by transcriptome sequencing, followed by quantitative PCR, Western blotting, and pharmacologic rescue with the p53 inhibitor pifithrin-α (PFT-α). Transcriptomic analysis revealed 5058 differentially expressed genes, and further indicated activation of the p53/p21 axis and promoted cellular senescence in PC-3 cells after SDNV treatment. In vivo, oral SDNVs significantly reduced xenograft growth and showed no evident histopathologic toxicity in major organs. Collectively, SDNVs represent a plant-derived, biocompatible nanotherapeutic platform that selectively restrains prostate cancer growth via p53/p21-mediated senescence, supporting further development of senescence-targeting SDNV-based interventions. The online version contains supplementary material available at 10.1186/s40643-026-01055-y. Keywords: Plant-derived nanovesicles, Senescence, Prostate cancer, P53, Herbal medicine\n\nCandidates:\nA. SDNVs were readily internalized by PC-4 cells and suppressed viability, proliferation, and migration while inducing apoptosis.\nB. SDNV uptake, antitumor activity, and safety were assessed in PC-3 and RWPE-1 cells, while efficacy and biodistribution were further evaluated in a subcutaneous PC-3 xenograft mouse model following oral administration.\nC. SDNV uptake, antitumor activity, and safety were assessed in PC-4 and RWPE-1 cells, while efficacy and biodistribution were further evaluated in a subcutaneous PC-3 xenograft mouse model following oral administration.\nD. Current therapies for prostate cancer are limited by toxicity and acquired resistance, motivating development of biocompatible nanotherapeutics.\nE. Here, Solanum nigrum L.-derived nanovesicles (SDNVs) were isolated and characterized, showing a mean diameter of 93.4 ± 0.2 nm and a particle concentration of (4.2 ± 0.4) × 10 11 /mL.\nF. Current therapies for prostate cancer are not limited by toxicity and acquired resistance, motivating development of biocompatible nanotherapeutics.\nG. SDNVs were readily internalized by PC-3 cells and suppressed viability, proliferation, and migration while inducing apoptosis.\nH. Here, Solanum nigrum L.-derived nanovesicles (SDNVs) were isolated and characterized, showing a mean diameter of 94.4 ± 0.2 nm and a particle concentration of (4.2 ± 0.4) × 10 11 /mL.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13121694", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13121694/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2a41ee1156f0c25677f6", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nScalable suspension culture technologies are essential for the large-scale manufacturing of mesenchymal stem/stromal cells (MSCs). However, conventional microcarrier-based systems often fail to achieve scalable efficiency because cell–microcarrier dynamics vary across scales, necessitating complex, scale-dependent agitation protocols. To overcome this limitation, we designed a scale-up-oriented suspension culture system employing fluffy, fibrillated nanofiber scaffolds composed of chitosan and chitin. These nanofibers were readily suspended under continuous, gentle agitation and trapped cells on their surfaces, while spontaneously forming fluffy cell–scaffold aggregates through scaffold agglomeration. The low-adhesive chitosan nanofibers acted as physical spacers that prevented aggregate coalescence, thereby maintaining a microenvironment favorable for proliferation. By defining cell–scaffold aggregate size as the key scaling parameter—a biology-centric approach—, we successfully achieved scale-up from 30 mL to 5 L under continuous gentle agitation, yielding comparable specific growth rates of (3.66 ± 0.28) × 10 −2 h -1 (30 mL), (3.27 ± 0.40) × 10 −2 h -1 (1 L), and 3.50 × 10 −2 h -1 (5 L), and reaching a total yield of 4.23 × 10 9 cells in the 5-L bioreactor (a single run). These findings demonstrate that fluffy nanofiber scaffolds enable a scale-up strategy that reproduces the cellular microenvironment in a manner that is less affected by scale-dependent physical forces. This concept provides a new framework for designing scalable culture environments applicable not only to cell therapy manufacturing but also to culture supernatant production and cell–based food materials.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13121900", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13121900/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bc8a9887b41b21d3e445", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nOsteoarthritis (OA) remains without disease‐modifying sssstherapies, in part due to biological heterogeneity and a hostile joint microenvironment that undermines one‐size‐fits‐all interventions. Extracellular vesicles (EVs) play a dual role in OA pathophysiology: endogenous EVs disseminate pro‐inflammatory and catabolic signals that propagate cartilage degeneration, whereas therapeutic EVs most commonly derived from regenerative cell sources can deliver anti‐inflammatory and anabolic cues. We frame this contrast as the EV paradox and argue that it represents a central translational challenge explaining why robust preclinical efficacy has not yet translated into consistent clinical benefit. We synthesize current evidence on EV biology in joint tissues, outcomes across preclinical models, and early human studies that demonstrate safety but limited efficacy. This analysis highlights key barriers to translation, including impaired EV function within inflamed and mechanically active joints, rapid clearance and limited tissue targeting, mismatch between animal models and human disease, and insufficient standardization of EV potency. Building on these insights, we propose a precision‐medicine roadmap that emphasizes patient stratification, rational EV design, improved delivery strategies, and manufacturing frameworks linked to mechanism‐anchored endpoints. Together, this framework reframes the EV paradox from a translational obstacle into a design principle for developing disease‐modifying EV‐based therapies for OA.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13123879", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13123879/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e7010c857764f8da4a03", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSuccinic acid has been considered an important molecule in the transition of chemical manufacturing from fossil‐based to sustainable and future‐proof processes. While there has been extensive research on biotechnological succinic acid production from biomass, attempts to roll out bio‐succinic acid are impeded by its high price and remaining sustainability issues. Both drawbacks are interconnected and can be traced back to the used feedstocks and a wasteful expenditure of acid and base, among others. In this opinion, we discuss biochemical principles and metabolic pathways of next‐generation carbon assimilation and low‐pH fermentations to address these drawbacks. For this reason, we chart the potential for producing succinic acid from sustainable next‐generation feedstocks based on electron, carbon and ATP balances as well as relevant thermodynamic considerations. Furthermore, we summarize key advances in low‐pH succinic acid synthesis using acid‐tolerant yeasts and assess the suitability of selected acid tolerance mechanisms for industrial application. Eventually, we aim to inspire researchers to synthesize innovative approaches to realize competitive and sustainable biotechnological succinic acid production. Keywords: industrial biotechnology, low‐pH fermentation, metabolic engineering, one carbon metabolism, succinic acid\n\nCandidates:\nA. In this opinion, we discuss biochemical principles and metabolic pathways of next‐generation carbon assimilation and low‐pH fermentations to address these drawbacks.\nB. While there has been extensive research on biotechnological succinic acid production from biomass, attempts to roll out bio‐succinic acid are impeded by its high price and remaining sustainability issues.\nC. The evidence does not state that succinic acid has been considered an important molecule in the transition of chemical manufacturing from fossil‐based to sustainable and future‐proof processes.\nD. The evidence does not state that in this opinion, we discuss biochemical principles and metabolic pathways of next‐generation carbon assimilation and low‐pH fermentations to address these drawbacks.\nE. Succinic acid has been considered an important molecule in the transition of chemical manufacturing from fossil‐based to sustainable and future‐proof processes.\nF. While there has been extensive research on biotechnological succinic acid production from biomass, attempts to roll out bio‐succinic acid are not impeded by its high price and remaining sustainability issues.\nG. Both drawbacks are interconnected and can be traced back to the used feedstocks and a wasteful expenditure of acid and base, among others.\nH. Both drawbacks are not interconnected and can be traced back to the used feedstocks and a wasteful expenditure of acid and base, among others.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13124660", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13124660/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f586c95707e635437189", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMonoclonal antibodies (mAbs) constitute the most rapidly expanding and therapeutically impactful class of biological medicines, driven by their exceptional target specificity, modular engineering capabilities, predictable pharmacokinetics facilitated by neonatal Fc receptor (FcRn) recycling, and favorable safety profiles attributable to mechanism-based pharmacology. As foundational patents on blockbuster antibody therapeutics expire, biosimilar antibodies have become a vital mechanism to enhance global accessibility, improve healthcare sustainability, and promote market competition. Simultaneously, regulatory agencies worldwide are substantially restructuring development expectations to emphasize analytical precision, mechanistic understanding, human-relevant methodologies, and ethical nonclinical practices aligned with the 3Rs principles of replacement, reduction, and refinement. This comprehensive review analyzes the convergence of innovator and biosimilar antibody development toward an analytics-first comparability paradigm, mechanism-based safety assessment, and targeted clinical confirmation based on residual uncertainty rather than prescriptive requirements. It systematically incorporates regulatory positions from the United States Food and Drug Administration (FDA), European Medicines Agency (EMA), World Health Organization (WHO), Japan’s Pharmaceuticals and Medical Devices Agency (PMDA), Health Canada, and the International Council for Harmonisation (ICH), together with contemporary peer-reviewed research literature. Focus is given to the FDA Modernization Act 2.0, which abolished statutory mandates for animal testing, and recent FDA draft guidance—which are provisional and open to revision pending finalization—addressing streamlined nonclinical safety studies for monospecific antibodies and science-based approaches to waiving comparative clinical efficacy studies (CES). The scientific boundaries distinguishing products amenable to streamlined development from those requiring tailored approaches are delineated, including the exclusion of polyclonal antibody preparations from biosimilar frameworks and the analytical complexities associated with conjugated antibodies and multispecific constructs. Important limitations of analytics-first approaches are also addressed, including scenarios involving heightened immunogenicity risk, novel targets with limited clinical experience, or insufficiently validated pharmacodynamic markers. These developments are examined alongside counterarguments and residual areas of regulatory skepticism. While the trajectory of regulatory reform appears to favor evidence-efficient, science-driven models for antibody development, this review explicitly distinguishes between established regulatory consensus, emerging draft guidance positions, and the author’s scholarly interpretation, recognizing that the pace and scope of regulatory change remain subject to ongoing deliberation.\n\nCandidates:\nA. The evidence does not state that as foundational patents on blockbuster antibody therapeutics expire, biosimilar antibodies have become a vital mechanism to enhance global accessibility, improve healthcare sustainability, and promote market competition.\nB. The evidence does not state that monoclonal antibodies (mAbs) constitute the most rapidly expanding and therapeutically impactful class of biological medicines, driven by their exceptional target specificity, modular engineering capabilities, predictable pharmacokinetics facilitated by neonatal Fc receptor (FcRn) recycling, and favorable safety profiles attributable to mechanism-based pharmacology.\nC. Monoclonal antibodies (mAbs) constitute the most rapidly expanding and therapeutically impactful class of biological medicines, driven by their exceptional target specificity, modular engineering capabilities, predictable pharmacokinetics facilitated by neonatal Fc receptor (FcRn) recycling, and favorable safety profiles attributable to mechanism-based pharmacology.\nD. As foundational patents on blockbuster antibody therapeutics expire, biosimilar antibodies have become a vital mechanism to enhance global accessibility, improve healthcare sustainability, and promote market competition.\nE. Simultaneously, regulatory agencies worldwide are substantially restructuring development expectations to emphasize analytical precision, mechanistic understanding, human-relevant methodologies, and ethical nonclinical practices aligned with the 3Rs principles of replacement, reduction, and refinement.\nF. Simultaneously, regulatory agencies worldwide are substantially restructuring development expectations to emphasize analytical precision, mechanistic understanding, human-relevant methodologies, and ethical nonclinical practices aligned with the 4Rs principles of replacement, reduction, and refinement.\nG. The evidence does not state that this comprehensive review analyzes the convergence of innovator and biosimilar antibody development toward an analytics-first comparability paradigm, mechanism-based safety assessment, and targeted clinical confirmation based on residual uncertainty rather than prescriptive requirements.\nH. This comprehensive review analyzes the convergence of innovator and biosimilar antibody development toward an analytics-first comparability paradigm, mechanism-based safety assessment, and targeted clinical confirmation based on residual uncertainty rather than prescriptive requirements.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13124720", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13124720/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4bd9c24a3c0ad88484c0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe spectral preprocessing step of data derived from Raman spectroscopy is important for chemometric models’ calibration, which allows real-time monitoring of pharmaceutical bioprocesses. Thus, the present work aimed to establish, with statistical criteria, the best combination of spectral filters for the biochemical monitoring of the baculovirus/ Sf9 insect cell system. The production of rabies virus-like particles was used as a model. Combining moving window smoothing and offset baseline correction spectral filters demonstrated the highest efficacy in biochemically monitoring the baculovirus/ Sf9 insect cell system using Raman spectroscopic data and Partial Least Squares regression. However, when applying Artificial Neural Network modeling, a different set of filters proved more effective: wavelet denoise spectral smoothing, asymmetric least square baseline correction, standard normal variate normalization, and quadratic first derivative. The simulation results demonstrate that by following these guidelines for spectral preprocessing, cell viability, glucose, lactate, glutamine, glutamate, and ammonium can be satisfactorily monitored in real-time, but not the density of viable cells. The absolute errors for cell viability, glucose, lactate, glutamine, glutamate, and ammonium were lower than 12%, 0.47 g L − 1 , 8.95 mg L − 1 , 0.11 g L − 1 , 0.10 g L − 1 , 3.21 mg L − 1 , respectively, which are suitable for bioprocess in-line monitoring and control through a soft sensor. The online version contains supplementary material available at 10.1007/s00449-026-03301-1. Keywords: Artificial neural network, Bioprocess monitoring, Partial least squares, Raman spectroscopy, SARS-CoV-2, Virus-like particles\n\nCandidates:\nA. Combining moving window smoothing and offset baseline correction spectral filters demonstrated the highest efficacy in biochemically monitoring the baculovirus/ Sf9 insect cell system using Raman spectroscopic data and Partial Least Squares regression.\nB. The production of rabies virus-like particles was not used as a model.\nC. The production of rabies virus-like particles was used as a model.\nD. Combining moving window smoothing and offset baseline correction spectral filters demonstrated the highest efficacy in biochemically monitoring the baculovirus/ Sf10 insect cell system using Raman spectroscopic data and Partial Least Squares regression.\nE. Thus, the present work aimed to establish, with statistical criteria, the best combination of spectral filters for the biochemical monitoring of the baculovirus/ Sf10 insect cell system.\nF. The spectral preprocessing step of data derived from Raman spectroscopy is important for chemometric models’ calibration, which allows real-time monitoring of pharmaceutical bioprocesses.\nG. The spectral preprocessing step of data derived from Raman spectroscopy is not important for chemometric models’ calibration, which allows real-time monitoring of pharmaceutical bioprocesses.\nH. Thus, the present work aimed to establish, with statistical criteria, the best combination of spectral filters for the biochemical monitoring of the baculovirus/ Sf9 insect cell system.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13124868", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13124868/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7a6233cf1d676a4cd58c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe fermentation of industrial wastes derived from water recovery (pretreated microalgae biomass, PTMB), biodiesel (glycerol, GLY), and ethanol (vinasse, VIN)) is a promising sustainable alternative for producing volatile fatty acids (VFA). In this study, controlled fermentation experiments were performed using PTMB-VIN and PTMB-GLY mixtures with varying substrate concentrations and retention times to optimize the VFA yield and production efficiency. Acid fermentation was optimized at a 3-day hydraulic retention time, and methanogenic reactions were enhanced at longer hydraulic retention times. Increasing the organic loading rate resulted in a high VFA yield (13.10 g VFA-COD . L −1 ) and a conversion rate of approximately 40%. GLY fermentation followed the oxidative and reductive pathways at a balanced redox potential. PTMB is linked to the oxidative pathway, and GLY is involved in the reductive pathway. Tests with PTMB-GLY resulted in limiting conditions, but significant observations regarding the metabolic pathway and the effect of PTMB were made. Keywords: Dark fermentation; Acidogenesis; Cosubstrate; Hydraulic retention time; 1,3 propanediol; Metabolic pathways\n\nCandidates:\nA. Increasing the organic loading rate resulted in a high VFA yield (13.10 g VFA-COD .\nB. In this study, controlled fermentation experiments were not performed using PTMB-VIN and PTMB-GLY mixtures with varying substrate concentrations and retention times to optimize the VFA yield and production efficiency.\nC. Acid fermentation was optimized at a 4-day hydraulic retention time, and methanogenic reactions were enhanced at longer hydraulic retention times.\nD. Acid fermentation was optimized at a 3-day hydraulic retention time, and methanogenic reactions were enhanced at longer hydraulic retention times.\nE. The fermentation of industrial wastes derived from water recovery (pretreated microalgae biomass, PTMB), biodiesel (glycerol, GLY), and ethanol (vinasse, VIN)) is a promising sustainable alternative for producing volatile fatty acids (VFA).\nF. Increasing the organic loading rate resulted in a high VFA yield (14.10 g VFA-COD .\nG. In this study, controlled fermentation experiments were performed using PTMB-VIN and PTMB-GLY mixtures with varying substrate concentrations and retention times to optimize the VFA yield and production efficiency.\nH. The fermentation of industrial wastes derived from water recovery (pretreated microalgae biomass, PTMB), biodiesel (glycerol, GLY), and ethanol (vinasse, VIN)) is not a promising sustainable alternative for producing volatile fatty acids (VFA).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13124889", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13124889/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0e548f1b146919982f7d", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"97%\", \"50%\", \"51%\", \"98%\"]\n\nEvidence:\nChinese hamster ovary (CHO) cells constitute the industry-standard platform for the production of complex therapeutic proteins, yet genomic heterogeneity arising from random integration leads to clonal variability and unstable expression, necessitating a robust cell line development process to efficiently isolate stable, high-expressing clones. By employing an optimized integration strategy, recombinant cell line performance can be enhanced through improved genomic stability and increased productivity. In this study, GFP reporter analysis demonstrated that the PiggyBac system significantly boosts the yield of both stable and high-expression clones. Subsequently, transposase modified with a nuclear localization signal (NLS) exhibited superior stability in recombinant cell lines and polyclonal pools. The nucleoplasmin NLS resulted in transgene integration into genomic loci that promote enhanced and more stable expression, thereby improving clonal distribution. Furthermore, this strategy also increased recombinant mAb expression by 97% in pools while enhancing average and specific productivity in derived cell lines. Additionally, recombinant suspension cells generated with the optimized system exhibited comparable performance, with over 50% of minipools showing robust growth. Overall, these findings highlight the promising utility of the NLS-optimized PiggyBac system for improving transgene stability and expression while streamlining the cell line screening process. The online version contains supplementary material available at 10.1186/s40643-026-01056-x. Keywords: Stability, Cell line construction, Transposon, PiggyBac system, Nuclear localization signal, Production", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13125444", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13125444/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a12dfe4aaa1905c1acf3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCombinatorial barcoding technologies for single-cell nucleotide sequencing, such as split-pool ligation protocols, involve sequential rounds of cell barcoding to uniquely tag individual cells. The rapid adoption of combinatorial barcoding in recent years is due in part to its scalability across cells and samples. However, small shifts in barcode positions within sequencing reads caused by technical artifacts, e.g. during barcode incorporation or synthesis, can impact the accurate assignment of reads to cell barcodes. Existing processing tools typically assume barcodes contain fixed-length nucleotide sequences located at fixed positions within reads, overlooking any positional variability. Consequently, reads containing truncated or mispositioned barcodes are discarded during initial data processing steps leading to significant data loss. To solve this limitation and maximize the retention of sequencing reads from single-cell combinatorial barcoding experiments, we introduce scarecrow . This tool screens a subsample of reads to generate position-specific barcode profiles, which are then used to flexibly identify barcode sequences in each read whilst accounting for positional errors, a phenomenon we refer to as “jitter”. Barcode matches are then prioritized to minimize nucleotide mismatches and the degree of jitter. These initial profiles are subsequently used to extract and error correct barcode combinations in high throughput sequencing libraries. By incorporating jitter into barcode error correction, scarecrow enables greater data recovery and improved downstream single-cell analyses. Scarecrow is fully open access, implemented in Python, and generates output files using standardized sequence file formats for maximal interoperability. A detailed explanation of the scarecrow workflow can be found in the supplementary materials. Scarecrow is freely available on GitHub https://github.com/MorganResearchLab/scarecrow and Zenodo https://doi.org/10.5281/zenodo.18621784 .\n\nCandidates:\nA. The rapid adoption of combinatorial barcoding in recent years is due in part to its scalability across cells and samples.\nB. However, small shifts in barcode positions within sequencing reads caused by technical artifacts, e.g.\nC. The evidence does not state that combinatorial barcoding technologies for single-cell nucleotide sequencing, such as split-pool ligation protocols, involve sequential rounds of cell barcoding to uniquely tag individual cells.\nD. Combinatorial barcoding technologies for single-cell nucleotide sequencing, such as split-pool ligation protocols, involve sequential rounds of cell barcoding to uniquely tag individual cells.\nE. during barcode incorporation or synthesis, can impact the accurate assignment of reads to cell barcodes.\nF. The rapid adoption of combinatorial barcoding in recent years is not due in part to its scalability across cells and samples.\nG. during barcode incorporation or synthesis, cannot impact the accurate assignment of reads to cell barcodes.\nH. The evidence does not state that however, small shifts in barcode positions within sequencing reads caused by technical artifacts, e.g.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13125751", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13125751/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9337ee6ab06e8e884971", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMammalian cell cultures are widely used for producing complex biopharmaceuticals that require human-like post-translational modifications, such as antibody-based therapeutics. Traditionally, serum-supplemented media support high cell viability and productivity; however, regulatory and scientific requirements demand serum-free conditions for clinical-grade manufacture. Recently, a novel fusion protein, the anti-huCD20(hγ1)-IL2no-alpha immunocytokine (IC), was presented as a promising therapeutic alternative, mostly for relapsed or refractory (r/r) B-cell non-Hodgkin lymphoma (B-NHL) patients, considering currently approved therapies. Three Chinese hamster ovary clones (K1 strain) producing the anti-huCD20(hγ1)-IL2no-alpha IC were generated and adapted to serum-free suspension culture. We performed a kinetic characterization of one clone in two culture media with different nutritional compositions, evaluating cell growth, productivity, cell cycle progression and mTOR signaling. The IC was purified by Protein A, then evaluated for identity, aggregation profile, CD20 recognition, CTLL-2 cytokine activity, ex vivo B-cell depletion in PBMC from r/r B-NHL patients and antitumor efficacy in immunocompetent C57BL/6 mice bearing EL4-hCD20- cells. The results demonstrated noticeable differences in cell growth and productivity in both batch and pseudo-perfusion performance, likely due to an influence on cell-cycle progression and mTOR signaling. The purified IC maintained its structural integrity while exhibiting an improved aggregation profile compared to serum-containing cultures. Furthermore, key biological activities, including B-cell depletion and antitumoral effects, remained intact. This research highlights the successful serum-free production of a functional anti-huCD20(hγ1)-IL2no-alpha IC, reinforcing its potential for biopharmaceutical development.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13126307", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13126307/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e32704e6a2e8c9932b9d", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"3.7 g/L\", \"2.7 g/L\", \"44%\", \"4.3 g/L\", \"10.2 g/L\", \"11.2 g/L\", \"5.3 g/L\", \"45%\"]\n\nEvidence:\nSoybean whey wastewater (SWW), a rich source of soybean whey protein (SWP), is prone to microbial rancidity, posing environmental and resource challenges. This study explores the causes of rancidity—characterized by a pungent, sour, and putrid odor—in the effluents of sealed buffer tank during SWP recovery via pneumatic flotation. Metagenome, bacterial diversity, and HPLC analyses showed the obligate anaerobe Megasphaera spp. dominated rancid effluents (up to 44% abundance), consumed lactate (decreasing from 10.2 g/L in influent to 2.7 g/L in effluent), and produced malodorous propionate and butyrate (up to 3.6 and 4.3 g/L, respectively). Three mitigation strategies were assessed: (1) full‐scale high‐throughput aeration—likely effective but energy‐ and cost‐intensive; (2) local aeration—low‐cost but weakly inhibitory; and (3) microbial intervention using the probiotic Enterococcus faecium LBSW, which colonizes the buffer tank, with localized aeration used only if microbial control fails. Strategy (3) was adopted for its energy and cost efficiency, successfully reducing pollution and supporting SWP recovery. Although the biosafety of E. faecium LBSW in food applications requires caution, the recovered SWP is primarily intended for animal feed, and subsequent high‐temperature drying and sterilization (> 120°C) also offer potential for food‐grade use.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13126613", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13126613/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-78ac2f3205b9640e6fcb", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nChimeric antigen receptor (CAR) T cell therapies have achieved clinical success in autologous treatment of hematological malignancies. However, their broader application remains limited. Beyond the biological challenges associated with the immunosuppressive microenvironment of solid tumors and the graft-versus-host disease risks inherent to allogeneic settings, the widespread adoption of CAR T cells is hindered by the high costs, long and complex vein-to-vein timelines and variability in product quality. Rapid CAR T cell manufacturing has emerged as an alternative paradigm that prioritizes shortened production workflows and preservation of naïve and stem-like T cell phenotypes with superior in vivo expansion, persistence and anti-tumor efficacy. This review examines rapid CAR T cell manufacturing from a bioprocess engineering perspective, focusing on how critical process parameters can be explored to shape CAR T cell phenotype and function within shortened timelines. We highlight key technological enablers, including automation, microfluidic systems, process analytical technologies, artificial intelligence-driven bioprocess control, quality control methodologies as well as safety considerations unique to accelerated workflows. Emerging CAR T cell manufacturing models, such as point-of-care production and in vivo CAR T cells generation, are also discussed. These insights outline engineering strategies to enable faster, more consistent and clinically effective CAR T cells. Keywords: CAR T cell, Stem-like CAR T cells, Rapid manufacturing, Bioprocess parameters, Point‑of‑care manufacturing, Bioprocess control\n\nCandidates:\nA. The evidence does not state that chimeric antigen receptor (CAR) T cell therapies have achieved clinical success in autologous treatment of hematological malignancies.\nB. The evidence does not state that however, their broader application remains limited.\nC. Rapid CAR T cell manufacturing has emerged as an alternative paradigm that prioritizes shortened production workflows and preservation of naïve and stem-like T cell phenotypes with superior in vivo expansion, persistence and anti-tumor efficacy.\nD. Chimeric antigen receptor (CAR) T cell therapies have achieved clinical success in autologous treatment of hematological malignancies.\nE. However, their broader application remains limited.\nF. Beyond the biological challenges associated with the immunosuppressive microenvironment of solid tumors and the graft-versus-host disease risks inherent to allogeneic settings, the widespread adoption of CAR T cells is hindered by the high costs, long and complex vein-to-vein timelines and variability in product quality.\nG. Beyond the biological challenges associated with the immunosuppressive microenvironment of solid tumors and the graft-versus-host disease risks inherent to allogeneic settings, the widespread adoption of CAR T cells is not hindered by the high costs, long and complex vein-to-vein timelines and variability in product quality.\nH. The evidence does not state that rapid CAR T cell manufacturing has emerged as an alternative paradigm that prioritizes shortened production workflows and preservation of naïve and stem-like T cell phenotypes with superior in vivo expansion, persistence and anti-tumor efficacy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13126794", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13126794/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e5a9ac9614fa05e21194", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"64%\", \"41–50%\", \"63%\", \"40–50%\"]\n\nEvidence:\nChitinases from Trichoderma species exhibit strong antifungal activity and high biocontrol potential, yet their industrial utilization has been constrained by low heterologous secretion efficiency and costly purification processes. In this study, the Generally Recognized As Safe yeast Saccharomyces cerevisiae Y2805 was engineered to secrete chitinase Tch36 from a Korean isolate of T. atroviride , using a rice α-amylase signal peptide under a constitutive glyceraldehyde-3-phosphate dehydrogenase (GPD) promoter. When cultivated in glycerol–colloidal chitin medium, the recombinant yeast exhibited approximately a fivefold increase in measurable chitinase activity relative to the SC mock control, whereas the empty-vector control showed only a modest increase. The crude, non-concentrated culture filtrate (approximately 1000 U L⁻ 1 ) displayed statistically significant antifungal activity against 12 fungal species, including 9 phytopathogens and 3 opportunistic Aspergillus species. Among the phytopathogens, Fusarium graminearum was one of the most strongly inhibited species, showing approximately 40–50% suppression of colonial growth on solid medium. In liquid culture, microscopy-based germination assays revealed ≥ 63% inhibition of early hyphal elongation in Botrytis cinerea and A. niger . All antifungal effects were evaluated relative to filtrates from the empty-vector control prepared under equivalent dilution conditions. The culture filtrate also enhanced protoplast formation, providing direct evidence of chitinase-associated cell wall weakening. Collectively, these results establish a purification-free, yeast-based heterologous secretion platform capable of producing active Trichoderma chitinase with inhibitory effects on diverse plant- and animal-associated fungi. This strategy has practical potential for biocontrol applications and for sustainable bioprocessing technologies based on microbial chitinase production. The online version contains supplementary material available at 10.1186/s40643-026-01054-z. Keywords: Saccharomyces cerevisiae , Trichoderma atroviride , Chitinase, Yeast secretion, Antifungal biocontrol, Extracellular enzyme production", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13129169", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13129169/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3f69adde37925a8cbc25", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nOptimization of biotechnological processes is traditionally limited by time-consuming trial-and-error approaches and the complexity of simultaneously optimizing multiple, often conflicting objectives. This applies particularly to plant tissue culture medium design, which therefore serves as the application case in this study. Recent advances in machine learning and evolutionary algorithms offer powerful alternatives, yet 80% of published studies rely on licensed software, and systematic data-driven optimization frameworks remain scarce. This creates significant barriers to adoption in both academic and commercial plant biotechnology. We introduce ADAM (Advanced Design and AI-Driven Modeling for Plant Tissue Culture Media Optimization), an open-access, web-based platform that transforms protocol development into a data-driven computational process. ADAM implements a complete ML-EA workflow through five integrated modules: 1. Design of Experiments (five different concepts) for systematic parameter exploration, 2. Data Preparation with automated quality control, and 3. Model Building using nine machine learning algorithms with automated selection. The platform enables Optimization (4.) through four advanced evolutionary algorithms (genetic algorithm, particle swarm optimization, NSGA-II, SMS-EMOA) for single- and multi-objective problems, with Evaluation (5.) tools to compare original versus optimized solutions. Validation across two plant tissue culture applications showed that ADAM’s models matched or exceeded the predictive performance of manually optimized approaches in the original studies. The platform successfully identified multiple optimal culture conditions balancing conflicting objectives, providing experimentally testable predictions that reduce the trial-and-error cycle. Deployed as a browser-based application requiring neither specialized hardware nor software licenses, ADAM democratizes advanced AI optimization for plant biotechnology, eliminating traditional barriers to entry while maintaining the rigor and flexibility required for scientific research. The online version contains supplementary material available at 10.1186/s13007-026-01534-5. Keywords: Plant tissue culture, Machine learning, Predictive modelling, Evolutionary algorithms, Culture media optimization\n\nCandidates:\nA. Recent advances in machine learning and evolutionary algorithms offer powerful alternatives, yet 80% of published studies rely on licensed software, and systematic data-driven optimization frameworks remain scarce.\nB. This creates significant barriers to adoption in both academic and commercial plant biotechnology.\nC. Optimization of biotechnological processes is traditionally limited by time-consuming trial-and-error approaches and the complexity of simultaneously optimizing multiple, often conflicting objectives.\nD. The evidence does not state that this applies particularly to plant tissue culture medium design, which therefore serves as the application case in this study.\nE. The evidence does not state that this creates significant barriers to adoption in both academic and commercial plant biotechnology.\nF. Recent advances in machine learning and evolutionary algorithms offer powerful alternatives, yet 81% of published studies rely on licensed software, and systematic data-driven optimization frameworks remain scarce.\nG. This applies particularly to plant tissue culture medium design, which therefore serves as the application case in this study.\nH. Optimization of biotechnological processes is not traditionally limited by time-consuming trial-and-error approaches and the complexity of simultaneously optimizing multiple, often conflicting objectives.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13130802", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13130802/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-25273e1f35fc27ccef8c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGas-phase bioprocesses that immobilize microbial cells on solid carriers enable efficient conversion of volatile or poorly water-soluble substrates. Acinetobacter sp. Tol 5 is a highly adhesive bacterium capable of utilizing various hydrocarbons, making it a promising chassis candidate for gas-phase bioprocesses. However, Gas-phase bioprocesses expose cells to fluctuating humidity and transient desiccation that can compromise viability and catalytic performance. In Gram-negative bacteria, especially in pathogens, desiccation is known to impose multifactorial stress, such as loss of cellular water and energetics, damage to DNA and proteins, oxidative stress, and disruption of the cell envelope. In contrast, for chassis strains used in or being considered for gas-phase bioprocesses, their desiccation tolerance including robustness of biocatalytic activity following humidity fluctuations and responses to desiccation stress remain incompletely defined. Here, we evaluated the viability, energy status, and gas-phase toluene degradation of Tol 5 as a chassis for gas-phase processes after controlled desiccation (8%, 52%, and > 95% RH), in comparison with Acinetobacter baylyi ADP1, Pseudomonas putida , and Escherichia coli . To minimize adhesion-related bias in post-desiccation measurements, we used an ataA -deficient Tol 5 mutant (Tol 5 Δ ataA ) for the assays. Acinetobacter strains maintained high viability during desiccation for 16 days, whereas P. putida and E. coli showed significant loss of viability at 8% RH and 52% RH. Intracellular ATP measurements further indicated that desiccation reduced intracellular ATP in all strains, but E. coli rapidly exhausted ATP at 8% and 52% RH, whereas Acinetobacter strains and P. putida retained intracellular ATP. In a gas-phase toluene degradation assay, immobilized Tol 5 retained higher toluene-degrading activity after desiccation than P. putida mt-2. Transcriptome profiling revealed a multilayered Tol 5 response involving DNA and RNA maintenance, cell envelope trafficking and remodeling, redox-responsive functions, and broad repression of growth-associated metabolism. Our results highlight Acinetobacter , particularly Tol 5, as a promising chassis candidate for gas-phase bioprocesses and suggest potential mechanistic targets for stabilizing bacterial cells under low-water-activity conditions. The online version contains supplementary material available at 10.1186/s13036-026-00668-3.\n\nCandidates:\nA. In Gram-negative bacteria, especially in pathogens, desiccation is known to impose multifactorial stress, such as loss of cellular water and energetics, damage to DNA and proteins, oxidative stress, and disruption of the cell envelope.\nB. Tol 6 is a highly adhesive bacterium capable of utilizing various hydrocarbons, making it a promising chassis candidate for gas-phase bioprocesses.\nC. However, Gas-phase bioprocesses expose cells to fluctuating humidity and transient desiccation that can compromise viability and catalytic performance.\nD. Tol 5 is a highly adhesive bacterium capable of utilizing various hydrocarbons, making it a promising chassis candidate for gas-phase bioprocesses.\nE. However, Gas-phase bioprocesses expose cells to fluctuating humidity and transient desiccation that cannot compromise viability and catalytic performance.\nF. In Gram-negative bacteria, especially in pathogens, desiccation is not known to impose multifactorial stress, such as loss of cellular water and energetics, damage to DNA and proteins, oxidative stress, and disruption of the cell envelope.\nG. Gas-phase bioprocesses that immobilize microbial cells on solid carriers enable efficient conversion of volatile or poorly water-soluble substrates.\nH. The evidence does not state that gas-phase bioprocesses that immobilize microbial cells on solid carriers enable efficient conversion of volatile or poorly water-soluble substrates.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13130816", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13130816/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4fe6061c011bb910543f", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"1001 mL\", \"1000 mL\", \"10 mL\", \"30 °C\", \"11 mL\", \"35 °C\", \"36 °C\", \"31 °C\"]\n\nEvidence:\nThe increasing reliance on fossil fuels for global energy production has intensified greenhouse gas emissions, highlighting the need for sustainable energy alternatives. Hydrogen is considered a promising green fuel due to its high energy density and conversion efficiency. Among various production pathways, microalgae-based biohydrogen generation via biophotolysis is particularly attractive owing to its high biomass productivity, adaptability to diverse water sources, flue gas mitigation potential, and low land requirements.This study investigates the effects of key microalgal growth parameters on biohydrogen production by Chlorella sp. through biophotolysis. The impacts of nitrogen purging during the transition from aerobic to anaerobic conditions, different photoperiod regimes (continuous illumination, continuous darkness, and a light–dark cycle), glucose supplementation (5, 10, and 15 g L⁻ 1 ), and temperature (25, 30, and 35 °C) were systematically evaluated. Microalgal cell density was monitored during hydrogen production to elucidate its relationship with hydrogen yield. Initial experiments were conducted in 10 mL test tubes to identify optimal conditions, which were subsequently applied to scale-up experiments in a 1000 mL jacketed reactor. Nitrogen purging significantly enhanced hydrogen production by removing oxygen and activating hydrogenase, resulting in a peak hydrogen concentration of 11 ppm. Continuous illumination yielded higher hydrogen levels than darkness and light–dark cycling. Glucose addition substantially increased hydrogen production, with the highest yield observed at 15 g L⁻ 1 (30 ppm). An optimal temperature of 30 °C also maximized hydrogen production. Under these conditions, hydrogen production increased as cell density decreased due to metabolic shifts. Scale-up experiments achieved a 405-fold increase in hydrogen yield, demonstrating the scalability potential of the process. These findings emphasize the importance of optimizing algal growth conditions to balance microalgal growth and biohydrogen production for future industrial applications. Keywords: Biohydrogen, Microalgae, Biophotolysis, Cell growth, Nitrogen purging, Photoperiod, Carbon source", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13132944", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13132944/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-102c76212d1c31eb99a7", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe biomass of Quillaja saponaria is a well-characterized source of triterpenic saponins, a family of amphiphilic glycosides with broad industrial and biomedical relevance. Among these, the highly purified fraction QS-21, and the related fractions QHA and QHC, have strong immunostimulant properties and have become key adjuvant components of several FDA-approved human vaccines. The expanding global use of QS-21-based adjuvants has raised concerns regarding long-term availability and sustainability, as commercial production currently relies exclusively on extraction from Q. saponaria biomass sourced in Chile. This review summarizes the historical development, chemical features, and immunological relevance of QS-21, and critically evaluates existing and emerging production technologies in terms of a projected production benchmark of 50 Kg per year (corresponding to at least one billion 50 µg doses). We conclude that appropriate management of wild Quillaja forests can sustain kilogram-scale QS-21 purification, but to meet increasing demand, clonal plantation forestry will be the most practical and scalable strategy in the near future. Alternative approaches, including plant cell culture, engineered microorganisms, and chemical synthesis, remain scientifically promising but currently face technical and economic limitations for large-scale pharmaceutical production.\n\nCandidates:\nA. Among these, the highly purified fraction QS-22, and the related fractions QHA and QHC, have strong immunostimulant properties and have become key adjuvant components of several FDA-approved human vaccines.\nB. The expanding global use of QS-22-based adjuvants has raised concerns regarding long-term availability and sustainability, as commercial production currently relies exclusively on extraction from Q.\nC. The biomass of Quillaja saponaria is a well-characterized source of triterpenic saponins, a family of amphiphilic glycosides with broad industrial and biomedical relevance.\nD. The expanding global use of QS-21-based adjuvants has raised concerns regarding long-term availability and sustainability, as commercial production currently relies exclusively on extraction from Q.\nE. Among these, the highly purified fraction QS-21, and the related fractions QHA and QHC, have strong immunostimulant properties and have become key adjuvant components of several FDA-approved human vaccines.\nF. The biomass of Quillaja saponaria is not a well-characterized source of triterpenic saponins, a family of amphiphilic glycosides with broad industrial and biomedical relevance.\nG. This review summarizes the historical development, chemical features, and immunological relevance of QS-22, and critically evaluates existing and emerging production technologies in terms of a projected production benchmark of 50 Kg per year (corresponding to at least one billion 50 µg doses).\nH. This review summarizes the historical development, chemical features, and immunological relevance of QS-21, and critically evaluates existing and emerging production technologies in terms of a projected production benchmark of 50 Kg per year (corresponding to at least one billion 50 µg doses).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13133571", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13133571/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8bdba68ad265270eed26", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe systematic exploration of novel bioactive compounds with superior functional properties is critical for driving innovations in agriculture, healthcare, and related fields, thereby becoming essential for advancing sustainable biotechnological solutions. Nonprotein amino acids (NPAAs), functional amino acids not incorporated into proteins, exhibit unique physiological activities and provide distinctive advantages in nutritional enhancement, functional product formulation, and food/feed processing. These attributes challenge the conventional perception of proteins as mere nutritional carriers, positioning NPAAs as promising bioproducts for biosynthesis and functional applications in agriculture, food, and medicine. This review summarizes the classification of the available NPAAs based on their synthetic substrates for the first time and then outlines their diverse functional roles. A comprehensive analysis of recent advances in biosynthetic pathways, engineering strategies, and production level demonstrates their primary research progress in the laboratory phase. The further sustainable biomanufacturing of NPAAs is hampered by several challenges, including poorly elucidated biosynthetic mechanisms, limited robustness and low productivity of microbial strains, and difficulties in scaling up production for industrial applications. Addressing these bottlenecks will require innovative strategies and technologies to facilitate the translation of NPAA production from bench to industry. This review offers valuable insights into the potential of NPAAs in the development of next-generation bioproducts of nutrition, immune regulation, antioxidant defense, and intestinal homeostasis maintenance, suggesting a promising direction for microbial production of high-performance bioactive molecules in agricultural synthetic biomanufacturing.\n\nCandidates:\nA. The evidence does not state that these attributes challenge the conventional perception of proteins as mere nutritional carriers, positioning NPAAs as promising bioproducts for biosynthesis and functional applications in agriculture, food, and medicine.\nB. Nonprotein amino acids (NPAAs), functional amino acids not incorporated into proteins, exhibit unique physiological activities and provide distinctive advantages in nutritional enhancement, functional product formulation, and food/feed processing.\nC. The systematic exploration of novel bioactive compounds with superior functional properties is not critical for driving innovations in agriculture, healthcare, and related fields, thereby becoming essential for advancing sustainable biotechnological solutions.\nD. The evidence does not state that this review summarizes the classification of the available NPAAs based on their synthetic substrates for the first time and then outlines their diverse functional roles.\nE. These attributes challenge the conventional perception of proteins as mere nutritional carriers, positioning NPAAs as promising bioproducts for biosynthesis and functional applications in agriculture, food, and medicine.\nF. This review summarizes the classification of the available NPAAs based on their synthetic substrates for the first time and then outlines their diverse functional roles.\nG. The evidence does not state that nonprotein amino acids (NPAAs), functional amino acids not incorporated into proteins, exhibit unique physiological activities and provide distinctive advantages in nutritional enhancement, functional product formulation, and food/feed processing.\nH. The systematic exploration of novel bioactive compounds with superior functional properties is critical for driving innovations in agriculture, healthcare, and related fields, thereby becoming essential for advancing sustainable biotechnological solutions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13134175", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13134175/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a364e8712785b2eb8a76", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPre-use post-sterilization integrity testing (PUPSIT) has emerged as a critical standard in biopharmaceutical manufacturing, driven by the revised EU GMP Annex 1 and global regulatory harmonization. PUPSIT aims to verify the integrity of sterilized filters before use, reducing the risks of defect masking and safeguarding drugs against contamination. However, implementing PUPSIT introduces significant technical and operational challenges, including more complex wetting and venting procedures that may bring increased risk of contamination and susceptibility to human error. This review outlines regulatory requirements and risk-based rationales for the PUPSIT evaluation process and discusses the integration of PUPSIT within a broader contamination control strategy. Design strategies for single-use systems emphasize simplicity, error-proofing, and effective wetting and flushing methods. A comparative analysis of single-use PUPSIT systems including manual, shadowboard-assisted, and automated PUPSIT systems illustrates how process flexibility, consistency, and safety can be achieved. Ultimately, successful PUPSIT implementation requires a balance of compliance, operational efficiency, and environmental stewardship, supported by documented risk assessments and validated process controls.\n\nCandidates:\nA. Pre-use post-sterilization integrity testing (PUPSIT) has emerged as a critical standard in biopharmaceutical manufacturing, driven by the revised EU GMP Annex 1 and global regulatory harmonization.\nB. The evidence does not state that this review outlines regulatory requirements and risk-based rationales for the PUPSIT evaluation process and discusses the integration of PUPSIT within a broader contamination control strategy.\nC. PUPSIT aims to verify the integrity of sterilized filters before use, reducing the risks of defect masking and safeguarding drugs against contamination.\nD. However, implementing PUPSIT introduces significant technical and operational challenges, including more complex wetting and venting procedures that may bring increased risk of contamination and susceptibility to human error.\nE. The evidence does not state that however, implementing PUPSIT introduces significant technical and operational challenges, including more complex wetting and venting procedures that may bring increased risk of contamination and susceptibility to human error.\nF. Pre-use post-sterilization integrity testing (PUPSIT) has emerged as a critical standard in biopharmaceutical manufacturing, driven by the revised EU GMP Annex 2 and global regulatory harmonization.\nG. The evidence does not state that pUPSIT aims to verify the integrity of sterilized filters before use, reducing the risks of defect masking and safeguarding drugs against contamination.\nH. This review outlines regulatory requirements and risk-based rationales for the PUPSIT evaluation process and discusses the integration of PUPSIT within a broader contamination control strategy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13134992", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13134992/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e35fed94d993bf0b41f7", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe plastic pollution crisis urges innovative recycling solutions. Promising approaches especially for polyester-containing wastes include enzymatic hydrolysis and microbial upcycling. For efficient enzymatic hydrolysis of polyesters, elevated temperatures (70–80 °C) are required, necessitating thermophilic microbial chassis for consolidated bioprocessing (CBP). In this study, we engineered Geobacillus thermoleovorans through adaptive laboratory evolution (ALE) for robust growth on adipic acid (AA) and 1,4-butanediol (BDO), two relevant monomers for example derived from poly(butylene adipate- co -terephthalate) (PBAT), enabling growth rates of up to 0.10 h −1 on AA and 0.13 h −1 on BDO. Based on a high-quality annotated genome sequence of the wild type, genomic mutations and gene expression levels were characterized in mutants grown on the respective substrates compared to glucose. For BDO, an alcohol dehydrogenase (Gth_001044) and an aldehyde dehydrogenase (Gth_001082) were identified to be likely responsible for its oxidative degradation. AA uptake appears to be mediated by a dicarboxylate transporter (Gth_003270), followed by CoA activation and β-oxidation involving a CoA transferase (Gth_003192) and several upregulated CoA-family dehydrogenases. To demonstrate applicability of these strains in plastic upcycling, they were co-cultivated with PBAT as the sole carbon source in combination with the cutinase HiC for PBAT hydrolysis. This resulted in growth on the released AA and BDO. Given the potential to purify the remaining terephthalate (TA), this approach highlights the feasibility of selective monomer valorization in bioprocesses. Additional ALE enabled co-utilization of AA and BDO by a single strain and improved AA consumption at lower concentrations, underscoring the strains’ adaptability and high potential for plastic upcycling applications. • G. thermoleovorans evolved for robust growth on adipate and 1,4-butanediol at 60 °C. • Genome and transcriptome analyses revealed underlying pathways and enzymes involved. • Co-cultivation of the evolved strains on PBAT with HiC as the sole carbon source. The online version contains supplementary material available at 10.1007/s00253-026-13836-8.\n\nCandidates:\nA. For efficient enzymatic hydrolysis of polyesters, elevated temperatures (71–80 °C) are required, necessitating thermophilic microbial chassis for consolidated bioprocessing (CBP).\nB. The evidence does not state that the plastic pollution crisis urges innovative recycling solutions.\nC. The plastic pollution crisis urges innovative recycling solutions.\nD. Promising approaches especially for polyester-containing wastes include enzymatic hydrolysis and microbial upcycling.\nE. In this study, we engineered Geobacillus thermoleovorans through adaptive laboratory evolution (ALE) for robust growth on adipic acid (AA) and 2,4-butanediol (BDO), two relevant monomers for example derived from poly(butylene adipate- co -terephthalate) (PBAT), enabling growth rates of up to 0.10 h −1 on AA and 0.13 h −1 on BDO.\nF. The evidence does not state that promising approaches especially for polyester-containing wastes include enzymatic hydrolysis and microbial upcycling.\nG. For efficient enzymatic hydrolysis of polyesters, elevated temperatures (70–80 °C) are required, necessitating thermophilic microbial chassis for consolidated bioprocessing (CBP).\nH. In this study, we engineered Geobacillus thermoleovorans through adaptive laboratory evolution (ALE) for robust growth on adipic acid (AA) and 1,4-butanediol (BDO), two relevant monomers for example derived from poly(butylene adipate- co -terephthalate) (PBAT), enabling growth rates of up to 0.10 h −1 on AA and 0.13 h −1 on BDO.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13135583", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13135583/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7965f8a5fc8fd323afa4", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"26 °C\", \"25 °C\"]\n\nEvidence:\nThe increasing production of biodiesel has led to a surplus of glycerol, a polluting by-product in need of valorization. In this study, we demonstrate that Citrobacter telavivensis T1.2D-1, an extremophile bacterium isolated from the Iberian Pyrite Belt, effectively converts glycerol into valuable compounds via dark anaerobic fermentation. Genomic and bioinformatic analyses confirmed the presence of the dha and pdu operons, responsible for 1,3-propanediol (1,3-PDO) synthesis, and the hyc operon and fdhF gene involved in hydrogen (H 2 ) production. Batch fermentations revealed that optimal yields of both H 2 (0.94 mol . mol-glycerol -1 ) and 1,3-PDO (0.66 mol‧mol-glycerol -1 ) were achieved at 25 °C using 2 g L -1 of supplied glycerol. Optimum yield of ethanol (1 mol‧mol-glycerol -1 ) was achieved using 12.5 g L -1 of supplied glycerol. Interestingly, 1,3-PDO and H 2 production inversely correlated with ethanol formation, suggesting metabolic competition. Antibiotic sensitivity profiling revealed susceptibility to multiple antibiotics, supporting future genetic engineering efforts. We suggest opperating with reactors at low concentrations to produce 1,3-PDO and H 2 with high yields, and at medium concentrations to generate ethanol. Our findings support C. telavivensis T1.2D-1 as a promising venue for the sustainable biotechnological production of biohydrogen and bio-based 1,3-PDO from glycerol, offering a dual solution to both energy demands and industrial waste management.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13136257", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13136257/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9c56c504dd86935fcfe6", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDigital twins of mammalian cell cultures hold great potential for predictive bioprocess modeling, yet their development is challenged by the nonlinear dynamics and metabolic complexity of these systems. We present a hybrid computational framework that integrates mechanistic and data-driven modeling to construct predictive digital twins for Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies. The framework couples ordinary differential equation (ODE) models with constraint-based metabolic modeling and machine learning components trained on Bayesian-estimated metabolic rates. Applied to 23 CHO fed-batch cultures, viable cell density, product titer, and key metabolite concentrations are accurately predicted under varying feeding and media conditions within a unified simulation engine, where empirical variability is incorporated through multivariate statistical constraints derived from experimental data. Cross-validation analyses demonstrated strong generalization across process variations, highlighting the framework’s capacity to capture both biochemical constraints and adaptive cellular behavior. This hybrid modeling approach provides a mechanistically interpretable yet data-adaptive foundation for constructing bioprocess digital twins. By bridging statistical, mechanistic, and machine learning methodologies, it advances the computational representation of CHO cell culture systems and offers a generalizable strategy for predictive modeling in complex biological production processes.\n\nCandidates:\nA. We present a hybrid computational framework that integrates mechanistic and data-driven modeling to construct predictive digital twins for Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies.\nB. Digital twins of mammalian cell cultures hold great potential for predictive bioprocess modeling, yet their development is not challenged by the nonlinear dynamics and metabolic complexity of these systems.\nC. Digital twins of mammalian cell cultures hold great potential for predictive bioprocess modeling, yet their development is challenged by the nonlinear dynamics and metabolic complexity of these systems.\nD. The evidence does not state that the framework couples ordinary differential equation (ODE) models with constraint-based metabolic modeling and machine learning components trained on Bayesian-estimated metabolic rates.\nE. The evidence does not state that we present a hybrid computational framework that integrates mechanistic and data-driven modeling to construct predictive digital twins for Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies.\nF. Applied to 24 CHO fed-batch cultures, viable cell density, product titer, and key metabolite concentrations are accurately predicted under varying feeding and media conditions within a unified simulation engine, where empirical variability is incorporated through multivariate statistical constraints derived from experimental data.\nG. The framework couples ordinary differential equation (ODE) models with constraint-based metabolic modeling and machine learning components trained on Bayesian-estimated metabolic rates.\nH. Applied to 23 CHO fed-batch cultures, viable cell density, product titer, and key metabolite concentrations are accurately predicted under varying feeding and media conditions within a unified simulation engine, where empirical variability is incorporated through multivariate statistical constraints derived from experimental data.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13136614", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13136614/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a5789165c8dcdfe87fa0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIsobutanol-producing S. cerevisiae strain for industrial application. Isobutanol-tolerant strain developed from conventional mutagenesis. CRISPR-Cas9-mediated BAT1 knockout to enhance isobutanol production. CRISPR-Cas9 avoids the presence of plasmids and antibiotic markers. Strain highly increased isobutanol yield, potential for bio-alcohol production. Keywords: Saccharomyces cerevisiae , Isobutanol, Isobutanol toxicity tolerant, Mitochondrial Branched-chain amino acid aminotransferase Bat1, CRISPR/Cas9\n\nCandidates:\nA. The evidence does not state that cerevisiae strain for industrial application.\nB. The evidence does not state that isobutanol-tolerant strain developed from conventional mutagenesis.\nC. CRISPR-Cas9-mediated BAT1 knockout to enhance isobutanol production.\nD. CRISPR-Cas10-mediated BAT1 knockout to enhance isobutanol production.\nE. CRISPR-Cas10 avoids the presence of plasmids and antibiotic markers.\nF. cerevisiae strain for industrial application.\nG. Isobutanol-tolerant strain developed from conventional mutagenesis.\nH. CRISPR-Cas9 avoids the presence of plasmids and antibiotic markers.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13137203", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13137203/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-19d947fec6f6079fc166", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nThe recombinant production of the protein-glutaminase (PG) from Bacteroides helcogenes (PGB) was investigated in the three Bacillus subtilis strains: 168, RIK1285 and the isolated strain 007. B. subtilis 007 produced the highest PG activity (7.6 ± 0.7 µkat L Culture supernatant −1 ) in shake flask cultivations and, thus, was used for further investigations. As a wild-type strain, B. subtilis 007 exhibited high extracellular proteolytic activity and formed large amounts of foam in a bioreactor cultivation. While the proteolytic activity would be favorable for the extracellular cleavage of PGB’s propeptide, foaming is undesirable and should be prevented. Therefore, the influence of the two foam-demolishing substances anti-foaming agent 204 and rapeseed oil on the PGB production was investigated in bioreactor cultivations. A PGB activity of 11.8 ± 1.1 µkat L Culture supernatant −1 was obtained using anti-foaming agent 204, while rapeseed oil led to a lower maximal PGB activity of 7.6 ± 0.2 µkat L Culture supernatant −1 . PGB was partially purified from the culture supernatant by fractionated ammonium sulphate precipitation followed by hydrophobic interaction chromatography. A final yield of 14.8% was obtained, while the specific PGB activity increased from 6.7 to 28.6 nkat mg −1 . Alternatively, PGB was purified from culture supernatant by cross-flow filtration and subsequent heating step (1 h, 60 °C) to inactivate the extracellular peptidases. Thereby, the total proteolytic activity was decreased to below 4% while the specific PGB activity increased to 32.1 ± 2.0 nkat mg −1 . The PGB preparation obtained was applied in gluten deamidation experiments which showed that PGB deamidated a 1% (w/v) gluten suspension by 95 ± 2%. The online version contains supplementary material available at 10.1007/s12010-026-05637-6.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13139226", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13139226/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8e42b0eb851e1c907c28", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nVascular endothelial growth factor (VEGF) is widely used in regenerative medicine and therapeutic research. However, the purification of recombinant VEGF largely relies on affinity chromatography, which requires expensive chromatographic columns, specialized equipment, and multistep processing. These column-based workflows increase operational complexity and cost, particularly for large-scale production. Therefore, the development of an alternative purification strategy to conventional chromatography-based purification for VEGF is needed. In this study, we developed a chromatography-free VEGF purification strategy using an anti-VEGF-scFv–calsequestrin (CSQ) fusion protein that enables calcium-dependent affinity precipitation. The fusion protein retained strong binding affinity for VEGF ( K d = 1.1 nM) while exhibiting rapid and reversible Ca 2 ⁺-dependent polymerization. Upon CaCl₂ addition, the anti-VEGF-scFv-CSQ–VEGF complex rapidly formed aggregates, enabling efficient separation of VEGF from impurities. Using this strategy, VEGF was purified within 30 min with a purity of 94% and a yield of 93%. SEC-HPLC analysis confirmed a purity of 94.3%, and host cell protein contamination was reduced from 1.44 × 10 4 ppm to 774 ppm. The fusion protein also maintained stable purification performance over five repeated cycles, with VEGF recovery consistently maintained above 85%. These findings demonstrate that the scFv-CSQ fusion protein enables rapid separation of VEGF through calcium-dependent polymerization. This column-free mechanism reduces operational cost and technical complexity, highlighting its potential as an alternative to conventional chromatography-based purification. The online version contains supplementary material available at 10.1186/s40643-026-01063-y.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13139529", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13139529/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e469ae3b5396d0e41e41", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe preparation of perillartine, a potent marketed sweetener, and N ‐tert‐butyl‐α‐phenylnitrone (PBN), a most commonly used free‐radical spin‐trap used in personal care formulation, endowed with antioxidant, neuroprotective, and anti‐aging properties, was studied by vibrating and planetary ball‐milling. The reactivity in polymeric jars, such as PTFE and in‐house made polyoxymethylene (POM), was evaluated and compared to the most commonly used stainless steel and zirconium oxide milling media. This investigation revealed an insightful correlation while also accounting for the energy involved in each transformation. Assessment of the processes by green chemistry metrics and tools ( e.g. , Chem21, DOZN 3.0 and EcoScale) showed that mechanochemical approaches offer a Safe and Sustainable by Design (SSbD) approach also complying with the IUPAC guiding principles of a responsible chemistry. The reduced solvent consumption, the simplified purification procedures, and lower associated environmental and safety hazards are strongly aligned with the green chemistry principles. Keywords: active ingredients, biomass transformation, green chemistry metrics, mechanochemistry, polymeric jars\n\nCandidates:\nA. The evidence does not state that this investigation revealed an insightful correlation while also accounting for the energy involved in each transformation.\nB. The preparation of perillartine, a potent marketed sweetener, and N ‐tert‐butyl‐α‐phenylnitrone (PBN), a most commonly used free‐radical spin‐trap used in personal care formulation, endowed with antioxidant, neuroprotective, and anti‐aging properties, was not studied by vibrating and planetary ball‐milling.\nC. This investigation revealed an insightful correlation while also accounting for the energy involved in each transformation.\nD. The preparation of perillartine, a potent marketed sweetener, and N ‐tert‐butyl‐α‐phenylnitrone (PBN), a most commonly used free‐radical spin‐trap used in personal care formulation, endowed with antioxidant, neuroprotective, and anti‐aging properties, was studied by vibrating and planetary ball‐milling.\nE. The evidence does not state that assessment of the processes by green chemistry metrics and tools ( e.g.\nF. Assessment of the processes by green chemistry metrics and tools ( e.g.\nG. The reactivity in polymeric jars, such as PTFE and in‐house made polyoxymethylene (POM), was evaluated and compared to the most commonly used stainless steel and zirconium oxide milling media.\nH. The reactivity in polymeric jars, such as PTFE and in‐house made polyoxymethylene (POM), was not evaluated and compared to the most commonly used stainless steel and zirconium oxide milling media.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13139749", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13139749/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b40d28239f1d7299fbfb", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAfrica is home to 15% of the global population and bears nearly a quarter of the world′s disease burden. However, it faces significant challenges in biomedical research and healthcare due to limited access to essential reagents. This review examines the critical need for localized reagent production to strengthen biomedical research and healthcare across the continent. The reagent market in Africa is characterized by varying levels of development, and the current production capacity falls short of meeting the biomedical research and healthcare demands. The reliance on imported reagents results in high costs, lengthy procurement processes, and vulnerability to supply chain disruptions. Establishing reagent production facilities across the continent can enhance self‐sufficiency, reduce costs, and improve biomedical research tailored to the unique health challenges faced by African populations. Such efforts can be supported by strategic public–private partnerships and international collaborations, leveraging the successes of the existing pharmaceutical investments across the continent and local capacity‐building initiatives. Regional collaboration, market development, technical training, infrastructure, regulatory frameworks, and sustainable funding are essential to ensure long‐term success. Developing skilled human capital and creating a unified market through initiatives like the African Continental Free Trade Area can attract investment and ensure economic sustainability of local reagent production. Together, these measures can significantly improve biomedical research output and healthcare response capabilities, preparing the continent for future health emergencies. Keywords: biomedical research, funding, local manufacturing, reagent production, sustainability\n\nCandidates:\nA. This review examines the critical need for localized reagent production to strengthen biomedical research and healthcare across the continent.\nB. The reagent market in Africa is characterized by varying levels of development, and the current production capacity falls short of meeting the biomedical research and healthcare demands.\nC. The reagent market in Africa is not characterized by varying levels of development, and the current production capacity falls short of meeting the biomedical research and healthcare demands.\nD. However, it faces significant challenges in biomedical research and healthcare due to limited access to essential reagents.\nE. Africa is home to 15% of the global population and bears nearly a quarter of the world′s disease burden.\nF. The evidence does not state that this review examines the critical need for localized reagent production to strengthen biomedical research and healthcare across the continent.\nG. The evidence does not state that however, it faces significant challenges in biomedical research and healthcare due to limited access to essential reagents.\nH. Africa is home to 16% of the global population and bears nearly a quarter of the world′s disease burden.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13140876", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13140876/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b73230d1f51139e84636", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"16015 L\", \"49.28 µg/mL\", \"96.9%\", \"95.9%\", \"16016 L\", \"48.28 µg/mL\", \"1000 µg/mL\", \"1001 µg/mL\"]\n\nEvidence:\nL-ASNase has attracted attention in many biomedical and food safety applications. Therefore, this study was designed to identify a novel and promising candidate for the sustainable biosynthesis of extracellular L-ASNase from P. ostreatus AUMC 16015 grown on various agricultural substrates under solid-state fermentation (SSF). Also, the enzyme’s wide-ranging bioactivities were examined, involving its antioxidant, anti-inflammatory, and antitumor properties, while evaluating its potential applications in food processing. Optimal P. ostreatus AUMC 16015 L-ASNase production was 56.47 U/mL, which was attained under SSF conditions where the enzyme yield increased by 2.46-fold compared to pre-optimization conditions. Enzyme high purity was validated by a single distinct band at approximately 48 kDa on both SDS-PAGE and native PAGE analyses. The enzyme demonstrated high substrate specificity ( K m = 7.7 mM; V max = 167.78 U/mL). Functionally, it exhibited strong antioxidant activity (2,2-diphenyl-1-picrylhydrazyl) (DPPH) IC 50 = 48.28 µg/mL) and a robust anti-hemolytic effect (95.9% at 1000 µg/mL). L-ASNase exhibited its most potent inhibitory effect against Caco-2 cells at an IC 50 of 5.49 ± 0.03 µg/mL, followed by MCF-7, which showed a slightly higher IC 50 of 5.86 ± 0.08 µg/mL. Furthermore, L-ASNase significantly mitigated potato chips acrylamide formation, achieving a 9.6-fold decrease after 120 min of treatment. Additionally, Gas chromatography-mass spectrometry ( GC-MS) showed that the potato’s chemical profile was significantly changed by L-ASNase treatment, with the introduction of numerous bioactive substances and the elimination of some potentially dangerous components. The biochemical activity of the purified L-ASNase suggested potential biomedical and food applications. This study is a trial for cost-effective enzyme production and supports a circular bioeconomy by converting waste into useful bioproducts. Future work should focus on scaling up production and testing its effects in living organisms to unlock this enzyme’s full commercial and medical potential. Keywords: L-ASNase, P. ostreatus AUMC 16015, SSF, Biomedical and food applications", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13141449", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13141449/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1716352ea633c702fd64", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nKeywords: Greenhouse, Hydroponic system, Plant molecular farming, Nicotiana benthamiana , Sustainable, Controlled-environment agriculture (CEA)\n\nCandidates:\nA. The evidence does not state that keywords: Greenhouse, Hydroponic system, Plant molecular farming, Nicotiana benthamiana , Sustainable, Controlled-environment agriculture (CEA)\nB. Keywords: Greenhouse, Hydroponic system, Plant molecular farming, Nicotiana benthamiana , Sustainable, Controlled-environment agriculture (CEA)", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13141774", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13141774/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-77b8ee149fcd42bb4aab", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBarcoded rabies viral tracing enables high-throughput mapping of brain-wide inputs to individual neurons while allowing integration with transcriptomic profiling. Here, we present a protocol to produce and apply barcoded rabies virus in mice for single-neuron input mapping. We describe steps to generate barcode plasmid libraries, produce barcoded rabies virus, and isolate target brain regions. We then detail procedures for preparing single-cell suspensions or bulk RNA, building barcode amplicon libraries from input regions, and performing initial analysis of the sequencing data. For complete details on the use and execution of this protocol, please refer to Tan et al. 1 , 2 Subject areas: Bioinformatics, Cell Biology, Cell culture, Neuroscience, Sequence analysis, Sequencing, Single Cell\n\nCandidates:\nA. Barcoded rabies viral tracing enables high-throughput mapping of brain-wide inputs to individual neurons while allowing integration with transcriptomic profiling.\nB. The evidence does not state that we describe steps to generate barcode plasmid libraries, produce barcoded rabies virus, and isolate target brain regions.\nC. The evidence does not state that barcoded rabies viral tracing enables high-throughput mapping of brain-wide inputs to individual neurons while allowing integration with transcriptomic profiling.\nD. The evidence does not state that we then detail procedures for preparing single-cell suspensions or bulk RNA, building barcode amplicon libraries from input regions, and performing initial analysis of the sequencing data.\nE. We describe steps to generate barcode plasmid libraries, produce barcoded rabies virus, and isolate target brain regions.\nF. Here, we present a protocol to produce and apply barcoded rabies virus in mice for single-neuron input mapping.\nG. The evidence does not state that here, we present a protocol to produce and apply barcoded rabies virus in mice for single-neuron input mapping.\nH. We then detail procedures for preparing single-cell suspensions or bulk RNA, building barcode amplicon libraries from input regions, and performing initial analysis of the sequencing data.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13142104", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13142104/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bc1b0627466bd21b980d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAntimicrobial food pads play a key role in the food packaging industry by improving food safety and quality. To control the growth of bacterial pathogens in fish fillets, this study aims to develop a bioactive system using absorbent food pads composed of bacterial cellulose (BC) infused with tangerine essential oil (TEO) as an antibacterial agent. The effects of active BC-pads, gamma irradiation, and their combination on artificially inoculated bacteria (previously isolated) were studied for 6 days at 4 °C using fish fillet samples as a model food system. The results revealed that the initial population counts of Klebsiella oxytoca, Serratia ficaria, Enterobacter cloacae, and Kocuria rosea, were 5.47, 5.63, 5.14, and 5.25 log CFU/g, respectively. These counts increased during the storage period, reaching 8.80 log CFU/g as a mean for all tested strains. After six days of storage, BC, BC/TEO, BC + 1.0 kGy, and BC/TEO + 1.0 kGy pads reduced the initial load of the four bacteria in the fish fillet samples by [1.59, 1.68, 1.92, 2.35], [3.21, 3.12, 3.36, 3.72], [3.53, 3.26, 3.26, 3.37] and [4.97, 5.02, 4.95, 4.83] log CFU/g, respectively. The combination of BC and TEO with low-dose gamma radiation (1.0 kGy) enhances the decontamination effectiveness of fish fillets through synergistic effects. FTIR analysis revealed a slight shift in the absorbance peaks of some functional group interactions and the formation of new bonds between BC and TEO in the presence of gamma irradiation. The study has successfully developed sustainable, eco-friendly, functional bioactive food pads that reduce microbial growth and prevent spoilage of fish fillets during storage. The online version contains supplementary material available at 10.1186/s40643-026-01061-0. Keywords: Food pads, Bacterial cellulose, Hurdle technology, Antimicrobial, Tangerine essential oil, Gamma irradiation, Klebsiella oxytoca , Serratia ficaria , Enterobacter cloacae , Kocuria rosea\n\nCandidates:\nA. The results revealed that the initial population counts of Klebsiella oxytoca, Serratia ficaria, Enterobacter cloacae, and Kocuria rosea, were 6.47, 5.63, 5.14, and 5.25 log CFU/g, respectively.\nB. To control the growth of bacterial pathogens in fish fillets, this study aims to develop a bioactive system using absorbent food pads composed of bacterial cellulose (BC) infused with tangerine essential oil (TEO) as an antibacterial agent.\nC. The evidence does not state that antimicrobial food pads play a key role in the food packaging industry by improving food safety and quality.\nD. The effects of active BC-pads, gamma irradiation, and their combination on artificially inoculated bacteria (previously isolated) were studied for 6 days at 4 °C using fish fillet samples as a model food system.\nE. The evidence does not state that to control the growth of bacterial pathogens in fish fillets, this study aims to develop a bioactive system using absorbent food pads composed of bacterial cellulose (BC) infused with tangerine essential oil (TEO) as an antibacterial agent.\nF. The effects of active BC-pads, gamma irradiation, and their combination on artificially inoculated bacteria (previously isolated) were studied for 7 days at 4 °C using fish fillet samples as a model food system.\nG. The results revealed that the initial population counts of Klebsiella oxytoca, Serratia ficaria, Enterobacter cloacae, and Kocuria rosea, were 5.47, 5.63, 5.14, and 5.25 log CFU/g, respectively.\nH. Antimicrobial food pads play a key role in the food packaging industry by improving food safety and quality.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13144468", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13144468/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4552f12396899a5bba60", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\n2-phenylethanol (2-PE) is a high-value aromatic alcohol. Its bioproduction is limited by pathway bottlenecks, limited by a rate-limiting decarboxylation step, product toxicity, and volatilization. We sought to build an efficient Escherichia coli ( E. coli ) platform by optimizing the segment of the Ehrlich route that converts L-phenylalanine (L-Phe) to 2-PE, together with a food-grade soybean-oil overlay for in situ product recovery (ISPR). Whole-cell assays showed that phenylpyruvate decarboxylation was the main bottleneck when the yeast decarboxylase Aro10 was used. A phylogeny-guided screen identified Lactococcus lactis KivD as a superior substitute in E. coli . After replacing Aro10 with KivD and tuning expression, leveraging endogenous glutamate dehydrogenase (GDH) for NAD(P)H recycling enabled sufficient cofactor regeneration, yielding 49.5 mM 2-PE from 50 mM L-Phe with 99.0% conversion. At the shake-flask scale, a fed-batch L-Phe feeding strategy coupled with a 2:1 soybean-oil overlay for in situ product removal reduced toxicity and volatilization losses, producing 130 mM 2-PE (15.9 g/L). Using soybean oil as an ISPR phase effectively sequestered 2-PE from the aqueous phase, minimizing volatilization and increasing the overall recovered yield to 94.2% based on L-Phe consumption. Replacing the rate-limiting decarboxylation step with KivD, balancing redox through cofactor recycling, and coupling the pathway to a mild oil-overlay recovery increased flux and enabled high-titer 2-PE in E. coli . The workflow of bottleneck substitution plus biocompatible in situ recovery can be transferred to related aromatic alcohols and provides a foundation for de novo routes and high-cell-density fermentations that target still higher titers. The online version contains supplementary material available at 10.1186/s13036-026-00667-4.\n\nCandidates:\nA. 2-phenylethanol (2-PE) is a high-value aromatic alcohol.\nB. The evidence does not state that we sought to build an efficient Escherichia coli ( E.\nC. Its bioproduction is limited by pathway bottlenecks, limited by a rate-limiting decarboxylation step, product toxicity, and volatilization.\nD. 3-phenylethanol (2-PE) is a high-value aromatic alcohol.\nE. Its bioproduction is not limited by pathway bottlenecks, limited by a rate-limiting decarboxylation step, product toxicity, and volatilization.\nF. coli ) platform by optimizing the segment of the Ehrlich route that converts L-phenylalanine (L-Phe) to 3-PE, together with a food-grade soybean-oil overlay for in situ product recovery (ISPR).\nG. We sought to build an efficient Escherichia coli ( E.\nH. coli ) platform by optimizing the segment of the Ehrlich route that converts L-phenylalanine (L-Phe) to 2-PE, together with a food-grade soybean-oil overlay for in situ product recovery (ISPR).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13147604", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13147604/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-391cf5296def72af09c1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMoreno-Corona et al. identify compound-heterozygous WDR75 variants in a patient with hypogammaglobulinemia and autism spectrum disorder. These variants impair pre-ribosomal RNA processing and trigger nucleolar stress, suggesting a new ribosomopathy.\n\nCandidates:\nA. identify compound-heterozygous WDR76 variants in a patient with hypogammaglobulinemia and autism spectrum disorder.\nB. These variants impair pre-ribosomal RNA processing and trigger nucleolar stress, suggesting a new ribosomopathy.\nC. identify compound-heterozygous WDR75 variants in a patient with hypogammaglobulinemia and autism spectrum disorder.\nD. The evidence does not state that these variants impair pre-ribosomal RNA processing and trigger nucleolar stress, suggesting a new ribosomopathy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13148477", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13148477/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e608878589f5826a35d5", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe baculovirus expression vector system is an established platform for large-scale production of (glyco)proteins, subunit vaccines, virus-like particles, and recombinant adeno-associated virus (rAAV) vectors. We engineered a novel bacmid vector (BAC6) to improve genetic stability by deletion of the non-homologous repeat (hr) origin of DNA replication (ori) and to preserve product integrity and recovery through deletion of chitinase and cathepsin. Tn7-based transposition in E. coli and homologous recombination in insect cells were combined in BAC6 to drive expression from the odv-e56 and polyhedrin loci, respectively. Virus growth kinetics of BAC6 were similar to the parental bacmid, and genetic stability was investigated for at least eight serial passages at high multiplicity of infection. Next-generation sequencing was used to identify mutations, deletions, and defective interfering particle (DIP) formation, which became apparent only in later passages. With BAC6, the enrichment of DIPs originating from the non-hr ori was prevented. BAC6 versatility was demonstrated by high-yield (12 mg/L) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike production in suspension Sf9 insect cells. Finally, rAAV production with BAC6, simultaneously employing both transgene insertion sites, resulted in yields of 5.8e10 AAV capsids/mL and 2.3e10 genome copies/mL. BAC6 provides insertion of multiple transgenes at two different loci and is non-inferior to commercial baculovirus expression vectors with regard to genetic stability and productivity. Keywords: genome editing, bacmid, baculovirus expression vector system, illumina next generation sequencing, SARS-CoV-2, adeno-associated virus, AAV\n\nCandidates:\nA. We engineered a novel bacmid vector (BAC7) to improve genetic stability by deletion of the non-homologous repeat (hr) origin of DNA replication (ori) and to preserve product integrity and recovery through deletion of chitinase and cathepsin.\nB. The baculovirus expression vector system is an established platform for large-scale production of (glyco)proteins, subunit vaccines, virus-like particles, and recombinant adeno-associated virus (rAAV) vectors.\nC. We engineered a novel bacmid vector (BAC6) to improve genetic stability by deletion of the non-homologous repeat (hr) origin of DNA replication (ori) and to preserve product integrity and recovery through deletion of chitinase and cathepsin.\nD. coli and homologous recombination in insect cells were combined in BAC7 to drive expression from the odv-e56 and polyhedrin loci, respectively.\nE. Virus growth kinetics of BAC6 were similar to the parental bacmid, and genetic stability was investigated for at least eight serial passages at high multiplicity of infection.\nF. Virus growth kinetics of BAC7 were similar to the parental bacmid, and genetic stability was investigated for at least eight serial passages at high multiplicity of infection.\nG. coli and homologous recombination in insect cells were combined in BAC6 to drive expression from the odv-e56 and polyhedrin loci, respectively.\nH. The baculovirus expression vector system is not an established platform for large-scale production of (glyco)proteins, subunit vaccines, virus-like particles, and recombinant adeno-associated virus (rAAV) vectors.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13148896", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13148896/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-47f3caa3f7dd5a7ff76b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGenetically modified cell therapies (GMCT) hold potential for the treatment of cancer, autoimmune conditions, and rare diseases. A major determinant of GMCT success is modification efficiency, yet transducing certain cells of interest, such as primary natural killer (NK) cells, remains challenging. Major barriers to widespread adoption of GMCT are cost and difficulty of scale. Therefore, improving transduction rates with accessible, good manufacturing practices (GMP) compatible reagents may help reduce this barrier and prevent manufacturing failure. We show that using a modified viscous transduction medium (VTM) made with methyl-cellulose significantly increased the transduction efficiency of primary human NK cells without affecting viability, expansion, or function. VTM outperformed commercially available transduction enhancers. Transduction with VTM significantly improved the yield of anti-CD22 chimeric antigen receptor (CAR)-NK cells. Moreover, VTM was similarly effective at improving the transduction of primary T cells and hematopoietic stem cells. Overall, VTM may provide a simple, cost-effective, and GMP-compliant solution to increase the yield of genetically modified immune cells for immunotherapy. This may improve the efficacy of immunotherapies and reduce the overall costs of production.\n\nCandidates:\nA. The evidence does not state that genetically modified cell therapies (GMCT) hold potential for the treatment of cancer, autoimmune conditions, and rare diseases.\nB. Major barriers to widespread adoption of GMCT are not cost and difficulty of scale.\nC. Therefore, improving transduction rates with accessible, good manufacturing practices (GMP) compatible reagents may help reduce this barrier and prevent manufacturing failure.\nD. A major determinant of GMCT success is modification efficiency, yet transducing certain cells of interest, such as primary natural killer (NK) cells, remains challenging.\nE. A major determinant of GMCT success is not modification efficiency, yet transducing certain cells of interest, such as primary natural killer (NK) cells, remains challenging.\nF. Major barriers to widespread adoption of GMCT are cost and difficulty of scale.\nG. Genetically modified cell therapies (GMCT) hold potential for the treatment of cancer, autoimmune conditions, and rare diseases.\nH. The evidence does not state that therefore, improving transduction rates with accessible, good manufacturing practices (GMP) compatible reagents may help reduce this barrier and prevent manufacturing failure.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13148914", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13148914/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-912c65538329075a0f06", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"31%\", \"30%\", \"20%\", \"21%\"]\n\nEvidence:\nChimeric antigen receptor (CAR) T cell therapies represent a significant advancement for treating hematological malignancies, particularly in relapsed/refractory cases. Despite their clinical success, the high cost of CAR T cell therapies remains a major barrier to broader implementation. A significant proportion of these costs stems from the dependency on viral vectors and the limited understanding of transduction mechanisms. This work evaluates the impact of physical and chemical parameters during transduction using a spinoculation process. Physical parameters, such as spinoculation duration and speed, were identified as key drivers of transduction efficiency, contributing to a 20%–30% increase in transduction. Similarly, the addition of LentiBOOST and polybrene enhanced transduction efficiency by approximately 1–2-fold compared with control conditions without these supplements. Given that both physical and chemical parameters influence transduction efficiency, a quality-by-design approach was used to systematically investigate their potential synergistic or antagonistic interactions. This systematic approach highlighted the cytotoxic impact of polybrene and demonstrated that LentiBOOST is critical to drive transduction, particularly in CD4 subsets. The optimized process led to a 2–3-fold improvement in transduction without compromising CAR T cell growth or functionality and was shown to be compatible with serum- and xeno-free medium, supporting its translational potential.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13148932", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13148932/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-44c98c89e68df26b9166", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"801 nm\", \"31%\", \"216%\", \"30%\", \"800 nm\", \"215%\"]\n\nEvidence:\nMembrane fouling remains a major challenge in water treatment, biomedical, and pharmaceutical fields, yet understanding its mechanisms at the membrane interface remains difficult. Herein, we report a 3D impedimetric microfluidic membrane-mimic (IM3) cassette that integrates a porous membrane between microfluidic channels for real-time fouling investigation via electrochemical impedance spectroscopy (EIS). System validation using fluorescein and KCl electrolytes demonstrated reproducible charge-transfer resistance ( R ct ) values and confirmed cassette robustness. Colloidal fouling studies using 800 nm polystyrene latex (PS) beads demonstrated a clear concentration-dependent behavior. High particle loading (10 5 particles/mL) caused rapid, severe impedance increases, indicating extensive pore blockage and cake layer formation. Low loading (10 1 particles/mL) showed minimal changes, suggesting negligible fouling, as confirmed by scanning electron microscopy. The distribution of relaxation times (DRT) analysis revealed a single dominant relaxation peak at 10 –2 s that grew stronger as fouling severity increased, confirming that pore blockage raised interfacial resistance through a unified charge-transfer mechanism rather than caused diffusion-limited processes. A quantitative fouling model using EIS-measured R ct changes captured temporal and concentration dependencies through an exponential growth framework. Maximum fouling extent ( F max ) increased significantly with particle concentration (30% at 10 1 to 215% at 10 5 particles/mL), demonstrating concentration-dependent pore blockage. The fouling rate constant ( k ) showed weak concentration dependence and plateaued at high loadings, indicating the limitation by available deposition sites rather than particle transport. These findings show that for PS beads, fouling is mainly governed by the extent of surface deposition (driven by concentration) rather than by the particle arrival rate (controlled by kinetics). The developed modular IM3 cassette enables customization of different membranes, channel geometries, and flow configurations. These results establish a quantitative framework linking EIS data to physically meaningful fouling phenomena, offering a versatile platform for the mechanistic investigation of dynamic membrane fouling behavior under varying particulate conditions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13154124", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13154124/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-30c4a97f272aab00a7d2", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe filamentous fungus Aspergillus niger is a well-established cell factory in biotechnology. Its productivity depends on macromorphological development which remains difficult to control, partly because the relationship between seed culture and reactor-specific shear force conditions has not been systematically investigated. This study examined how high or low shear forces affect pellet development at both micro- and macromorphological levels in stirred-tank reactors (STR, high shear regime) and rocking-motion bioreactors (RMB, low shear regime). A. niger seed cultures with initially either large or small pellets were used to inoculate batch STR or RMB. Comparable cultivation conditions were applied so that fermentations differed mainly in shear force regime. Growth characteristics and pellet macromorphologies were analysed using 2D and 3D image analyses, enabling us to classify pellets according to three different classes based on their inner pellet architecture. The distribution of these classes depended on both the macromorphologies of the seed culture and the reactor type. Under high shear forces in the STR, pellets underwent breakage shortly after stirrer activation, were limited in their size to an average diameter of 500–600 µm, and formed a homogeneous population. In addition, broken pellets occurred predominantly under STR conditions. In contrast, cultivations in RMB preserved the initial pellet architecture, allowed the formation of larger pellets (median diameter ~ 800 µm) and supported pellet fusion, thus resulting in a more heterogeneous macromorphological population. Notably, glucose uptake rate correlated with the surface-to-volume ratio of the pellet populations, i.e., glucose became faster consumed under STR conditions accompanied with lower biomass yields and higher protein secretion. Citric acid production, however, was detectable in both STR and RMB only when reactors were inoculated with seed cultures characterised by a loose pellet morphology. Overall, our study demonstrates how shear regime and seed culture morphology jointly shape pellet architecture, population heterogeneity and productivity in scale-up processes. Such a comprehensive understanding of morphological developments is instrumental for optimising bioprocesses and future predictive modelling approaches. • 2D/3D analysis of defined seed cultures in different shear-induced environments • High shear restricts and homogenises pellets, while low shear maintains heterogeneity • Highest citric acid and total protein levels were found in smaller, compact pellets. The online version contains supplementary material available at 10.1007/s00253-026-13822-0.\n\nCandidates:\nA. The filamentous fungus Aspergillus niger is not a well-established cell factory in biotechnology.\nB. niger seed cultures with initially either large or small pellets were used to inoculate batch STR or RMB.\nC. The evidence does not state that its productivity depends on macromorphological development which remains difficult to control, partly because the relationship between seed culture and reactor-specific shear force conditions has not been systematically investigated.\nD. Its productivity depends on macromorphological development which remains difficult to control, partly because the relationship between seed culture and reactor-specific shear force conditions has not been systematically investigated.\nE. The evidence does not state that this study examined how high or low shear forces affect pellet development at both micro- and macromorphological levels in stirred-tank reactors (STR, high shear regime) and rocking-motion bioreactors (RMB, low shear regime).\nF. The filamentous fungus Aspergillus niger is a well-established cell factory in biotechnology.\nG. This study examined how high or low shear forces affect pellet development at both micro- and macromorphological levels in stirred-tank reactors (STR, high shear regime) and rocking-motion bioreactors (RMB, low shear regime).\nH. niger seed cultures with initially either large or small pellets were not used to inoculate batch STR or RMB.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13156162", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13156162/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-648d564bf5ce7b73800d", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"61.2–97.9%\", \"62.2–97.9%\"]\n\nEvidence:\nInadequate sludge treatment poses a significant risk of environmental pollution. To reduce pollution, utilizing waste sludge as an energy source offers a sustainable solution to mitigate pollution. Since sludges contain abundant organics, they are expected to produce more valuable organics, such as volatile fatty acids (VFAs). Although sludge fermentation has been widely studied, direct yields of VFA production from different sludge types are still limited, particularly for sludge materials originating from Finnish wastewater treatment plants. In this study, laboratory-scale bioreactors were built to examine VFA production from digester feed sludge, digested sludge, and ammonia removal sludge, which were collected from local wastewater treatment plants. Among the tested substrates, digested feed sludge resulted in the highest VFA yield, reaching 171.6 ± 6.3 mg/g volatile solids after 11 days of incubation, which indicated its superior potential for VFA generation. In contrast, bioreactors fed with digested sludge or ammonia removal sludge showed no clear increasing trend in VFA production. The addition of sawdust led to lower overall VFA yields, approximately half of those obtained with digested feed sludge alone, and increasing the sludge proportion did not result in further yield enhancement. Acetic acid was the dominant VFA in all bioreactors, accounting for 61.2–97.9% of total VFAs. Microbial community analysis indicated the prevalence of phyla Bacillota and Pseudomonadota, with Lactobacillus being relatively abundant in bioreactors exhibiting higher VFA production. These findings suggest that sludge type plays an important role in determining VFA production performance and may support more informed selection of sludge substrates for VFA-oriented sludge valorisation. Keywords: Volatile fatty acids, Waste sludge valorisation, Microbial community, Sludge fermentation, Bioprocess sustainability, Resource recovery", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13156358", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13156358/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f321cbb1091d95e188b3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBottom-up proteomics workflows encompass several key stages, including sample preparation, data acquisition, and data analysis. Of these, sample preparation is the initial and critical stage, as it significantly influences the depth, reproducibility, and reliability of subsequent mass spectrometry–based analyses. While several main digestion strategies exist, including in-gel, in-solution, and filter-aided methods, each presents distinct trade-offs in terms of throughput, contamination removal, and applicability to complex biological matrices. The Suspension Trapping (S-Trap) method offers a compelling alternative by efficiently capturing and digesting proteins while removing interferents like sodium dodecyl sulfate (SDS), which can compromise downstream LC–MS/MS performance. This protocol details a S-Trap workflow optimized for biofluid proteomics, specifically plasma, serum, and cerebrospinal fluid (CSF). We describe two complementary formats: a manual tube-based procedure for individual or small-batch samples and a 96-well-plate-based system enabling high-throughput processing. The protocol integrates optional high-abundance protein depletion to enhance coverage of low-abundance analytes and includes steps for reduction, alkylation, digestion, and peptide elution for low total protein content samples, such as plasma, serum, and cerebrospinal fluid. By providing a detailed protocol, this work aims to improve the consistency and accessibility of S-Trap-based sample preparation, facilitating robust and reproducible discoveries in bottom-up proteomics. Key features • Plasma/serum/cerebrospinal fluid sample preparation for bottom-up proteomics. • Lab Suspension Trapping (S-Trap)-based digestion for efficient detergent removal and high peptide recovery. • Optimized for challenging samples (e.g., CSF, plasma) with low protein concentration or high lipid content. • Includes both single-tube and high-throughput 96-well plate formats for flexible experimental design. Keywords: Mass spectrometry, LC-MS, Proteomics, Sample preparation, S-trap\n\nCandidates:\nA. Bottom-up proteomics workflows encompass several key stages, including sample preparation, data acquisition, and data analysis.\nB. The Suspension Trapping (S-Trap) method offers a compelling alternative by efficiently capturing and digesting proteins while removing interferents like sodium dodecyl sulfate (SDS), which cannot compromise downstream LC–MS/MS performance.\nC. While several main digestion strategies exist, including in-gel, in-solution, and filter-aided methods, each presents distinct trade-offs in terms of throughput, contamination removal, and applicability to complex biological matrices.\nD. Of these, sample preparation is the initial and critical stage, as it significantly influences the depth, reproducibility, and reliability of subsequent mass spectrometry–based analyses.\nE. The evidence does not state that while several main digestion strategies exist, including in-gel, in-solution, and filter-aided methods, each presents distinct trade-offs in terms of throughput, contamination removal, and applicability to complex biological matrices.\nF. Of these, sample preparation is not the initial and critical stage, as it significantly influences the depth, reproducibility, and reliability of subsequent mass spectrometry–based analyses.\nG. The evidence does not state that bottom-up proteomics workflows encompass several key stages, including sample preparation, data acquisition, and data analysis.\nH. The Suspension Trapping (S-Trap) method offers a compelling alternative by efficiently capturing and digesting proteins while removing interferents like sodium dodecyl sulfate (SDS), which can compromise downstream LC–MS/MS performance.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13156697", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13156697/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-03b871c8aefaaabd3e84", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPolysaccharides from Chroogomphus rutilus (CRPs) exhibit promising bioactivities; however, conventional alkaline extraction (CAE) often results in low yields and structural disruption. In this study, a support vector regression (SVR)–optimized ultrasonic-assisted alkaline extraction (UAAE) process was developed to maximize yield while preserving conformational integrity. Compared with the quadratic response surface methodology model, SVR showed superior predictive accuracy and robustness, with the testing R 2 increasing from 0.8751 to 0.9027 and RMSE reduced by 11.7%. SVR also exhibited narrower residual dispersion and improved stability in the high-yield region (>17%), confirming its suitability for nonlinear, multivariable bioprocess optimization. Under the optimized conditions—ultrasonic temperature 56.5 °C, ultrasonic time 35 min, soaking time 138 min, liquid–solid ratio 26 mL/g, NaOH concentration 0.55 mol/L, and ultrasonic power 325 W—the extraction yield reached 20.09 ± 0.10%, representing a 53.7% increase compared with CAE. High-performance gel permeation chromatography revealed two dominant molecular weight (Mw) fractions for UAAE-derived CRP at 7.54 × 10 5 and 1.32 × 10 4 Da, whereas CAE-derived products exhibited a trimodal distribution dominated by low-Mw species at 1.43 × 10 3 Da. Spectroscopic, microscopic, and thermal analyses demonstrated that UAAE more effectively preserved ordered helical conformations, and improved structural stability compared with CAE. CRPs obtained under optimized conditions also showed enhanced antioxidant activity, including ABTS• + and hydroxyl radical scavenging capacities. The integrating acoustic cavitation with alkaline treatment and SVR-based modeling provides an efficient, data-driven strategy for sustainable production of high-quality fungal polysaccharides with preserved bioactive conformations.\n\nCandidates:\nA. In this study, a support vector regression (SVR)–optimized ultrasonic-assisted alkaline extraction (UAAE) process was developed to maximize yield while preserving conformational integrity.\nB. The evidence does not state that polysaccharides from Chroogomphus rutilus (CRPs) exhibit promising bioactivities; however, conventional alkaline extraction (CAE) often results in low yields and structural disruption.\nC. SVR also exhibited narrower residual dispersion and improved stability in the high-yield region (>18%), confirming its suitability for nonlinear, multivariable bioprocess optimization.\nD. Compared with the quadratic response surface methodology model, SVR showed superior predictive accuracy and robustness, with the testing R 3 increasing from 0.8751 to 0.9027 and RMSE reduced by 11.7%.\nE. Compared with the quadratic response surface methodology model, SVR showed superior predictive accuracy and robustness, with the testing R 2 increasing from 0.8751 to 0.9027 and RMSE reduced by 11.7%.\nF. In this study, a support vector regression (SVR)–optimized ultrasonic-assisted alkaline extraction (UAAE) process was not developed to maximize yield while preserving conformational integrity.\nG. SVR also exhibited narrower residual dispersion and improved stability in the high-yield region (>17%), confirming its suitability for nonlinear, multivariable bioprocess optimization.\nH. Polysaccharides from Chroogomphus rutilus (CRPs) exhibit promising bioactivities; however, conventional alkaline extraction (CAE) often results in low yields and structural disruption.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13156778", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13156778/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b7df668e91d11725d9a4", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nOptimizing transient expression systems in fish embryos is crucial for rapid gene function analysis. Here, we established an efficient system in medaka ( Oryzias latipes ) embryos by evaluating nucleic acid type and injection site. Our results revealed that injecting the elongation factor 1αA ( ef1αA ) promoter‐driven plasmid into the yolk yielded the highest expression on day 1 post‐fertilization. Using this optimized system, we investigated cytosolic 5′‐nucleotidase 1a ( nt5c1a ), which is involved in the metabolism of inosine monophosphate (IMP), an umami flavor compound. In silico analysis revealed that medaka had two nt5c1a paralogs: nt5c1aa and nt5c1ab . While nt5c1ab retains conserved substrate‐recognition sequences and exhibits significant IMP degradation activity, nt5c1aa has lost these functions. Structural analysis using AlphaFold revealed that the Nt5c1aa L305P mutation causes local conformational changes near the substrate‐binding site, potentially altering substrate orientation without disrupting the overall protein fold. Our expression system demonstrated that this single L305P substitution partially restored IMP‐degrading activity in Nt5c1aa, confirming that residue 305 is a key determinant of its functional divergence. Our findings provide a robust foundation for molecular breeding to enhance umami flavor in farmed fish. Specifically, the targeted manipulation of these nt5c1a paralogs could facilitate developing breeds with maximized IMP accumulation in muscle tissues.\n\nCandidates:\nA. Optimizing transient expression systems in fish embryos is not crucial for rapid gene function analysis.\nB. Using this optimized system, we investigated cytosolic 6′‐nucleotidase 1a ( nt5c1a ), which is involved in the metabolism of inosine monophosphate (IMP), an umami flavor compound.\nC. Here, we established an efficient system in medaka ( Oryzias latipes ) embryos by evaluating nucleic acid type and injection site.\nD. Our results revealed that injecting the elongation factor 2αA ( ef1αA ) promoter‐driven plasmid into the yolk yielded the highest expression on day 1 post‐fertilization.\nE. Optimizing transient expression systems in fish embryos is crucial for rapid gene function analysis.\nF. Using this optimized system, we investigated cytosolic 5′‐nucleotidase 1a ( nt5c1a ), which is involved in the metabolism of inosine monophosphate (IMP), an umami flavor compound.\nG. The evidence does not state that here, we established an efficient system in medaka ( Oryzias latipes ) embryos by evaluating nucleic acid type and injection site.\nH. Our results revealed that injecting the elongation factor 1αA ( ef1αA ) promoter‐driven plasmid into the yolk yielded the highest expression on day 1 post‐fertilization.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13156797", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13156797/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-af203a2ba652de0aee80", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis data article presents an open ultrasonic pulse – echo dataset acquired from NMC – 622 cathode slurries monitored under static conditions to characterise their post – mixing structural recovery behaviour. Slurries were prepared at varying solid contents of 63, 65, 67 and 69 wt.% and transferred into a fixed test container for ultrasonic monitoring. A 5 MHz contact transducer was used to record continuous A – Scan waveform under consistent acquisition settings (raw time – domain signals paired with frequency – domain representation). From each waveform, reflection amplitude and time – of – flight (ToF) features extracted from the slurry – air interface echo provides a compact representation of evolving acoustic response over time. The dataset is intended for benchmarking of signal processing pipelines, development of physics – informed feature extraction, and machine – learning models for slurry metrology and coating readiness assessment. By Packing signals, minimal metadata, and lightweight visualisation and analysis script, the resources enable reproducible reuse across materials – processing and signal – processing communities. Keywords: Ultrasonic testing, Lithium-ion battery manufacturing, Cathode slurry, Structural recovery, Process monitoring, Production line parameterisation\n\nCandidates:\nA. A 5 MHz contact transducer was used to record continuous A – Scan waveform under consistent acquisition settings (raw time – domain signals paired with frequency – domain representation).\nB. A 6 MHz contact transducer was used to record continuous A – Scan waveform under consistent acquisition settings (raw time – domain signals paired with frequency – domain representation).\nC. This data article presents an open ultrasonic pulse – echo dataset acquired from NMC – 622 cathode slurries monitored under static conditions to characterise their post – mixing structural recovery behaviour.\nD. The evidence does not state that from each waveform, reflection amplitude and time – of – flight (ToF) features extracted from the slurry – air interface echo provides a compact representation of evolving acoustic response over time.\nE. From each waveform, reflection amplitude and time – of – flight (ToF) features extracted from the slurry – air interface echo provides a compact representation of evolving acoustic response over time.\nF. Slurries were prepared at varying solid contents of 64, 65, 67 and 69 wt.% and transferred into a fixed test container for ultrasonic monitoring.\nG. Slurries were prepared at varying solid contents of 63, 65, 67 and 69 wt.% and transferred into a fixed test container for ultrasonic monitoring.\nH. This data article presents an open ultrasonic pulse – echo dataset acquired from NMC – 623 cathode slurries monitored under static conditions to characterise their post – mixing structural recovery behaviour.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13156995", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13156995/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f1eacf03006a181a0984", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAutomated phenotyping in ophthalmology requires accurate standardization of clinical terms to facilitate interoperability and research. This study evaluates the suitability of the human phenotype ontology (HPO) for automated extraction of ophthalmic phenotypes from narrative documentation. We developed a locally operated AI pipeline combining text segmentation and negation detection based on a small language model (PHI-4) with a dense retrieval approach using an augmented multilingual HPO catalog. Synonyms were incorporated into the HPO during training on anonymized consecutive physician letters. To validate the pipeline, 175 anterior segment and fundus descriptions from randomly picked medical records were manually annotated with HPO terms as ground truth. Overall, 342 HPO terms were identified manually (on average 2.53 terms per document), with 341 retrieved by the pipeline (on average 2.52 terms per document). Performance metrics showed a median Jaccard similarity of 0.67, precision of 0.83, recall of 0.82, and F1 score of 0.80. These results demonstrate that our AI pipeline effectively extracts standardized HPO terms from free-text ophthalmic findings. Integration of this pipeline into clinical information systems may enhance data interoperability and reduce manual coding workload in ophthalmology practices in the future. The online version contains supplementary material available at 10.1038/s41598-026-51512-z.\n\nCandidates:\nA. Synonyms were not incorporated into the HPO during training on anonymized consecutive physician letters.\nB. We developed a locally operated AI pipeline combining text segmentation and negation detection based on a small language model (PHI-4) with a dense retrieval approach using an augmented multilingual HPO catalog.\nC. The evidence does not state that this study evaluates the suitability of the human phenotype ontology (HPO) for automated extraction of ophthalmic phenotypes from narrative documentation.\nD. The evidence does not state that automated phenotyping in ophthalmology requires accurate standardization of clinical terms to facilitate interoperability and research.\nE. Automated phenotyping in ophthalmology requires accurate standardization of clinical terms to facilitate interoperability and research.\nF. Synonyms were incorporated into the HPO during training on anonymized consecutive physician letters.\nG. We developed a locally operated AI pipeline combining text segmentation and negation detection based on a small language model (PHI-5) with a dense retrieval approach using an augmented multilingual HPO catalog.\nH. This study evaluates the suitability of the human phenotype ontology (HPO) for automated extraction of ophthalmic phenotypes from narrative documentation.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13157479", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13157479/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-49a8b5cfe537098f3ddd", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nWhole‐cell biocatalysis offers a sustainable alternative to traditional chemical synthesis for producing pharmaceutically relevant, often chiral, amines and amino acids. Saccharomyces cerevisiae has emerged as a privileged microbial chassis due to its robustness, ease of genetic manipulation, and GRAS status. This concise review summarizes recent advances in metabolic and genetic engineering of S. cerevisiae for amine biocatalysis, focusing on strategies to overcome bottlenecks such as enzyme gene expression, cofactor regeneration, and precursor channeling. The first section covers state‐of‐the‐art methods for engineered strain construction, including genomic editing, optimization of gene expression (copy number, promoters, terminators, codon usage), and metabolic engineering (pathway balancing, compartmentalization, cofactor supply, transport proteins, auxiliary enzymes, and enzyme targeting via signal peptides), all enhancing product yields and enabling complex amine synthesis. The central section critically discusses compound families accessible via engineered S. cerevisiae , including various amines, amino alcohols, and amino acids such as l ‐carnitine, ergothioneine, halogenated tryptamine, serotonin, psilocybin, spermidine, l ‐ornithine, and mycosporine derivatives. Bioproduction of complex alkaloids, such as tropine derivatives (hyoscyamine and scopolamine) and ergot alkaloids, is also reviewed. Finally, current challenges and future perspectives are outlined, highlighting the integration of systems and synthetic biology tools to establish S. cerevisiae as a scalable platform for industrial amine production.\n\nCandidates:\nA. Saccharomyces cerevisiae has emerged as a privileged microbial chassis due to its robustness, ease of genetic manipulation, and GRAS status.\nB. Whole‐cell biocatalysis offers a sustainable alternative to traditional chemical synthesis for producing pharmaceutically relevant, often chiral, amines and amino acids.\nC. The evidence does not state that this concise review summarizes recent advances in metabolic and genetic engineering of S.\nD. The evidence does not state that saccharomyces cerevisiae has emerged as a privileged microbial chassis due to its robustness, ease of genetic manipulation, and GRAS status.\nE. This concise review summarizes recent advances in metabolic and genetic engineering of S.\nF. cerevisiae for amine biocatalysis, focusing on strategies to overcome bottlenecks such as enzyme gene expression, cofactor regeneration, and precursor channeling.\nG. The evidence does not state that whole‐cell biocatalysis offers a sustainable alternative to traditional chemical synthesis for producing pharmaceutically relevant, often chiral, amines and amino acids.\nH. The evidence does not state that cerevisiae for amine biocatalysis, focusing on strategies to overcome bottlenecks such as enzyme gene expression, cofactor regeneration, and precursor channeling.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13157890", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13157890/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-57fc60d0a42d35592fae", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIn times of a climate crisis caused by extreme emissions of green-house gases on a global scale, mitigation solutions need to be found. One solution is the system of carbon capture and utilization (CCU), where C1 gases, such as carbon monoxide (CO), carbon dioxide (CO 2 ), or methane, are either redirected from industrial off-gas streams or directly air-captured. A biotechnological process for CCU is the use of Methanothermobacter marburgensis for CO 2 fixation and production of value-added compounds. In this study, we focused on valine production, an amino acid important for human or feedstock nutrition. We demonstrated overproduction of valine from CO 2 in M. marburgensis with temperature-induced promoters. Here, we reached a 12.9-fold increase in valine production between the OFF- and ON states of the inducible promoter with a maximal specific production rate of 14.17 mg gCDW −1 h −1 of valine in closed batch experiments. In the second approach for valine production, we overexpressed acetolactate synthase genes with resistance to allosteric valine inhibition from Methanothermobacter thermautotrophicus recombinant in M. marburgensis . We identified a strong reduction in allosteric inhibition towards valine. This resulted in specific valine productivity of up to 40 mg gCDW −1 h −1 and states the highest specific productivity on an individual amino acid in methanogens. With those findings, we expanded the toolbox for genetic modification of M. marburgensis by a thermo-inducible promoter system and applied protein engineering for enhanced production of value-added compounds to M. marburgensis . This proof of concept shows the feasibility of archaeal cell factories generation via genetic engineering for industrial production of value-added compounds with thermophilic methanogens.\n\nCandidates:\nA. A biotechnological process for CCU is the use of Methanothermobacter marburgensis for CO 2 fixation and production of value-added compounds.\nB. The evidence does not state that in this study, we focused on valine production, an amino acid important for human or feedstock nutrition.\nC. One solution is the system of carbon capture and utilization (CCU), where C1 gases, such as carbon monoxide (CO), carbon dioxide (CO 2 ), or methane, are either redirected from industrial off-gas streams or directly air-captured.\nD. The evidence does not state that in times of a climate crisis caused by extreme emissions of green-house gases on a global scale, mitigation solutions need to be found.\nE. In times of a climate crisis caused by extreme emissions of green-house gases on a global scale, mitigation solutions need to be found.\nF. In this study, we focused on valine production, an amino acid important for human or feedstock nutrition.\nG. A biotechnological process for CCU is the use of Methanothermobacter marburgensis for CO 3 fixation and production of value-added compounds.\nH. One solution is the system of carbon capture and utilization (CCU), where C2 gases, such as carbon monoxide (CO), carbon dioxide (CO 2 ), or methane, are either redirected from industrial off-gas streams or directly air-captured.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13158618", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13158618/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ad1c5ee1bf73a01ecad7", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nExtracellular vesicles (EVs) are nanoscale mediators of intercellular communication with diverse molecular cargoes that reflect their cell of origin. Advances in isolation, detection, and single‐particle analytics have revealed increasing molecular and functional heterogeneity, while exposing limitations in how EV identity and activity are currently defined. The field has expanded rapidly; however, translational progress is constrained by an incomplete mechanistic understanding, a lack of standardized measurements, and inconsistencies in regulatory classification. This review provides a critical synthesis of current EV research from an analytical and translational perspective, emphasizing the measurement science needed to define, compare, and benchmark EV preparations as therapeutic products. We discuss evolving regulatory frameworks and recent updates to the MISEV guidelines, highlighting the need for operational definitions grounded in source material, isolation method, and molecular markers. Updated workflow considerations are presented across EV production and characterization, with a focus on orthogonal analyses, quantification of co‐isolates, and potency assays that support reproducibility and quality control. Together, these advances and ongoing challenges underscore the need for analytical rigor to transform EV research from descriptive studies into a reproducible and quantitative measurement science.\n\nCandidates:\nA. Advances in isolation, detection, and single‐particle analytics have revealed increasing molecular and functional heterogeneity, while exposing limitations in how EV identity and activity are currently defined.\nB. The field has expanded rapidly; however, translational progress is constrained by an incomplete mechanistic understanding, a lack of standardized measurements, and inconsistencies in regulatory classification.\nC. This review provides a critical synthesis of current EV research from an analytical and translational perspective, emphasizing the measurement science needed to define, compare, and benchmark EV preparations as therapeutic products.\nD. Extracellular vesicles (EVs) are not nanoscale mediators of intercellular communication with diverse molecular cargoes that reflect their cell of origin.\nE. Advances in isolation, detection, and single‐particle analytics have revealed increasing molecular and functional heterogeneity, while exposing limitations in how EV identity and activity are not currently defined.\nF. The evidence does not state that this review provides a critical synthesis of current EV research from an analytical and translational perspective, emphasizing the measurement science needed to define, compare, and benchmark EV preparations as therapeutic products.\nG. Extracellular vesicles (EVs) are nanoscale mediators of intercellular communication with diverse molecular cargoes that reflect their cell of origin.\nH. The field has expanded rapidly; however, translational progress is not constrained by an incomplete mechanistic understanding, a lack of standardized measurements, and inconsistencies in regulatory classification.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13159167", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13159167/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d1d9191cd002660f1b6a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGiven the biotechnological potential of yeast‐derived oils for oleochemical production, genes encoding lipid metabolism enzymes are key targets for metabolic engineering. Genetic engineering tools such as Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9, Transcription Activator‐Like Effector Nucleases (TALENs), Zinc‐Finger Nucleases (ZFNs), RNA interference (RNAi), and integrative plasmids can be used to modulate fatty acid biosynthesis and optimize lipid production. Among them, the CRISPR/Cas9 system, recognized for its simplicity and efficiency, has been deployed as a tool to create oleaginous yeast strains with high lipid productivity and features suitable for application in biorefineries. Species such as Cutaneotrichosporon oleaginosus , Rhodotorula toruloides , Candida spp., and Yarrowia lipolytica have already been engineered using CRISPR/Cas9 to enhance the production of fatty acids and their derivatives. However, designing and constructing an efficient CRISPR/Cas9 platform for oleaginous yeasts faces several hurdles, including low transformation efficiency, difficulties in expressing Cas9 and sgRNAs efficiently and consistently, the lack of well‐characterized promoters, limited availability of PAM sequences, and poorly understood DNA repair mechanisms. Here, we address the application of the CRISPR/Cas9 system in oleaginous yeasts, laying out the challenges to developing efficient platforms and highlighting key trends in the field. We compare and discuss alternative CRISPR‐Cas9 expression strategies to provide an overview of the current landscape and support the development of new approaches.\n\nCandidates:\nA. Given the biotechnological potential of yeast‐derived oils for oleochemical production, genes encoding lipid metabolism enzymes are key targets for metabolic engineering.\nB. Among them, the CRISPR/Cas9 system, recognized for its simplicity and efficiency, has been deployed as a tool to create oleaginous yeast strains with high lipid productivity and features suitable for application in biorefineries.\nC. Genetic engineering tools such as Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9, Transcription Activator‐Like Effector Nucleases (TALENs), Zinc‐Finger Nucleases (ZFNs), RNA interference (RNAi), and integrative plasmids can be used to modulate fatty acid biosynthesis and optimize lipid production.\nD. Given the biotechnological potential of yeast‐derived oils for oleochemical production, genes encoding lipid metabolism enzymes are not key targets for metabolic engineering.\nE. Among them, the CRISPR/Cas10 system, recognized for its simplicity and efficiency, has been deployed as a tool to create oleaginous yeast strains with high lipid productivity and features suitable for application in biorefineries.\nF. Species such as Cutaneotrichosporon oleaginosus , Rhodotorula toruloides , Candida spp., and Yarrowia lipolytica have already been engineered using CRISPR/Cas9 to enhance the production of fatty acids and their derivatives.\nG. Genetic engineering tools such as Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas10, Transcription Activator‐Like Effector Nucleases (TALENs), Zinc‐Finger Nucleases (ZFNs), RNA interference (RNAi), and integrative plasmids can be used to modulate fatty acid biosynthesis and optimize lipid production.\nH. Species such as Cutaneotrichosporon oleaginosus , Rhodotorula toruloides , Candida spp., and Yarrowia lipolytica have already been engineered using CRISPR/Cas10 to enhance the production of fatty acids and their derivatives.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13159405", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13159405/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-72e49bf2f5a1c9fb0594", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTo address agrochemical pollution and improve microbial screening efficiency, this study aimed to develop an integrated pipeline for isolating high-yielding lipopeptide-producing Bacillus strains and validating their agricultural applications. A tiered screening strategy combining hemolytic activity assay, plate confrontation, and oil-spreading assay was established to isolate strains from rhizosphere soils. Lipopeptide production was identified by MALDI-TOF MS and quantified via HPLC. Genomic analysis was performed to elucidate the genetic basis of lipopeptide synthesis. Crude lipopeptide extracts were evaluated for antimicrobial activity via plate assays, and for plant growth-promoting effects under pot and field conditions. The screening successfully identified potent Bacillus producers with multiple plant growth-promoting traits. Strain A1C-8 produced 1140.7 mg/L surfactin, Bv1 produced 450.1 mg/L iturin, and C5C-6 produced 707.8 mg/L fengycin. Crude extracts exhibited strong antimicrobial activity, with inhibition rates of 54.6% against Staphylococcus aureus and 273.3% against Rhizoctonia solani . Genomic analysis confirmed a robust genetic basis for lipopeptide synthesis. Notably, field trials demonstrated that lipopeptide extracts alone significantly enhanced crop growth: maize ear fresh weight increased by 32.46 % under 100 mg/L treatment, peanut pod number per plant rose by 42.75 % under 200 mg/L treatment, and sunflower fresh seed weight per capitulum improved by 35 % with 50 mg/L application. This study provides an integrated pipeline from strain screening to field validation, highlighting Bacillus lipopeptides as sustainable alternatives to agrochemicals for combined biocontrol and plant growth promotion.\n\nCandidates:\nA. Lipopeptide production was not identified by MALDI-TOF MS and quantified via HPLC.\nB. Genomic analysis was not performed to elucidate the genetic basis of lipopeptide synthesis.\nC. The evidence does not state that to address agrochemical pollution and improve microbial screening efficiency, this study aimed to develop an integrated pipeline for isolating high-yielding lipopeptide-producing Bacillus strains and validating their agricultural applications.\nD. A tiered screening strategy combining hemolytic activity assay, plate confrontation, and oil-spreading assay was not established to isolate strains from rhizosphere soils.\nE. To address agrochemical pollution and improve microbial screening efficiency, this study aimed to develop an integrated pipeline for isolating high-yielding lipopeptide-producing Bacillus strains and validating their agricultural applications.\nF. A tiered screening strategy combining hemolytic activity assay, plate confrontation, and oil-spreading assay was established to isolate strains from rhizosphere soils.\nG. Lipopeptide production was identified by MALDI-TOF MS and quantified via HPLC.\nH. Genomic analysis was performed to elucidate the genetic basis of lipopeptide synthesis.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13161030", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13161030/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-64db15f5ee48ae76f37d", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"31 °C\", \"1.5–1.5%\", \"30 °C\", \"0.5–1.5%\", \"95%\", \"96%\"]\n\nEvidence:\nCytokines are small glycosylated polypeptides that orchestrate immune responses and are widely produced in recombinant form for therapeutic and research purposes. This review highlights Komagataella phaffii as an efficient host for the heterologous expression of recombinant cytokines from human and other species. A systematic search of PubMed, Scopus, and Web of Science identified studies addressing upstream and downstream processes involved in cytokine expression. Molecular design strategies such as codon optimization, vector design, and signal peptide selection are discussed together with process parameters including temperature, inducer concentration, and bioreactor conditions, as well as recovery and purification of biologically active cytokines. Optimal production is often associated using multi-copy vectors driven by the AOX1 promoter, α-factor secretion signals, methanol induction (0.5–1.5% v/v), temperatures below 30 °C, and co-feeding strategies. Downstream purification commonly yields products exceeding 95% purity. Strategies such as PEGylation, albumin fusion, and antibody fusion are also described to improve cytokine stability and half-life. This review integrates and critically analyzes molecular and bioprocessing advances that establish K. phaffii as a powerful platform for recombinant cytokine production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13161034", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13161034/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-baf6cf01550b8c62ee4b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHigh-content imaging (HCI) involves the automated acquisition and quantitative analysis of cell phenotypes from microscopy images. These studies often rely on screening, which can involve thousands of chemical or genetic perturbations that produce terabytes of microscopy data. To extract meaningful biological insights, these data must be processed into quantitative features through a technique known as image-based profiling. A major analytical bottleneck is curating the high-dimensional, single-cell data derived from various image-analysis tools. These datasets suffer from inconsistent schemas, inefficient file formats, and undocumented ontological relationships. These challenges reduce reproducibility and slow progress in downstream applications. To solve these issues, we introduce CytoTable, a software package for harmonizing single-cell image-based profiling. CytoTable enables modular, portable, and cross-language data integration through a robust, reproducible, and scalable engine that harmonizes single-cell readouts from multiple image-analysis tools, preparing for feature integration with software in the Cytomining ecosystem such as Pycytominer. Keywords: image-based profiling, open-source software, single-cell harmonization, microscopy image analysis, reproducibility, high-content imaging\n\nCandidates:\nA. High-content imaging (HCI) involves the automated acquisition and quantitative analysis of cell phenotypes from microscopy images.\nB. A major analytical bottleneck is curating the high-dimensional, single-cell data derived from various image-analysis tools.\nC. The evidence does not state that high-content imaging (HCI) involves the automated acquisition and quantitative analysis of cell phenotypes from microscopy images.\nD. To extract meaningful biological insights, these data must be processed into quantitative features through a technique known as image-based profiling.\nE. A major analytical bottleneck is not curating the high-dimensional, single-cell data derived from various image-analysis tools.\nF. The evidence does not state that to extract meaningful biological insights, these data must be processed into quantitative features through a technique known as image-based profiling.\nG. These studies often rely on screening, which can involve thousands of chemical or genetic perturbations that produce terabytes of microscopy data.\nH. These studies often rely on screening, which cannot involve thousands of chemical or genetic perturbations that produce terabytes of microscopy data.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13161684", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13161684/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f838fc95fc864322294a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nLarge quantities of spoiled date palm fruits are generated annually and are often discarded, resulting in environmental and economic losses. Developing sustainable strategies to convert this waste into valuable products is therefore an important challenge. This study aimed to establish an integrated bioprocess that utilizes spoiled date fruits as a low-cost substrate to simultaneously produce biodiesel-grade lipids, natural pigments, and fungal chitosan using Talaromyces atroroseus . The objectives included optimizing substrate concentration and pretreatment conditions, evaluating lipid and pigment production, and recovering chitosan from residual fungal biomass. The results demonstrated that moderate substrate levels supported maximum lipid and pigment yields, while dilute acid pretreatment significantly enhanced sugar release from spoiled date fruits and fungal productivity. The extracted lipids were rich in C16–C18 fatty acids and complied with international biodiesel quality standards. The produced pigments showed good pH and thermal stability along with strong antioxidant activity. Additionally, chitosan recovered from de-oiled biomass exhibited suitable structural properties, and pigment–chitosan composites displayed enhanced antioxidant performance. Therefore, this work presents a sustainable biorefinery approach that transforms agricultural waste into multiple high-value products, supporting waste valorization, renewable energy production, and circular bioeconomy initiatives and offering clear benefits to society and industry.\n\nCandidates:\nA. This study aimed to establish an integrated bioprocess that utilizes spoiled date fruits as a low-cost substrate to simultaneously produce biodiesel-grade lipids, natural pigments, and fungal chitosan using Talaromyces atroroseus .\nB. The objectives included optimizing substrate concentration and pretreatment conditions, evaluating lipid and pigment production, and recovering chitosan from residual fungal biomass.\nC. The evidence does not state that this study aimed to establish an integrated bioprocess that utilizes spoiled date fruits as a low-cost substrate to simultaneously produce biodiesel-grade lipids, natural pigments, and fungal chitosan using Talaromyces atroroseus .\nD. Large quantities of spoiled date palm fruits are generated annually and are often discarded, resulting in environmental and economic losses.\nE. The evidence does not state that the objectives included optimizing substrate concentration and pretreatment conditions, evaluating lipid and pigment production, and recovering chitosan from residual fungal biomass.\nF. Large quantities of spoiled date palm fruits are not generated annually and are often discarded, resulting in environmental and economic losses.\nG. Developing sustainable strategies to convert this waste into valuable products is not therefore an important challenge.\nH. Developing sustainable strategies to convert this waste into valuable products is therefore an important challenge.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13162831", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13162831/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aa981044a7095fb1c392", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicroalgae are tiny organisms that play an essential role in nature, especially in places with harsh environmental conditions. In this study, we examined a microalga called Chloroidium saccharophilum , which we found in Laguna Blanca, Chile. This is important because this species had never been reported in Chile before or in this region of South America. Its presence in such an extreme environment makes it valuable for scientific and biotechnological research. This microalga is naturally resistant to very salty water, strong sunlight, and temperature changes—conditions that would harm many other organisms. These special abilities give it potential as a natural source of useful compounds such as antioxidants. Antioxidants help protect cells from damage caused by stress and inflammation, which is relevant for health-related products and other applications. Although the microalga showed strong growth, it produced only small amounts of antioxidants under the conditions we tested. This suggests that more research is needed to discover the best environmental factors that encourage higher production of these beneficial compounds. Learning about organisms that thrive in extreme areas can support new sustainable technologies and contribute to economic and social development in remote regions.\n\nCandidates:\nA. The evidence does not state that in this study, we examined a microalga called Chloroidium saccharophilum , which we found in Laguna Blanca, Chile.\nB. This is not important because this species had never been reported in Chile before or in this region of South America.\nC. Microalgae are not tiny organisms that play an essential role in nature, especially in places with harsh environmental conditions.\nD. Microalgae are tiny organisms that play an essential role in nature, especially in places with harsh environmental conditions.\nE. This is important because this species had never been reported in Chile before or in this region of South America.\nF. Its presence in such an extreme environment makes it valuable for scientific and biotechnological research.\nG. The evidence does not state that its presence in such an extreme environment makes it valuable for scientific and biotechnological research.\nH. In this study, we examined a microalga called Chloroidium saccharophilum , which we found in Laguna Blanca, Chile.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13162895", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13162895/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-74fbad2f45e7760e5636", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe association between dietary fiber and phenolic compounds allows the latter to reach the colon, where most polysaccharides undergo fermentation. This bioprocessing weakens the matrix and promotes the release of the phenolic compounds, which then exert beneficial effects on intestinal function. Although this notion is widely accepted, supporting evidence remains scarce. In this study, we subjected grape pomace skin to in vitro digestion to obtain an indigestible fraction suitable for SHIME bioreactors. Throughout these stages, we observed a sequential increase in the release of phenolic compounds, with colonic fermentation playing an important role. Although we did not observe an increase in short-chain fatty acid (SCFA) production by the gut microbiota, we performed a repeated-challenge design on differentiated Caco-2 monolayers. With this approach, we found that the phenolic-rich ferment prevented the transepithelial electrical resistance (TEER) drop on the second challenge and modulated the transcriptomic profile assessed by RNA-seq. Our findings indicate that the Caco-2 cellular responses mentioned above were SCFA-independent and likely due to the differential impact of phenolic compound load after colonic fermentation of grape pomace skin.\n\nCandidates:\nA. Although this notion is widely accepted, supporting evidence remains scarce.\nB. This bioprocessing weakens the matrix and promotes the release of the phenolic compounds, which then exert beneficial effects on intestinal function.\nC. The evidence does not state that this bioprocessing weakens the matrix and promotes the release of the phenolic compounds, which then exert beneficial effects on intestinal function.\nD. Although this notion is not widely accepted, supporting evidence remains scarce.\nE. In this study, we subjected grape pomace skin to in vitro digestion to obtain an indigestible fraction suitable for SHIME bioreactors.\nF. The evidence does not state that in this study, we subjected grape pomace skin to in vitro digestion to obtain an indigestible fraction suitable for SHIME bioreactors.\nG. The association between dietary fiber and phenolic compounds allows the latter to reach the colon, where most polysaccharides undergo fermentation.\nH. The evidence does not state that the association between dietary fiber and phenolic compounds allows the latter to reach the colon, where most polysaccharides undergo fermentation.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13163868", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13163868/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b8850173a96ff15aacf3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGenetically modified (GM) lactic acid bacteria (LAB) are gaining attention as tools for innovation in the food sector, health applications, and industrial processes. LAB have long been used safely due to their GRAS/QPS status, making them suitable for improving fermentation and synthesizing specific and beneficial metabolites. Advances in genomics and gene editing have significantly expanded the available tools, ranging from classical mutagenesis to site-specific recombination, homologous recombination in non-coding regions, CRISPR-based systems, and food-grade chromosomal integration. These approaches enable the insertion of desired genes and the development of engineered strains with tailored functionalities. GM-LAB are also being studied as live delivery systems for therapeutic molecules, including cytokines, hormones, antimicrobial peptides, and vaccine antigens. Engineered strains of Lactococcus lactis and Lactobacillus spp. have yielded promising outcomes in applications such as mucosal immunization, modulation of inflammatory and metabolic responses, and inhibition of pathogenic microorganisms, including multidrug-resistant bacteria. From an industrial perspective, several studies highlight their potential for cost-effective recombinant protein production and the synthesis of high-value metabolites through fermentation. However, within the European Union, their use is subject to stringent regulatory oversight, requiring comprehensive molecular and environmental risk assessments, careful evaluation of horizontal gene transfer, and a preference for markerless chromosomal integrations. Despite these constraints, GM-LAB offer significant potential to improve food quality, sustainability, and human health.\n\nCandidates:\nA. Genetically modified (GM) lactic acid bacteria (LAB) are gaining attention as tools for innovation in the food sector, health applications, and industrial processes.\nB. These approaches enable the insertion of desired genes and the development of engineered strains with tailored functionalities.\nC. Genetically modified (GM) lactic acid bacteria (LAB) are not gaining attention as tools for innovation in the food sector, health applications, and industrial processes.\nD. The evidence does not state that advances in genomics and gene editing have significantly expanded the available tools, ranging from classical mutagenesis to site-specific recombination, homologous recombination in non-coding regions, CRISPR-based systems, and food-grade chromosomal integration.\nE. Advances in genomics and gene editing have significantly expanded the available tools, ranging from classical mutagenesis to site-specific recombination, homologous recombination in non-coding regions, CRISPR-based systems, and food-grade chromosomal integration.\nF. The evidence does not state that these approaches enable the insertion of desired genes and the development of engineered strains with tailored functionalities.\nG. The evidence does not state that lAB have long been used safely due to their GRAS/QPS status, making them suitable for improving fermentation and synthesizing specific and beneficial metabolites.\nH. LAB have long been used safely due to their GRAS/QPS status, making them suitable for improving fermentation and synthesizing specific and beneficial metabolites.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13164114", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13164114/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0ed208b571fbc6fc5f6b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicrobial lipids have emerged as a promising sustainable alternative to plant- and petroleum-derived oils, with applications spanning biofuels, oleochemicals, nutraceuticals, and specialty materials. Significant advances in metabolic engineering and strain development have increased lipid production capacity across diverse microorganisms. Numerous reviews have summarized the biological and metabolic advances in this field, highlighting significant progress in metabolic engineering and strain development that has increased lipid production capacity across diverse microorganisms. However, translating these gains into economically viable industrial processes remains a major challenge. This review examines process engineering strategies for microbial lipid production across the full bioprocessing pipeline, from laboratory-scale strain evolution to industrial-scale operation. We discuss recent developments in adaptive laboratory evolution, systems-guided strain optimization, and robustness engineering, emphasizing their implications for process performance. Key bioprocess parameters—including substrate selection, nutrient limitation strategies, reactor design, oxygen transfer, and process control—are critically evaluated for their impact on lipid yield, productivity, and scalability. Furthermore, downstream processing considerations and techno-economic constraints are analyzed in the context of large-scale implementation. By integrating strain-level innovations with process engineering principles, this review highlights current bottlenecks, emerging solutions, and future directions for achieving efficient and scalable microbial lipid biomanufacturing.\n\nCandidates:\nA. Numerous reviews have summarized the biological and metabolic advances in this field, highlighting significant progress in metabolic engineering and strain development that has increased lipid production capacity across diverse microorganisms.\nB. However, translating these gains into economically viable industrial processes remains a major challenge.\nC. Significant advances in metabolic engineering and strain development have increased lipid production capacity across diverse microorganisms.\nD. The evidence does not state that however, translating these gains into economically viable industrial processes remains a major challenge.\nE. The evidence does not state that microbial lipids have emerged as a promising sustainable alternative to plant- and petroleum-derived oils, with applications spanning biofuels, oleochemicals, nutraceuticals, and specialty materials.\nF. The evidence does not state that numerous reviews have summarized the biological and metabolic advances in this field, highlighting significant progress in metabolic engineering and strain development that has increased lipid production capacity across diverse microorganisms.\nG. Microbial lipids have emerged as a promising sustainable alternative to plant- and petroleum-derived oils, with applications spanning biofuels, oleochemicals, nutraceuticals, and specialty materials.\nH. The evidence does not state that significant advances in metabolic engineering and strain development have increased lipid production capacity across diverse microorganisms.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13164146", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13164146/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-001d4b9a7c912e5f977a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAkkermansia muciniphila , a next-generation probiotic in the human intestinal mucus layer, exhibits significant health-promoting properties. However, traditional static culture systems fail to replicate the dynamic peristaltic environment of the gastrointestinal tract, limiting understanding of its metabolic characteristics. This study employed an improved gastrointestinal bioreactor simulating intestinal peristalsis to investigate A. muciniphila growth dynamics and metabolomic profiles under dynamic conditions. Dynamic cultivation significantly enhanced bacterial growth. Biomass reached 1.32 ± 0.03 g/L in bovine heart infusion (BHI) medium and 2.03 ± 0.05 g/L in BHI supplemented with 2.5 g/L porcine mucin. These values represent increases of 45.05% and 123.08% relative to static BHI cultures, respectively. Dynamic conditions markedly elevated short-chain fatty acid production (acetic, propionic, isobutyric, isovaleric acids). Untargeted metabolomics identified 1463 metabolites with 1294 showing significant differential expression. Dynamic cultivation substantially altered amino acid biosynthesis, fatty acid, purine, and pyrimidine metabolism. These findings advance the understanding of A. muciniphila physiology and provide insights into its metabolic characteristics under simulated intestinal conditions.\n\nCandidates:\nA. However, traditional static culture systems fail to replicate the dynamic peristaltic environment of the gastrointestinal tract, limiting understanding of its metabolic characteristics.\nB. muciniphila growth dynamics and metabolomic profiles under dynamic conditions.\nC. Akkermansia muciniphila , a next-generation probiotic in the human intestinal mucus layer, exhibits significant health-promoting properties.\nD. This study employed an improved gastrointestinal bioreactor simulating intestinal peristalsis to investigate A.\nE. The evidence does not state that muciniphila growth dynamics and metabolomic profiles under dynamic conditions.\nF. The evidence does not state that this study employed an improved gastrointestinal bioreactor simulating intestinal peristalsis to investigate A.\nG. The evidence does not state that akkermansia muciniphila , a next-generation probiotic in the human intestinal mucus layer, exhibits significant health-promoting properties.\nH. The evidence does not state that however, traditional static culture systems fail to replicate the dynamic peristaltic environment of the gastrointestinal tract, limiting understanding of its metabolic characteristics.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13164191", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13164191/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-187b6f4594034a9502c9", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nLentils ( Lens culinaris ; family: Fabaceae) are increasingly recognized as functional legumes with potential benefits for gut health because they provide bioactive peptides, resistant starch, and polyphenol-rich fractions within a shared food matrix. However, most existing studies have focused on individual lentil-derived compounds, and their matrix-dependent complementary interactions during digestion and fermentation remain insufficiently resolved. This review synthesizes current evidence on lentil-derived peptides, resistant starch, and polyphenols, with particular emphasis on their matrix-dependent complementary relationships, digestion-dependent transformation, microbial co-metabolism, and implications for intestinal barrier function. During gastrointestinal digestion and colonic fermentation, lentil proteins, resistant starch, and phenolic compounds undergo sequential transformation, yielding bioactive peptides, fermentable substrates, short-chain fatty acids (SCFAs), and phenolic metabolites that may collectively influence microbial composition and metabolic activity. Emerging evidence suggests that these interconnected processes may support gut health through microbiota–host crosstalk by modulating tight junction-related markers, reducing intestinal permeability, and maintaining epithelial homeostasis. Mechanistically, these effects have been associated with SCFA-mediated G protein-coupled receptor (GPCR) signaling, suppression of TLR4–NF-κB/MAPK inflammatory cascades, and activation of Keap1–Nrf2 antioxidant defenses, thereby attenuating oxidative stress and pro-inflammatory responses. Current evidence is more consistent with matrix-dependent complementary or convergent actions than with demonstrated synergy. At present, phenolic-rich fractions provide clear pathway-level evidence, whereas fermentation-linked carbohydrate effects are more strongly supported by microbiota- and in vivo-associated outcomes, and protein- or peptide-related mechanisms remain comparatively underdefined. Nevertheless, the evidence base remains limited by the scarcity of integrated studies, well-controlled human intervention trials, and factorial experimental designs capable of distinguishing complementary, additive, and truly synergistic effects among lentil bioactives. This review therefore highlights the need to move from describing coexisting beneficial effects toward formally testing interaction effects within physiologically relevant lentil matrices.\n\nCandidates:\nA. Lentils ( Lens culinaris ; family: Fabaceae) are increasingly recognized as functional legumes with potential benefits for gut health because they provide bioactive peptides, resistant starch, and polyphenol-rich fractions within a shared food matrix.\nB. During gastrointestinal digestion and colonic fermentation, lentil proteins, resistant starch, and phenolic compounds undergo sequential transformation, yielding bioactive peptides, fermentable substrates, short-chain fatty acids (SCFAs), and phenolic metabolites that may collectively influence microbial composition and metabolic activity.\nC. The evidence does not state that however, most existing studies have focused on individual lentil-derived compounds, and their matrix-dependent complementary interactions during digestion and fermentation remain insufficiently resolved.\nD. This review synthesizes current evidence on lentil-derived peptides, resistant starch, and polyphenols, with particular emphasis on their matrix-dependent complementary relationships, digestion-dependent transformation, microbial co-metabolism, and implications for intestinal barrier function.\nE. Lentils ( Lens culinaris ; family: Fabaceae) are not increasingly recognized as functional legumes with potential benefits for gut health because they provide bioactive peptides, resistant starch, and polyphenol-rich fractions within a shared food matrix.\nF. However, most existing studies have focused on individual lentil-derived compounds, and their matrix-dependent complementary interactions during digestion and fermentation remain insufficiently resolved.\nG. The evidence does not state that during gastrointestinal digestion and colonic fermentation, lentil proteins, resistant starch, and phenolic compounds undergo sequential transformation, yielding bioactive peptides, fermentable substrates, short-chain fatty acids (SCFAs), and phenolic metabolites that may collectively influence microbial composition and metabolic activity.\nH. The evidence does not state that this review synthesizes current evidence on lentil-derived peptides, resistant starch, and polyphenols, with particular emphasis on their matrix-dependent complementary relationships, digestion-dependent transformation, microbial co-metabolism, and implications for intestinal barrier function.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13165167", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13165167/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-471157ca793382359363", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nL-theanine is a characteristic non-proteinogenic amino acid found in tea leaves and has attracted considerable attention because of its diverse physiological activity and broad application prospects. γ-glutamyl transpeptidase (GGT) can catalyze the synthesis of L-theanine from L-glutamine and ethylamine without ATP consumption, highlighting its advantages for enzymatic production. In this study, a complete process was established for L-theanine production. Through screening of single and dual promoters, the optimal expression combination, PyxiE-PspoVG, was identified. Furthermore, by integrating the dal selection marker, an antibiotic-free engineered strain was developed. After flask-level optimization, the GGT activity reached 27.32 U/mL and further increased to 127.37 U/mL in 3 L fed-batch fermentation. Using the fermentation broth as the biocatalyst, fed-batch conversion of 0.6 M L-glutamine and 2 M ethylamine yielded 0.52 M (91.44 g/L) L-theanine within 24 h. Further integration of ceramic membrane filtration, ultrafiltration, nanofiltration, electrodialysis, activated-carbon decolorization and ethanol crystallization afforded a final product purity of 95.6%. This study offers a useful reference for large-scale L-theanine production.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13165169", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13165169/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-db5fb5dc3c43b39c0b37", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTriple-negative breast cancer (TNBC) remains a significant challenge in oncology, contributing to a significant portion of cancer-related deaths among women. Current therapeutic options, including chemotherapy, surgery, radiation, and hormonal targeting therapies, exhibit limited efficacy, necessitating the exploration of innovative treatment modalities. The emergence of drug resistance and the persistence of cancer stem cells (CSCs) further emphasize the urgent need for novel therapeutic strategies. In this context, natural killer cell-derived extracellular vesicles (NK-EVs) have emerged as a promising cell-free therapeutic approach that exhibits high tumor infiltration and cytotoxicity against cancer cells and CSCs. This study aims to investigate the efficacy of NK-EVs as a therapeutic strategy for TNBC using various clinically relevant models, including patient-derived xenografts. Pathway analysis suggests strong activation of apoptosis via canonical caspase activation, as well as necrosis, thereby confirming the important cytotoxic effect of NK-EVs. Interestingly, NK-EVs were also found to suppress TNBC CSCs by disrupting their functionality and viability, and NK-EV treatment increased the expression of apoptosis markers in both CSCs and non-CSCs. By elucidating the therapeutic efficacy and translational potential of NK-EV-based interventions in TNBC, these findings offer critical insights for the development of future immunotherapeutic strategies against this aggressive subtype of breast cancer.\n\nCandidates:\nA. The evidence does not state that in this context, natural killer cell-derived extracellular vesicles (NK-EVs) have emerged as a promising cell-free therapeutic approach that exhibits high tumor infiltration and cytotoxicity against cancer cells and CSCs.\nB. Current therapeutic options, including chemotherapy, surgery, radiation, and hormonal targeting therapies, exhibit limited efficacy, necessitating the exploration of innovative treatment modalities.\nC. In this context, natural killer cell-derived extracellular vesicles (NK-EVs) have emerged as a promising cell-free therapeutic approach that exhibits high tumor infiltration and cytotoxicity against cancer cells and CSCs.\nD. Triple-negative breast cancer (TNBC) remains a significant challenge in oncology, contributing to a significant portion of cancer-related deaths among women.\nE. The evidence does not state that triple-negative breast cancer (TNBC) remains a significant challenge in oncology, contributing to a significant portion of cancer-related deaths among women.\nF. The emergence of drug resistance and the persistence of cancer stem cells (CSCs) further emphasize the urgent need for novel therapeutic strategies.\nG. The evidence does not state that current therapeutic options, including chemotherapy, surgery, radiation, and hormonal targeting therapies, exhibit limited efficacy, necessitating the exploration of innovative treatment modalities.\nH. The evidence does not state that the emergence of drug resistance and the persistence of cancer stem cells (CSCs) further emphasize the urgent need for novel therapeutic strategies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13165227", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13165227/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f59c426bc5054037d9e1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMonoclonal antibody N -glycosylation is a critical quality attribute influencing therapeutic safety and efficacy, and is strongly influenced by bioprocess design. NISTCHO, a publicly available Chinese hamster ovary producer cell line, is increasingly encouraged for use as a reference system. However, the impact of feeding strategies on cellular performance and N -glycosylation has not been assessed. Here, we applied multivariate analysis of compositional N -glycan data to assess how feeding strategies influence N -glycan composition of cNISTmAb. We varied feeding strategies in frequency, glucose supply, and galactose/manganese supplementation. Feeding frequency had minimal impact on quality attributes but strongly affected culture performance, with every-other-day feeding improving titers and cell-specific productivity. High glucose availability supported growth and productivity. Low glucose strategies reduced titers and shifted N -glycosylation towards non-galactosylated and fucosylated species, despite lactate accumulation remaining within favorable ranges. Galactose and manganese consistently increased antibody galactosylation, with galactose additionally serving as an auxiliary carbon source, extending cell viability. Importantly, mAb glycation remained stable across all feeding strategies at harvest. Overall, these results demonstrate that feed composition and timing can be used to tune both cellular performance and mAb glycosylation, establishing NISTCHO as a robust benchmark for standardized process-quality studies. Subject terms: Biochemistry, Biological techniques, Biotechnology\n\nCandidates:\nA. The evidence does not state that here, we applied multivariate analysis of compositional N -glycan data to assess how feeding strategies influence N -glycan composition of cNISTmAb.\nB. Here, we applied multivariate analysis of compositional N -glycan data to assess how feeding strategies influence N -glycan composition of cNISTmAb.\nC. Monoclonal antibody N -glycosylation is a critical quality attribute influencing therapeutic safety and efficacy, and is strongly influenced by bioprocess design.\nD. The evidence does not state that however, the impact of feeding strategies on cellular performance and N -glycosylation has not been assessed.\nE. NISTCHO, a publicly available Chinese hamster ovary producer cell line, is not increasingly encouraged for use as a reference system.\nF. NISTCHO, a publicly available Chinese hamster ovary producer cell line, is increasingly encouraged for use as a reference system.\nG. Monoclonal antibody N -glycosylation is not a critical quality attribute influencing therapeutic safety and efficacy, and is strongly influenced by bioprocess design.\nH. However, the impact of feeding strategies on cellular performance and N -glycosylation has not been assessed.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13168316", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13168316/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-739b1426e7f78adea8b7", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"120 g/l\", \"121 g/l\", \"20 g/l\", \"21 g/l\"]\n\nEvidence:\nThe filamentous fungus Thermothelomyces heterothallica C1 has been developed into a highly productive protein production system for heterologous proteins like antibodies and vaccine candidates. While it is capable of secreting over 120 g/l of its native enzymes, monoclonal antibodies (mAbs) have been produced at titers exceeding 20 g/l, and strains engineered to produce human-type N-glycan structures have been developed. However, significant variability in mAb productivity and reduced production levels in glycoengineered strains limit the use of C1 as a widespread production host in the pharmaceutical industry. To address these issues, transcriptome analysis was conducted on strains producing five different mAbs with varying production efficiencies, as well as on mAb-producing strains with native and glycoengineered N-glycans. Genes related to protein folding and secretion, which are regulated in response to mAb production and glycoengineering, were over-expressed in a glycoengineered mAb producing C1 strain. In addition, genes identified based on previously described functions in the secretory pathway, including counterparts of human origin, were included in the study. Transcriptome analysis revealed that the mAb heavy and light chains were among the most abundantly expressed transcripts, indicating that production bottlenecks occur after transcription. Genes associated with protein folding, quality control, glycosylation, and transport within the secretory pathway were upregulated in the mAb-producing strains. This upregulation was more pronounced in strains with low mAb yields and in glycoengineered strains. The over-expression of 10 genes ( bet1 , dnaj -type gene, dpm1 , ero1 , erv46 , human calreticulin, human cypb , human mzb1 , pmr1 , and UDP-galactose transporter), each playing distinct roles in the secretory pathway, enhanced mAb production in glycoengineered C1 strains from 1.5- to 2.5-fold. Through a comprehensive analysis of transcriptome data from C1 strains producing various monoclonal antibodies (mAbs) and an extensive literature search, several factors related to protein folding and secretion were identified as potential targets for enhancing mAb production. The over-expression of some of these genes in glycoengineered C1 strains led to improvement in mAb production, with some genes enhancing mAb yields by 2.5-fold. The online version contains supplementary material available at 10.1186/s12934-026-02989-w.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13170295", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13170295/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b8d89931d979bddddf14", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThermothelomyces thermophilus is a filamentous fungus isolated from self-heating compost. Unlike most of the fungal kingdom, this species exhibits a growth optimum at 45°C and is intolerant of temperatures below 30°C. To investigate genetic contributors to temperature-dependent fitness in this system, we implemented a large-scale insertional mutagenesis approach. We generated thousands of T. thermophilus mutants and cultured them at temperature extremes in a standard medium. Phenotyping-by-sequencing identified dozens of disrupted loci representing candidate determinants of thermophilic life history, including several annotated in metal transport. We then validated a subset of screen hits with a directed, single-gene knockout paradigm. The results revealed a temperature-dependent regulatory logic for germination, the developmental decision by which a fungal spore initiates growth. Surprisingly, most mutants germinated far better at 50°C than the wild type in a standard medium and showed markedly slower germination at lower temperatures, consistent with altered germination regulation rather than enhanced intrinsic heat tolerance. We hypothesized that T. thermophilus has evolved sophisticated regulatory machinery to block germination at high temperatures unless environmental conditions are favorable. As a proof of concept, we surveyed media conditions and established that elevated zinc dampened germination of wild-type T. thermophilus at 50°C but promoted it at lower temperatures; mutation experiments made clear that such sensitivity was mediated in part by the zinc transporter zip . We interpret these results under a model in which T. thermophilus integrates temperature and nutrient availability to control the transition from spore dormancy to vegetative growth, a developmental decision that shapes fitness outcomes across temperatures. Fungal thermophiles thrive at temperatures that represent the upper limits of eukaryotic life. The regulatory and developmental mechanisms that shape their temperature-dependent fitness remain poorly understood. In this work, we elucidate how Thermothelomyces thermophilus integrates temperature cues with other environmental inputs during germination, a key life-cycle stage for dispersal. Our findings highlight germination regulation as an important contributor to fitness at elevated temperatures in a thermophilic eukaryote. These insights are of basic biological interest and provide a foundation for rational strategies to modulate temperature-dependent performance in industrial strains, with applications for high-temperature bioprocessing.\n\nCandidates:\nA. Thermothelomyces thermophilus is a filamentous fungus isolated from self-heating compost.\nB. Unlike most of the fungal kingdom, this species exhibits a growth optimum at 45°C and is intolerant of temperatures below 30°C.\nC. To investigate genetic contributors to temperature-dependent fitness in this system, we implemented a large-scale insertional mutagenesis approach.\nD. The evidence does not state that to investigate genetic contributors to temperature-dependent fitness in this system, we implemented a large-scale insertional mutagenesis approach.\nE. thermophilus mutants and cultured them at temperature extremes in a standard medium.\nF. The evidence does not state that thermophilus mutants and cultured them at temperature extremes in a standard medium.\nG. Thermothelomyces thermophilus is not a filamentous fungus isolated from self-heating compost.\nH. Unlike most of the fungal kingdom, this species exhibits a growth optimum at 46°C and is intolerant of temperatures below 30°C.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13170357", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13170357/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-66315830e9d2768de5d2", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTransferrin is one of the major soluble serum proteins and is responsible for iron transport. Industrially, it is significant as a component of mammalian cell culture media, where a safe and stable supply is necessary. However, because transferrin is a glycoprotein containing 19 disulfide bonds, it is difficult to produce as a recombinant protein in bacteria, and at present it is mainly sourced from animals. In glycoprotein production, stability of the N -glycan profile is crucial, as glycans play important roles in diverse biological processes and influence the efficacy of glycoproteins. In this study, we aimed to produce recombinant human transferrin (rhTF) with stable N -glycan profiles. We generated transgenic rice calli expressing human TF (hTF) as a secretory glycosylated protein. rhTF was successfully produced as a soluble protein in the liquid culture medium of transgenic rice calli and subsequently purified. We confirmed that rhTF contained two plant-specific N -glycans and that these profiles were consistent across production batches. Purified rhTFs promoted the proliferation of cultured animal cells and human iPS cells, similar to serum-derived transferrin. Our results demonstrate new possibilities for producing recombinant glycoproteins with stable N -glycan profiles using a plant cell culture-based secretory protein expression system. Keywords: N -glycan profile, recombinant protein, rice, transferrin\n\nCandidates:\nA. Transferrin is one of the major soluble serum proteins and is responsible for iron transport.\nB. Industrially, it is significant as a component of mammalian cell culture media, where a safe and stable supply is necessary.\nC. Transferrin is not one of the major soluble serum proteins and is responsible for iron transport.\nD. However, because transferrin is a glycoprotein containing 19 disulfide bonds, it is difficult to produce as a recombinant protein in bacteria, and at present it is mainly sourced from animals.\nE. In glycoprotein production, stability of the N -glycan profile is crucial, as glycans play important roles in diverse biological processes and influence the efficacy of glycoproteins.\nF. However, because transferrin is a glycoprotein containing 20 disulfide bonds, it is difficult to produce as a recombinant protein in bacteria, and at present it is mainly sourced from animals.\nG. In glycoprotein production, stability of the N -glycan profile is not crucial, as glycans play important roles in diverse biological processes and influence the efficacy of glycoproteins.\nH. Industrially, it is not significant as a component of mammalian cell culture media, where a safe and stable supply is necessary.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13170787", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13170787/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cf0369c7ba3af46f0d5c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nDirect manufacturing of alloys through casting is economical, user-friendly and conducive to near-net-shape processing, and therefore of great application interest. However, as-cast alloys are rarely used directly because, without post-cast thermomechanical treatments, the solidification product is susceptible to coarse grains, compositional segregation and particularly unsatisfying precipitates, which tend towards inadequate strength and brittleness. These ubiquitous disadvantages have, for a long time, seriously hindered practical applications. Here, through screening by using thermodynamic calculations and calorimetric monitoring, we successfully landed non-equiatomic NiCoCrAlTaZrB complex-concentrated alloys (CCAs) to realize in situ micro-segregation-induced size-gradient L1 2 nanoprecipitates increasing from dendritic to interdendritic regions. Consequently, without any post-cast treatment, prolific in situ nanoprecipitation strengthening renders ultrahigh as-cast yield strength at the gigapascal (GPa) level, while multiscale chemical/structural segregation heterogeneities reinforced by gradient nanoprecipitates result in progressive and persistent hetero-deformation within dendrites, thereby sustaining a high strain-hardening rate of ∼3 GPa and uniform tensile elongation of up to ∼32%. Such robust as-cast strength−ductility synergy not only exceeds all previous as-cast CCAs, but also outperforms all commercial alloys that have already been optimized via post-cast treatments. This advance through exploiting in situ size-gradient nanoprecipitation accomplishes a goal that is more practical than the common pursuit for property records, which all require downstream (multistep and/or subtractive) processing that increases production time and expenses, and even raises severe environmental concerns.\n\nCandidates:\nA. Here, through screening by using thermodynamic calculations and calorimetric monitoring, we successfully landed non-equiatomic NiCoCrAlTaZrB complex-concentrated alloys (CCAs) to realize in situ micro-segregation-induced size-gradient L2 2 nanoprecipitates increasing from dendritic to interdendritic regions.\nB. The evidence does not state that these ubiquitous disadvantages have, for a long time, seriously hindered practical applications.\nC. Direct manufacturing of alloys through casting is not economical, user-friendly and conducive to near-net-shape processing, and therefore of great application interest.\nD. Direct manufacturing of alloys through casting is economical, user-friendly and conducive to near-net-shape processing, and therefore of great application interest.\nE. However, as-cast alloys are rarely used directly because, without post-cast thermomechanical treatments, the solidification product is not susceptible to coarse grains, compositional segregation and particularly unsatisfying precipitates, which tend towards inadequate strength and brittleness.\nF. These ubiquitous disadvantages have, for a long time, seriously hindered practical applications.\nG. However, as-cast alloys are rarely used directly because, without post-cast thermomechanical treatments, the solidification product is susceptible to coarse grains, compositional segregation and particularly unsatisfying precipitates, which tend towards inadequate strength and brittleness.\nH. Here, through screening by using thermodynamic calculations and calorimetric monitoring, we successfully landed non-equiatomic NiCoCrAlTaZrB complex-concentrated alloys (CCAs) to realize in situ micro-segregation-induced size-gradient L1 2 nanoprecipitates increasing from dendritic to interdendritic regions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13170801", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13170801/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-494daaa40edfec51858a", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"501 hours\", \"500 hours\", \"3.7 g/L\", \"2.7 g/L\"]\n\nEvidence:\nHalogenation enhances the stability and function of pharmaceuticals, biomaterials, and industrial compounds. However, chemical halogenation of molecules and peptides can lack stereoselectivity and require the use of toxic chemicals. Although enzymatic halogenation can improve selectivity and reduce environmental impact, current halogenases are inefficient and insoluble, leading to low yields that limit their applications. Here, we develop RebH Evo4 , a soluble and highly active tryptophan halogenase, containing 12 mutations that confer 37-fold and 44-fold increases in 7-chloro- and 7-bromotryptophan production respectively, in vivo. To create RebH Evo4 , we devise an aminoacyl-tRNA synthetase-based halogenase biosensor and conduct over 500 hours of phage-assisted continuous evolution (PACE). Use of RebH Evo4 in a bioreactor results in the production of 2.7 g/L of halogenated tryptophan. When coupled with a downstream enzyme, RebH Evo4 allows 36-fold increased yields of halogenated tryptamines compared to the wild-type enzyme. Additionally, RebH Evo4 enables efficient production of genetically encoded antimicrobial halogenated peptides. The efficient, site-specific halogenation enabled by our evolved halogenase will accelerate sustainable biomanufacturing of halogenated drugs.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13171985", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13171985/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7157df0b4507c702f718", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIndigo, a ubiquitous vat dye in denim manufacturing, is characterized by its exceptionally low aqueous solubility, necessitating chemical reduction to its leuco form using hazardous agents such as sodium dithionite. This conventional process yields sulfite, sulfate, and sulfide byproducts, leading to significant environmental and toxicological concerns. To address these limitations, this study synthesized 1:1 molar ratio inclusion complexes of indigo with β-cyclodextrin (β-CD) and hydroxypropyl-β-cyclodextrin (HP-β-CD) to enhance solubility and stability without chemical intervention. The formation of these complexes was rigorously validated through UV–Vis, FT-IR, PXRD, TEM, and 1 H-NMR spectroscopy. Notably, the aqueous solubility of indigo was enhanced 3.58-fold and 5.55-fold for the β-CD and HP-β-CD complexes, respectively. Furthermore, both complexes demonstrated superior thermal and photostability, with HP-β-CD exhibiting the most pronounced effects. This cyclodextrin-assisted solubilization was successfully extended to naturally extracted Jeju Indigo, underscoring its broad applicability. Our findings suggest that cyclodextrin-based encapsulation offers a sustainable and effective alternative to conventional reduction-oxidation dyeing processes. The online version contains supplementary material available at 10.1186/s40643-026-01065-w. Keywords: Indigo, β‑cyclodextrin, Hydroxypropyl‑β‑cyclodextrin, Inclusion complex, Solubility enhancement, Reducing agent-free dyeing\n\nCandidates:\nA. The formation of these complexes was rigorously validated through UV–Vis, FT-IR, PXRD, TEM, and 1 H-NMR spectroscopy.\nB. To address these limitations, this study synthesized 2:1 molar ratio inclusion complexes of indigo with β-cyclodextrin (β-CD) and hydroxypropyl-β-cyclodextrin (HP-β-CD) to enhance solubility and stability without chemical intervention.\nC. Indigo, a ubiquitous vat dye in denim manufacturing, is not characterized by its exceptionally low aqueous solubility, necessitating chemical reduction to its leuco form using hazardous agents such as sodium dithionite.\nD. Indigo, a ubiquitous vat dye in denim manufacturing, is characterized by its exceptionally low aqueous solubility, necessitating chemical reduction to its leuco form using hazardous agents such as sodium dithionite.\nE. To address these limitations, this study synthesized 1:1 molar ratio inclusion complexes of indigo with β-cyclodextrin (β-CD) and hydroxypropyl-β-cyclodextrin (HP-β-CD) to enhance solubility and stability without chemical intervention.\nF. The formation of these complexes was rigorously validated through UV–Vis, FT-IR, PXRD, TEM, and 2 H-NMR spectroscopy.\nG. The evidence does not state that this conventional process yields sulfite, sulfate, and sulfide byproducts, leading to significant environmental and toxicological concerns.\nH. This conventional process yields sulfite, sulfate, and sulfide byproducts, leading to significant environmental and toxicological concerns.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13172062", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13172062/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9d2511302e2446620183", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\np -Coumaric acid (p-CA) is a key aromatic precursor for the biosynthesis of flavonoids, stilbenoids, and other high-value phenylpropanoids. While microbial production of p-CA typically relies on sugar-based substrates, methanol offers a sustainable and cost-effective alternative, though its use for aromatic biosynthesis remains unexplored. Here, we report the first de novo production of p-CA from methanol using engineered methylotrophic yeast Pichia pastoris . Through heterologous expression of a tyrosine ammonia-lyase and implementing a balanced push–pull strategy in the shikimate pathway using feedback-resistant variants of DAHP synthase ( ARO4 ) and chorismate mutase ( ARO7 ), carbon flux from methanol-derived C3 and C4 precursors was effectively redirected toward aromatic biosynthesis. Shake-flask studies revealed strong gene-dosage-dependent p-CA production, but strains with high-copy numbers suffered metabolic burden under high-density fermentation. Fed-batch bioreactor cultivation demonstrated that a moderate-copy strain achieved the highest titer of 704 ± 6 mg/L, outperforming high-copy variants in robustness and scalability. This study establishes P . pastoris as a promising chassis for methanol-based aromatic production and highlights the critical trade-off between pathway amplification and cellular fitness in C1 biomanufacturing. The online version contains supplementary material available at 10.1186/s40643-026-01068-7.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13172077", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13172077/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-34902982256601453541", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTobacco waste, rich in nicotine, is both an environmental burden and a potential feedstock for high-value chemicals. Here, we developed a proximity-enhanced co-immobilized multi-enzyme cascade that efficiently converts the nicotine into a pharmaceutical intermediate 3-succinoylpyridine (SP). The cascade system comprises the nicotine oxidoreductase NicA2 V321 (NicAm), pseudooxynicotine amine oxidase (Pnao), and 3-succinoylsemialdehyde-pyridine dehydrogenase (Sapd) for sequential nicotine conversion, coupled with an aldehyde–ketone reductase (AKR) module for in situ NADP + regeneration. To improve nicotine-to-SP conversion and facilitate multi-enzyme recycling, a SpyCatcher/SpyTag self-assembled cofactor regeneration enzyme scaffold was adopted, in conjunction with AviTag–BirA-mediated biotinylation for cascade enzyme site-specific co-immobilization on streptavidin-coated supports. This proximity-enhanced design promoted efficient cofactor cycling and boosted the nicotine-to-SP conversion to approximately 63%, much higher than the 42.7% achieved by free enzymes. The co-immobilized system also showed improved pH and thermal stability, retaining over 60% of its initial activity after eight reuse cycles. This modular biocatalytic strategy provides a green and promising route for the valorization of nicotine-rich tobacco waste. The online version contains supplementary material available at 10.1186/s40643-026-01066-9.\n\nCandidates:\nA. Tobacco waste, rich in nicotine, is both an environmental burden and a potential feedstock for high-value chemicals.\nB. Tobacco waste, rich in nicotine, is not both an environmental burden and a potential feedstock for high-value chemicals.\nC. The cascade system comprises the nicotine oxidoreductase NicA3 V321 (NicAm), pseudooxynicotine amine oxidase (Pnao), and 3-succinoylsemialdehyde-pyridine dehydrogenase (Sapd) for sequential nicotine conversion, coupled with an aldehyde–ketone reductase (AKR) module for in situ NADP + regeneration.\nD. To improve nicotine-to-SP conversion and facilitate multi-enzyme recycling, a SpyCatcher/SpyTag self-assembled cofactor regeneration enzyme scaffold was adopted, in conjunction with AviTag–BirA-mediated biotinylation for cascade enzyme site-specific co-immobilization on streptavidin-coated supports.\nE. Here, we developed a proximity-enhanced co-immobilized multi-enzyme cascade that efficiently converts the nicotine into a pharmaceutical intermediate 3-succinoylpyridine (SP).\nF. To improve nicotine-to-SP conversion and facilitate multi-enzyme recycling, a SpyCatcher/SpyTag self-assembled cofactor regeneration enzyme scaffold was not adopted, in conjunction with AviTag–BirA-mediated biotinylation for cascade enzyme site-specific co-immobilization on streptavidin-coated supports.\nG. Here, we developed a proximity-enhanced co-immobilized multi-enzyme cascade that efficiently converts the nicotine into a pharmaceutical intermediate 4-succinoylpyridine (SP).\nH. The cascade system comprises the nicotine oxidoreductase NicA2 V321 (NicAm), pseudooxynicotine amine oxidase (Pnao), and 3-succinoylsemialdehyde-pyridine dehydrogenase (Sapd) for sequential nicotine conversion, coupled with an aldehyde–ketone reductase (AKR) module for in situ NADP + regeneration.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13172129", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13172129/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2dcb7d32686f7a231785", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"120 °C\", \"35%\", \"10 h\", \"36%\", \"2.75%\", \"11 h\", \"1.75%\", \"121 °C\"]\n\nEvidence:\nThis study assesses the impact of physicochemical pretreatment, enzymatic hydrolysis, and co-culture fermentation strategies on bioethanol production from corn husk biomass (CHB). Under optimal alkali pretreatment conditions (1.75% alkali, 4.0 g substrate concentration, 120 °C, 10 h), 35% lignin removal was achieved, with 48% cellulose and 39% hemicellulose recovery. In contrast, acid pretreatment resulted in 30% lignin removal, 45% cellulose recovery, and 34% hemicellulose recovery, showing lower efficiency than alkali pretreatment. During ultrasonication alkali pretreatment enhanced cellulose and hemicellulose exposure up to 51 and 46% and delignification up to 49%. Enzymatic hydrolysis of pretreated corn husk biomass was performed using commercial enzymes [Celluclast 1.5 L (700 EGU or 854 U mL −1 ) and Viscozyme (13.4 FBG/mL)] and isolated bacterial enzymes, including cellulase from Bacillus licheniformis (9.3 ± 0.3 U mL −1 ) and xylanase from Enterobacter asburiae PQ396173 (7.0 ± 0.4 U mL −1 ). The developed enzyme cocktail in ratio 3:2:3:1 (v/v; U mL −1 ) (Celluclast: Viscozyme: native cellulase: native xylanse) using a cocktail of native and commercial enzymes, yielded total reducing sugar of 740 mg g −1 glucose and 54.6 mg g −1 xylose. Fermentation of hydrolysate prepared with commercial enzymes using monoculture of Saccharomyces cerevisiae and Pichia pastoris yielded 17.6 g L −1 and 12.2 g L −1 bioethanol separately. Co-cultured yeasts produced 26.8 g L −1 ethanol at 96 h of incubation, exceeding monoculture yields. The fermentation with integration of commercial and isolated bacterial enzyme cocktails yielded the highest bioethanol output of 37.3 g L −1 at 96 h incubation, indicating that enzymatic saccharification with a combination of commercial and native enzyme cocktails results in maximum bioethanol production. The online version contains supplementary material available at 10.1186/s40643-026-01062-z. Keywords: Corn husk biomass, Ultrasonication pretreatment, Enzymatic hydrolysis, Enzyme cocktail, Fermentation, Bioethanol", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13172165", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13172165/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e174bb944031180120a3", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nBrazzein is a heat-stable sweet protein with strong potential as a sugar alternative. Here, we leveraged precision fermentation to produce high titers of recombinant brazzein in the yeast Komagataella phaffii . As the brazzein expression cassette included a zeocin resistance marker, we investigated whether high zeocin resistance could serve as a predictive indicator of high brazzein production. A microscale screen across 100–1000 µg mL −1 zeocin rapidly identified clones with varying resistance, including the high-resistant Braz_3, which produced 4.5-fold more brazzein than the moderate-resistant Braz_7 in fed-batch bioreactors (61.5 vs. 13.7 mg L −1 ). Long-read genome sequencing revealed that five copies of the expression cassette were integrated into the genome of the high-producing strain compared to a single copy in the moderate-producing strain. Consequently, our results suggest a positive correlation between zeocin resistance and brazzein titers, demonstrating that antibiotic resistance phenotyping can guide strain selection for scalable production of functional food proteins. The online version contains supplementary material available at 10.1007/s10068-026-02150-8.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13172243", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13172243/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4a86c8ac3b5dd37d7e41", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIncreasing the production of structured cultivated meat (CM) remains a major challenge. Many current 3D printing and bioprinting technologies lack the necessary throughput, material compatibility, and cytocompatibility to produce realistic meat alternatives. In this study, screen printing, a traditional high-throughput printing technology, was investigated for the first time for the application of meat alternatives. The new process, 3D bio-screen printing (3D-BSP), presents a biomanufacturing process that uses edible materials (e.g., plant proteins) to produce meat-like structures with high resolution (0.1 mm). The suitability of edible inks for the 3D-BSP process was investigated on the basis of soy protein isolate (SPI) rheologically (flow index value < 0.4) and in terms of printability. In order to increase the protein content in edible inks while maintaining processability, an approach based on reducing agents was investigated. Sodium sulphite served as a demonstration model and resulted in a high protein content (>20 wt%) in SPI, while maintaining the flow properties suitable for processing protein-rich inks. To test the suitability as scaffolding technology for hybrid cultivated meat, meat-like scaffold structures were printed with C2C12 myoblasts differentiated on them. The printability of the structures was high in the resolution range investigated from 0.1 mm to 1 mm and supported 2D and 3D myoblast cultures (64% actin coverage and myotube formation). Ultimately, a marbled prototype was produced whose thickness could be increased by a stacking approach (>0.5cm). The texture profile (e.g., chewiness) of stacked and printed scaffolds was comparable to that of conventional meat. Upon successful transfer of the process parameters determined here to industrial screen printing machines, production rates of >100 kg/h could be achieved with one machine in the future. 3D-BSP offers a practical and cost-effective perspective for the mass production of structured meat in industrial quantities using screen printing technology. It could remove technical and economic barriers in this area and bring us closer to the commercial production of high-quality meat alternatives.\n\nCandidates:\nA. The evidence does not state that increasing the production of structured cultivated meat (CM) remains a major challenge.\nB. In this study, screen printing, a traditional high-throughput printing technology, was not investigated for the first time for the application of meat alternatives.\nC. The new process, 4D bio-screen printing (3D-BSP), presents a biomanufacturing process that uses edible materials (e.g., plant proteins) to produce meat-like structures with high resolution (0.1 mm).\nD. The new process, 3D bio-screen printing (3D-BSP), presents a biomanufacturing process that uses edible materials (e.g., plant proteins) to produce meat-like structures with high resolution (0.1 mm).\nE. Increasing the production of structured cultivated meat (CM) remains a major challenge.\nF. Many current 3D printing and bioprinting technologies lack the necessary throughput, material compatibility, and cytocompatibility to produce realistic meat alternatives.\nG. In this study, screen printing, a traditional high-throughput printing technology, was investigated for the first time for the application of meat alternatives.\nH. Many current 4D printing and bioprinting technologies lack the necessary throughput, material compatibility, and cytocompatibility to produce realistic meat alternatives.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13172316", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13172316/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-75e95856f99b99e1bb14", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nHuman induced pluripotent stem cells (iPSCs) are transforming adoptive cell therapy by combining unlimited self-renewal, broad differentiation potential, and high amenability to genome engineering. These attributes make iPSCs a versatile source for the development of standardized immune effector cells at industrial scale, enabling a shift from patient- or donor-restricted cell products toward true off-the-shelf immunotherapies that can be improved through iterative genome engineering. iPSC-derived natural killer (iNK) cells are the most clinically advanced and exemplify the platform’s advantages over conventional autologous or donor-sourced approaches. Unlike autologous therapies, which require labor-intensive and expensive personalized clinical-grade manufacturing, and are constrained by variable quality and genetic intractability of donor products, iPSC technology supports the creation of renewable, clonally defined master cell banks as uniform starting material for NK-cell therapy products. Advances in CRISPR/Cas-based editing now permit multiplex introduction of functional traits, enhanced cytokine signaling, antibody-dependent cytotoxicity, checkpoint resistance, optimized trafficking, safety switches, and increasing signal complexity, directly at the pluripotent or progenitor stages; ultimately allowing for fully-programmable iNK cells with customizable potency and persistence. Early clinical studies of iNK products validate the feasibility, safety, and therapeutic potential of this approach, but also underscore the need for continued refinement of differentiation protocols, manufacturing pipelines, and regulatory standards to ensure efficacy, genomic stability, phenotypic maturity, and long-term safety. This review outlines current breakthroughs and future directions of iNK cell therapies, emphasizing how programmable iPSC chassis platforms are enabling modular and off-the-shelf targeted immunotherapies.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13176210", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13176210/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6ffe99167d37b16096e5", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"30 °C\", \"31 °C\", \"5.4%\", \"4.4%\"]\n\nEvidence:\nTo achieve high-value utilization of brewer’s spent grains and to produce protein-rich food ingredients, brewer’s spent grains were upcycled by a liquid fungal fermentation and the fermentation conditions were optimized. Brewer’s spent grains (BSG) represent the most abundant by-product of the brewing industry. Although BSG are food-grade, their direct use in food is limited because of sensory changes of the products. In this study, screening experiments revealed that black beer spent grains could be efficiently upcycled by submerged fermentation with the edible fungus Pleurotus ostreatus . The conditions of the fermentation of BSG with P. ostreatus were optimized using response surface methodology, including the parameters substrate concentration, inoculum volume, initial pH value, and temperature. As no separation between BSG and mycelium was possible after the fermentation, ergosterol was used as a biomarker to determine the fungal growth. As optimum conditions, a BSG concentration of 17 g L − 1 dry matter, an inoculum volume of 4.4% (v/v), an initial pH of 9.3 and a temperature of 30 °C were identified. The fermentation with P. ostreatus increased the true protein content of the biomass compared to the spent grains and reduced the total fat content. The biological value was increased from 88 to 94 (reference standard is whole chicken egg with a biological value of 100). Tryptophan and lysine were limiting in the non-fermented spent grains, while after fermentation, the chemical score of lysine increased from 78 to 92, and tryptophan wasn’t limiting anymore. Fermentation by P. ostreatus enhanced the nutritional value of BSG, and the fermentation conditions were optimized by using response surface methodology. The online version contains supplementary material available at 10.1186/s40643-026-01018-3.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13176399", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13176399/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-868abdc48d2d575e078d", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"98%\", \"94%\", \"99%\", \"93%\"]\n\nEvidence:\nUltrafiltration and diafiltration (UF/DF) operations have been demonstrated to clear leachables from drug substance, however there is limited data available. Consequently, comprehensive and systematic characterization of leachables clearance during UF/DF is required and essential. To achieve this, the reduction capacity for 28 selected organic compounds spiked into 3 different proteins during UF/DF processes was investigated using liquid chromatography high-resolution mass spectrometry. Selection of compounds was based on their presence in representative biomanufacturing processes. Most compounds (24) showed clearance over 98% across the process for the 3 protein materials. The specific protein characteristics and process parameters for each protein had a minimal impact on clearance, with sieving coefficients essentially the same for each one of the 3 protein processes. The sieving coefficient is a parameter that characterizes clearance of compounds during UF/DF. Physicochemical properties of the compounds under study significantly influenced their clearance, with the octanol–water coefficient (Log P) being the most crucial factor. Compounds with Log P < 4 had sieving coefficients close to ideal clearance, and compounds with Log P > 7 showed lower but still significant clearance (> 93%). Other important parameters were established to be molecular weight, polarizability and solvent accessible surface area. Modelling tools based on Orthogonal Partial Least Squares (OPLS) regression were created to predict sieving coefficients. The present work has created a strong background to describe the ability of UF/DF to remove potential organic leachables. Application of these modelling approaches becomes critical to support product safety assessments. Demonstration of significant removal along UF/DF operations confirms risk reduction of leachables coming mostly from upstream stages. The online version contains supplementary material available at 10.1007/s11095-026-04050-2. Keywords: Liquid chromatography-mass spectrometry, Process equipment related leachables, Regression models, Ultrafiltration/diafiltration, Vortex-assisted liquid–liquid microextraction", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13179207", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13179207/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-509409f1bfb6d68f8800", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPolyethylene terephthalate (PET) plastic waste causes serious environmental pollution due to insufficient recycling rates. Enzymatic PET depolymerization offers a sustainable recycling strategy, but limited stability and activity of current PET-degrading enzymes restrict practical implementation. Here, we engineer Polyester Hydrolase Leipzig 7 (PHL7), a PET hydrolase from a compost metagenome, to enhance its stability and catalytic performance under recycling-relevant conditions. Using Rosetta PROSS-based computational design combined with rational mutagenesis, we introduce up to 24 mutations, generating variants with melting temperatures of 88-95 °C and over 110-fold higher activity in 0.1 M phosphate buffer compared to the parent enzyme. Benchmarking shows that the best variants (R4M6, R4M9, and R4M10) match or exceed the performance of established engineered PET hydrolases, including ICCG and LCC-A2, and approach that of TurboPETase across multiple conditions. Under high substrate loadings, the PHL7-R4 variants degrade 75-78% of 10% (w/w) PET within 24 h at 65 °C, outperforming ICCG, while an optimized variant R4M10-H185Y achieves up to 84% degradation of 20% (w/w) PET. X-ray structure determination and molecular dynamics simulations reveal key stabilizing and activity enhancing mechanisms. These engineered PHL7 variants represent robust biocatalysts for scalable enzymatic PET recycling.\n\nCandidates:\nA. Here, we engineer Polyester Hydrolase Leipzig 8 (PHL7), a PET hydrolase from a compost metagenome, to enhance its stability and catalytic performance under recycling-relevant conditions.\nB. Polyethylene terephthalate (PET) plastic waste causes serious environmental pollution due to insufficient recycling rates.\nC. The evidence does not state that polyethylene terephthalate (PET) plastic waste causes serious environmental pollution due to insufficient recycling rates.\nD. Enzymatic PET depolymerization offers a sustainable recycling strategy, but limited stability and activity of current PET-degrading enzymes restrict practical implementation.\nE. Using Rosetta PROSS-based computational design combined with rational mutagenesis, we introduce up to 25 mutations, generating variants with melting temperatures of 88-95 °C and over 110-fold higher activity in 0.1 M phosphate buffer compared to the parent enzyme.\nF. Here, we engineer Polyester Hydrolase Leipzig 7 (PHL7), a PET hydrolase from a compost metagenome, to enhance its stability and catalytic performance under recycling-relevant conditions.\nG. Using Rosetta PROSS-based computational design combined with rational mutagenesis, we introduce up to 24 mutations, generating variants with melting temperatures of 88-95 °C and over 110-fold higher activity in 0.1 M phosphate buffer compared to the parent enzyme.\nH. The evidence does not state that enzymatic PET depolymerization offers a sustainable recycling strategy, but limited stability and activity of current PET-degrading enzymes restrict practical implementation.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13179365", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13179365/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4cc179924146ed5dd7f4", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"11%\", \"51%\", \"50%\", \"10%\"]\n\nEvidence:\nWhen applied for industrial-scale bioproduction, cells are subjected to ever-changing cultivation environments due to bioreactor heterogeneities, which can significantly influence their growth and production behavior. Cultivating and comparing cells and strains under conditions representative of an actual bioprocess instead of laboratory environments is therefore necessary for developing more reliable production strains. Scale-down bioreactors provide an experimental approach for this endeavor, yet they are limited in their temporal resolution and flexibility when it comes to environmental dynamics. Thus, the impact of second-scale differences in dynamic environments on microbial producers remains unexplored. This study uses the advantages of microfluidic single-cell cultivation to compare the growth behavior and intracellular parameters of three yeast strains under dynamic cultivation conditions with alternating phases of glucose excess and limitation at the timescale of seconds aiming to uncover strain-specific performance under as well as adaptation to fluctuating, bioprocess-relevant environments. Across all three strains, a consistent trend was observed: decreasing glucose availability resulted in reduced growth rates, lower ATP levels, diminished glycolytic flux, and smaller cell sizes. Notably, cells already reached approximately 50% of their maximal growth rate when exposed to growth-promoting glucose conditions for only 10% of the time. Importantly, both growth rate and cell size exhibited an adaptation phase rather than an immediate response to oscillatory glucose supply. Longer exposure to favorable conditions shortened the adaptation phase and resulted in higher adapted growth rates and larger cell sizes. By leveraging microfluidic single-cell cultivation, this study provides unprecedented temporal resolution of environmental dynamics, enabling direct, strain-specific comparison of cellular responses and adaptation to rapidly changing cultivation environments. Seconds do make a difference. Our findings demonstrate that growth rate is a highly conserved trait under environmental perturbations, which points to an intrinsic robustness of the investigated strains and highlights adaptation as a key determinant of performance in dynamic environments. Together, these insights have important implications for the design of future single-cell experiments and for the development of robust bioprocesses operating under dynamic conditions. The online version contains supplementary material available at 10.1186/s12934-026-03029-3. Keywords: Dynamic microfluidic single-cell cultivation, Strain comparison, Bioprocess development, Saccharomyces cerevisiae, Biosensors, Scale-down, Dynamic environment", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13179603", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13179603/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-202cd3e3fbe618470e98", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nCarboxylic acids are key platform chemicals that can be biologically produced in mixed-culture fermentations. In these systems, product formation is determined by the microbial community composition, which is shaped by operational conditions. A general trend has been observed linking substrate availability to the production of either lactic acid or volatile fatty acids, but it remains unclear at which substrate concentration this shift occurs. This study investigates the effect of carbohydrate concentrations (from 0 to 670 mg COD·L −1 ) and type (hexoses and pentoses) on product selectivity and microbial community composition in thermophilic mixed-culture fermentations. Fermentation experiments were conducted in thermophilic reactors (55 °C, pH 5.3, HRT 4 days), fed either with glucose or xylose. Higher substrate concentrations (650–670 mg COD·L −1 ) favored lactic acid production, accounting for 60–76% of the soluble products. Lower carbohydrate concentrations reduced lactic acid production and increased volatile fatty acid concentrations. Volatile fatty acids became the main product under low substrate concentrations (continuous operation; 0 mg COD·L −1 ), with a selectivity of 83–97%. Despite this general trend, the substrate concentration at which the shift from lactic acid to volatile fatty acids occurred depended on the carbohydrate type. Microbial community analyses revealed Thermoanaerobacterium as the dominant genus in all reactors, with genera within the Bacillaceae family (putatively involved in lactic acid production) increasing in relative abundance under high carbohydrate concentrations. This suggests that the product shift resulted from both a metabolic shift within the dominant species and a change in the microbial community composition. Low carbohydrate concentrations favor VFA production. High carbohydrate concentrations promote lactic acid formation. Carbohydrate type influences the shift from lactic acid to VFAs. Low carbohydrate concentrations favor VFA production. High carbohydrate concentrations promote lactic acid formation. Carbohydrate type influences the shift from lactic acid to VFAs. The online version contains supplementary material available at 10.1007/s00253-026-13806-0.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13180770", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13180770/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-49be6123638424bdaea1", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nSurfactin production by Bacillus subtilis is typically performed under aerobic conditions, requiring high aeration and agitation, which leads to mechanical stress for the cells and therefore promotes excessive foaming. As a process-oriented alternative to full aerobic operation, an aerobic to micro-aerobic switching strategy was developed that aims to decouple biomass formation from surfactin synthesis by controlling oxygen availability. Promoter activities relevant to nitrate respiration and surfactin biosynthesis were first characterised in shake flasks using transcriptional reporter strains and online monitoring of dissolved oxygen. The nitrite reductase promoter showed the strongest induction under oxygen-limited conditions and was used to construct an oxygen-responsive production strain in which the native P srfA promoter was replaced to suppress surfactin formation during aerobic growth and shift production to the micro-aerobic phase. The switching concept was subsequently evaluated in 30-L stirred-tank bioreactor cultivations using stepwise reductions of dissolved oxygen setpoints combined with exponential glucose feeding and nitrate supplementation. The engineered strain B. subtilis MG19 enabled stable transitions into micro-aerobic operation without apparent loss of biomass, while a reference strain B. subtilis MG17 with the native regulation showed surfactin formation already during the aerobic phase and pronounced process disturbance after switching, characterised by glucose accumulation and a strong decrease in surfactin concentration. Overall, the study demonstrates oxygen switching as a scalable process engineering tool for controlling surfactin production phases and highlights key constraints for robust micro-aerobic bioreactor operation. • Oxygen availability decoupled growth and surfactin formation. • Micro-aerobic process control was evaluated in 30-L bioreactor cultivations. • The engineered strain shifted surfactin production to oxygen-limited conditions. The online version contains supplementary material available at 10.1007/s00253-026-13868-0. Keywords: Lipopeptide production, Oxygen-limited cultivation, Fed-batch fermentation, Bioreactor scale-up, Process kinetics, Nitrogen metabolism", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13183733", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13183733/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-01de8ef88cc2717fda11", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAs the technological advancements of the early 21st century are pushing industrial biotechnology (IB) into the realm of Big Data–driven innovation, the requirement for trustworthy data management, annotation, and standardization is emerging as a necessity. Minimum information models (MIMs) have long been used across disciplines as the backbone of good data management practices by providing the scaffold upon which standardized recording of metadata can adequately and succinctly describe an understudied phenomenon. Here we present a minimum set of metadata, named the minimum information for fermentation experiments (MIFE) and devices (MIFD), that has been specifically designed to accommodate the data management and annotation needs of IB-related fermentation experiments. Although the proposed schema is tailored to IB applications, MIFE and MIFD build upon well-established models and community standards to facilitate easier integration with existing infrastructure and easier adoption by the community, as well as aim to integrate Findable, Accessible, Interoperable, and Reproducible (FAIR) principles in the IB field. In addition, the integration with FAIR Data Station (FAIR DS), a tool that offers metadata validation and enables the automated uptake of (meta)data from data management repositories such as FAIRDOM-SEEK, is showcased. The proposed models are accompanied by a Python package that enables their programmatic use by creating a Linked Data Modeling Language (LinkML) schema that can fuel subsequent analyses. Through the promotion and simplification of knowledge discovery, we believe that MIFE and MIFD can accelerate the application of state-of-the-art artificial intelligence (AI) methods and the adoption of explainable AI to better understand bioprocesses at scale.\n\nCandidates:\nA. Minimum information models (MIMs) have long been used across disciplines as the backbone of good data management practices by providing the scaffold upon which standardized recording of metadata cannot adequately and succinctly describe an understudied phenomenon.\nB. The evidence does not state that here we present a minimum set of metadata, named the minimum information for fermentation experiments (MIFE) and devices (MIFD), that has been specifically designed to accommodate the data management and annotation needs of IB-related fermentation experiments.\nC. Although the proposed schema is not tailored to IB applications, MIFE and MIFD build upon well-established models and community standards to facilitate easier integration with existing infrastructure and easier adoption by the community, as well as aim to integrate Findable, Accessible, Interoperable, and Reproducible (FAIR) principles in the IB field.\nD. As the technological advancements of the early 21st century are pushing industrial biotechnology (IB) into the realm of Big Data–driven innovation, the requirement for trustworthy data management, annotation, and standardization is emerging as a necessity.\nE. Although the proposed schema is tailored to IB applications, MIFE and MIFD build upon well-established models and community standards to facilitate easier integration with existing infrastructure and easier adoption by the community, as well as aim to integrate Findable, Accessible, Interoperable, and Reproducible (FAIR) principles in the IB field.\nF. Minimum information models (MIMs) have long been used across disciplines as the backbone of good data management practices by providing the scaffold upon which standardized recording of metadata can adequately and succinctly describe an understudied phenomenon.\nG. As the technological advancements of the early 22st century are pushing industrial biotechnology (IB) into the realm of Big Data–driven innovation, the requirement for trustworthy data management, annotation, and standardization is emerging as a necessity.\nH. Here we present a minimum set of metadata, named the minimum information for fermentation experiments (MIFE) and devices (MIFD), that has been specifically designed to accommodate the data management and annotation needs of IB-related fermentation experiments.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13184968", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13184968/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-028c07435b14fc36e8cf", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"50 °C\", \"77 h\", \"1386 h\", \"51 °C\", \"76 h\", \"1387 h\"]\n\nEvidence:\nCarbocyclic nicotinamide cofactors are attractive NAD(P) + analogues that retain native redox activity while offering substantially enhanced chemical and thermal stability. Their broader use, however, has been limited by demanding multistep chemical syntheses involving complex protection strategies and limited exploration of enzymatic alternatives. In this study, we studied both chemical and enzymatic approaches for the synthesis of carba-analogues. For the enzymatic route, we identify and characterize the key enzymes involved in cofactor assembly, evaluating their tolerance toward non-native, carbocyclic substrates and extending the assembly to further generate the phosphorylated analogue, cNADP + . In addition, extending the analysis to cofactor thermostability, carba-NAD + displayed a remarkable halftime ( t 1/2 > 1386 h at 50 °C), far exceeding that of NAD + ( t 1/2 = 76 h) and is accepted by a broad panel of oxidoreductases. Collectively, this work outlines a modular workflow and details the synthesis landscape for accessing thermostable, synthetic nicotinamide cofactor analogues, cNAD + and cNADP + , unveiling new opportunities for their application in cofactor engineering and synthetic biocatalysis.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13185757", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13185757/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-338933cf42bca5163d0b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGeological carbon sequestration (GCS) is a key option for climate change mitigation, and subsurface microorganisms can alter CO 2 behavior through biomethanation, bioliquefaction, and biomineralization. This review summarizes the major microbial processes involved in GCS and evaluates their effects on carbon stabilization, resource reutilization, and storage risk. We compare microbial distribution and metabolic functions across representative geological reservoirs and synthesize laboratory, numerical, and field approaches into a multi-scale framework for studying microbially mediated GCS. We also discuss engineering regulation strategies, site selection, monitoring, and risk control, together with current technical challenges and future research priorities. Overall, this review provides an integrated perspective on microbial mechanisms and practical guidance for safer and more effective GCS deployment. Subject areas: Earth sciences, Biogeochemistry, Biogeoscience, Global carbon cycle, Microbiology, Applied microbiology\n\nCandidates:\nA. Geological carbon sequestration (GCS) is a key option for climate change mitigation, and subsurface microorganisms can alter CO 2 behavior through biomethanation, bioliquefaction, and biomineralization.\nB. Geological carbon sequestration (GCS) is a key option for climate change mitigation, and subsurface microorganisms can alter CO 3 behavior through biomethanation, bioliquefaction, and biomineralization.\nC. The evidence does not state that this review summarizes the major microbial processes involved in GCS and evaluates their effects on carbon stabilization, resource reutilization, and storage risk.\nD. We compare microbial distribution and metabolic functions across representative geological reservoirs and synthesize laboratory, numerical, and field approaches into a multi-scale framework for studying microbially mediated GCS.\nE. The evidence does not state that we compare microbial distribution and metabolic functions across representative geological reservoirs and synthesize laboratory, numerical, and field approaches into a multi-scale framework for studying microbially mediated GCS.\nF. We also discuss engineering regulation strategies, site selection, monitoring, and risk control, together with current technical challenges and future research priorities.\nG. The evidence does not state that we also discuss engineering regulation strategies, site selection, monitoring, and risk control, together with current technical challenges and future research priorities.\nH. This review summarizes the major microbial processes involved in GCS and evaluates their effects on carbon stabilization, resource reutilization, and storage risk.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13186083", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13186083/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bee88dbfd5a35d78006b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe development of microbial biocomposites represents a promising frontier in the design of sustainable, multifunctional, and environmentally friendly materials. This minireview synthesises recent advances (2020–2026) in the production, modification, and application of biocomposites derived from microorganisms, with a focus on bacterial cellulose (BC) and fungal mycelium. It explores their synergies with emerging approaches in synthetic biology, 3D printing, and mineral functionalisation. Key microbial systems such as Komagataeibacter spp. and Ganoderma spp. are discussed, alongside structural and functional engineering strategies including in situ hydroxyapatite mineralisation, incorporation of plant fibres, and the addition of functional nanomaterials such as graphene oxide. The review further highlights the integration of these materials into high-value applications, including osteogenic scaffolds, self-healing living materials, biodegradable packaging, and environmental remediation systems. Finally, it addresses current regulatory and technical challenges related to industrial scalability, functional stability, and batch-to-batch standardisation. This article aims to provide a critical and comprehensive perspective for researchers and professionals in applied microbiology, materials science, and industrial biotechnology, emphasising the potential of microbial biocomposites as a convergent platform at the interface of sustainability, functional innovation, and bioinspired design.\n\nCandidates:\nA. It explores their synergies with emerging approaches in synthetic biology, 4D printing, and mineral functionalisation.\nB. It explores their synergies with emerging approaches in synthetic biology, 3D printing, and mineral functionalisation.\nC. The development of microbial biocomposites represents a promising frontier in the design of sustainable, multifunctional, and environmentally friendly materials.\nD. Key microbial systems such as Komagataeibacter spp.\nE. This minireview synthesises recent advances (2020–2026) in the production, modification, and application of biocomposites derived from microorganisms, with a focus on bacterial cellulose (BC) and fungal mycelium.\nF. The evidence does not state that the development of microbial biocomposites represents a promising frontier in the design of sustainable, multifunctional, and environmentally friendly materials.\nG. The evidence does not state that key microbial systems such as Komagataeibacter spp.\nH. This minireview synthesises recent advances (2021–2026) in the production, modification, and application of biocomposites derived from microorganisms, with a focus on bacterial cellulose (BC) and fungal mycelium.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13186926", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13186926/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b6938d258e0a3734366c", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nAspergillus welwitschiae strain SVUAw9 was isolated from sugarcane bagasse and identified by the sequencing of the calmodulin (CaM) gene, was found to exhibit substantial cold-active xylanase activity in this study. Under solid-state fermentation conditions, the strain can utilize a range of agro-industrial residues as substrates to produce cold-active xylanase. After 14 days at pH 7 and 10 °C using ammonium chloride as the nitrogen source, sugarcane bagasse was the most productive substrate, generating 106.93 ± 12.1 U/gram dry substrate (gds), followed by bean straw, corn cob, and date palm leaves which produced 57.82 ± 8.27, 57.75 ± 7.44, and 38.87 ± 6.15 U/gds, respectively. Conversely, the substrate exhibiting the lowest production was rice husk, producing 29.32 ± 4.88 U/gds. Using Trilite MC 08 and Sephacryl S-200 columns, the xylanase was purified 75.87 times, yielding a single band at approximately 71 kDa. At pH 4.0 and 30 °C, the maximum activity of 156.46 ± 12 U/mg was achieved. Co(NO₃)₂, MnSO₄, and NiSO₄ markedly enhanced enzyme activity, resulting in residual activity increases of 152.94 ± 11.54%, 152.94 ± 9.58%, and 134.12 ± ठर 7.66%, respectively, with specific activities of 239.3 ± 18, 239.3 ± 15, and 209.84 ± 12 U/mg. Km and Vmax for the pure xylanase were determined as 0.1 ± 0.005 mg/mL and 144.9 ± 7.14 µmol/min, respectively. The purified xylanase could degrade oat spelt xylan, corn cob xylan, xylan, Birchwood xylan, maize stalk xylan, carboxymethyl cellulose (CMC), beechwood and microcrystalline cellulose (MCC), resulting in activities of 156 ± 12, 108 ± 7, 82 ± 6, 79 ± 5, 62 ± 4, 22 ± 1.5, and 38 ± 3 U/mg, respectively. Keywords: Cold-adapted, Bagasse , Enzymes, Fungi, Lignocellulose, Plant-biomass-derived waste, Xylanase", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13188304", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13188304/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5791863607b3b2490930", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe chemical and biochemical engineering, microbiology, and biotechnology communities deeply mourn the passing of Dr. Robert M. Kelly, the Alcoa Professor of Chemical and Biomolecular Engineering and Director of the Biotechnology Program at NC State University. A towering pioneer in the biology of extremophiles, an esteemed educator and mentor, and a dedicated editor for Applied and Environmental Microbiology (AEM), Bob leaves behind an extraordinary legacy that reshaped our understanding of life at high temperatures.\n\nCandidates:\nA. The chemical and biochemical engineering, microbiology, and biotechnology communities deeply mourn the passing of Dr.\nB. The evidence does not state that kelly, the Alcoa Professor of Chemical and Biomolecular Engineering and Director of the Biotechnology Program at NC State University.\nC. A towering pioneer in the biology of extremophiles, an esteemed educator and mentor, and a dedicated editor for Applied and Environmental Microbiology (AEM), Bob leaves behind an extraordinary legacy that reshaped our understanding of life at high temperatures.\nD. The evidence does not state that a towering pioneer in the biology of extremophiles, an esteemed educator and mentor, and a dedicated editor for Applied and Environmental Microbiology (AEM), Bob leaves behind an extraordinary legacy that reshaped our understanding of life at high temperatures.\nE. Kelly, the Alcoa Professor of Chemical and Biomolecular Engineering and Director of the Biotechnology Program at NC State University.\nF. The evidence does not state that the chemical and biochemical engineering, microbiology, and biotechnology communities deeply mourn the passing of Dr.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13188926", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13188926/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-635867cbb55017ef77a3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIndustrial production of malic acid remains dependent on fossil resources or, when performed microbiologically, on sugar-based feedstocks. Both routes come with caveats, generating emissions and competing with food supply. The use of CO₂-derived one-carbon substrates offers a promising alternative to circumvent these constraints. In this study, the malic acid production process from methanol in metabolically engineered Ogataea polymorpha NYCY495 LEU-ΔSTE12 Pyc Mdh MAE1 strain was optimized and scaled up. A two-phase cultivation strategy, using glycerol for biomass formation and methanol for product synthesis, was established in shake flasks and subsequently transferred to a 1 L bioreactor. Process optimization through automated feeding strategies was evaluated. DO-based feeding was the most effective approach, using a combination of methanol and glycerol, achieving a final molar yield of 0.1 mol MA mol MeOH ⁻¹ and a maximum productivity of 0.5 g l⁻¹ h⁻¹. This successful fermentation strategy was validated using green methanol, showcasing the feasibility of “closing the loop” as envisioned in the bioeconomy. Finally, a comparative study of the effect of glycerol, methanol, and their mixture on O. polymorpha NYCY495 LEU-ΔSTE12 Pyc Mdh MAE1 methanol metabolism, peroxisome biogenesis, and cellular redox balance is presented, supporting the positive cumulative effect of both on gene transcription. Keywords: C1 metabolism, methylotrophic yeast, bioprocess optimization, cofeeding, green methanol, transcriptomics\n\nCandidates:\nA. The use of CO₂-derived one-carbon substrates offers a promising alternative to circumvent these constraints.\nB. Industrial production of malic acid remains dependent on fossil resources or, when performed microbiologically, on sugar-based feedstocks.\nC. The evidence does not state that the use of CO₂-derived one-carbon substrates offers a promising alternative to circumvent these constraints.\nD. The evidence does not state that both routes come with caveats, generating emissions and competing with food supply.\nE. In this study, the malic acid production process from methanol in metabolically engineered Ogataea polymorpha NYCY495 LEU-ΔSTE12 Pyc Mdh MAE1 strain was optimized and scaled up.\nF. The evidence does not state that industrial production of malic acid remains dependent on fossil resources or, when performed microbiologically, on sugar-based feedstocks.\nG. In this study, the malic acid production process from methanol in metabolically engineered Ogataea polymorpha NYCY496 LEU-ΔSTE12 Pyc Mdh MAE1 strain was optimized and scaled up.\nH. Both routes come with caveats, generating emissions and competing with food supply.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13189001", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13189001/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e0ad017f77c5d8c32363", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nFormaldehyde-based fixatives, particularly neutral-buffered formalin (NBF), are widely used in histology owing to their strong protein cross-linking capacity and reliable preservation of cytoarchitecture. However, formaldehyde is toxic and carcinogenic, motivating the search for safer alternatives. Here, we systematically evaluated lactic acid (LA) as a potential formaldehyde-free fixative for murine brain tissues. Brains were fixed either by immersion or by transcardial perfusion followed by immersion using LA at varying concentrations, pH-adjusted LA, or phosphate-buffered saline (PBS), and were compared with NBF. Histomorphological preservation was assessed using qualitative evaluation, semi-quantitative scoring, and quantitative crack-area measurements. Across all conditions, NBF provided the best overall tissue preservation. LA fixation showed clear concentration-dependent effects: low concentrations resulted in poor structural integrity, whereas higher concentrations (LA10–LA20) achieved moderate preservation of cytoarchitecture, reduced cracking, and acceptable cellular morphology. White-matter integrity remained inferior to NBF, consistent with the lipid-rich composition of myelin and the predominantly protein-denaturing, non-cross-linking fixation properties of LA. pH-adjustment experiments indicated that fixation efficacy partially depended on acidity. Notably, LA20 delivered by transcardial perfusion substantially improved tissue preservation compared with immersion alone and outperformed PBS. Although LA cannot fully replace NBF, high-concentration LA might represent an alternative for selected neurohistological applications. The online version contains supplementary material available at 10.1038/s41598-026-51513-y. Keywords: Histology, Fixation, Mouse brain, Neutral buffered formaldehyde, Paraffin sections, Tissue preservation Subject terms: Biological techniques, Medical research, Neurology, Neuroscience\n\nCandidates:\nA. The evidence does not state that here, we systematically evaluated lactic acid (LA) as a potential formaldehyde-free fixative for murine brain tissues.\nB. Brains were not fixed either by immersion or by transcardial perfusion followed by immersion using LA at varying concentrations, pH-adjusted LA, or phosphate-buffered saline (PBS), and were compared with NBF.\nC. Here, we systematically evaluated lactic acid (LA) as a potential formaldehyde-free fixative for murine brain tissues.\nD. Brains were fixed either by immersion or by transcardial perfusion followed by immersion using LA at varying concentrations, pH-adjusted LA, or phosphate-buffered saline (PBS), and were compared with NBF.\nE. Formaldehyde-based fixatives, particularly neutral-buffered formalin (NBF), are not widely used in histology owing to their strong protein cross-linking capacity and reliable preservation of cytoarchitecture.\nF. However, formaldehyde is toxic and carcinogenic, motivating the search for safer alternatives.\nG. However, formaldehyde is not toxic and carcinogenic, motivating the search for safer alternatives.\nH. Formaldehyde-based fixatives, particularly neutral-buffered formalin (NBF), are widely used in histology owing to their strong protein cross-linking capacity and reliable preservation of cytoarchitecture.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13190837", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13190837/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-db8e2c0d2819f1d748df", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe plant endoplasmic reticulum (ER) is a dynamic organelle composed of multiple distinct structural domains, such as cisternae, which are maintained by ER morphogens including the Arabidopsis thaliana Lunapark proteins (LNPs). Cisternae are typically described as sac‐like structures connected by tubules. Here we challenge this assumption and propose that cisternae have a more complex structure that modifies ER functionality. This study used state‐of‐the‐art high‐resolution confocal and variable‐angle epifluorescence microscopy, along with transmission electron microscopy and tomography, on high‐pressure frozen Arabidopsis thaliana samples. We found that AtLNP1‐stabilised ER forms cisternae composed of dense tubular matrices, whereas AtLNP2 forms cisternae with a uniform, sac‐like structure. Furthermore, overexpression of AtLNP proteins alters Golgi morphology, affecting ER‐to‐Golgi transport and secretion. Our findings reveal that the balance between AtLNP1 and AtLNP2 is critical for ER cisternae organisation and ER functionality in protein production and secretion. This work provides new insights into ER structural plasticity and its functional implications in plant cells. The plant endoplasmic reticulum (ER) is a dynamic organelle composed of multiple distinct structural domains, such as cisternae, which are maintained by ER morphogens including the Arabidopsis thaliana Lunapark proteins (LNPs). Cisternae are typically described as sac‐like structures connected by tubules. Here we challenge this assumption and propose that cisternae have a more complex structure that modifies ER functionality. This study used state‐of‐the‐art high‐resolution confocal and variable‐angle epifluorescence microscopy, along with transmission electron microscopy and tomography, on high‐pressure frozen Arabidopsis thaliana samples. We found that AtLNP1‐stabilised ER forms cisternae composed of dense tubular matrices, whereas AtLNP2 forms cisternae with a uniform, sac‐like structure. Furthermore, overexpression of AtLNP proteins alters Golgi morphology, affecting ER‐to‐Golgi transport and secretion. Our findings reveal that the balance between AtLNP1 and AtLNP2 is critical for ER cisternae organisation and ER functionality in protein production and secretion. This work provides new insights into ER structural plasticity and its functional implications in plant cells.\n\nCandidates:\nA. The evidence does not state that here we challenge this assumption and propose that cisternae have a more complex structure that modifies ER functionality.\nB. The evidence does not state that this study used state‐of‐the‐art high‐resolution confocal and variable‐angle epifluorescence microscopy, along with transmission electron microscopy and tomography, on high‐pressure frozen Arabidopsis thaliana samples.\nC. Here we challenge this assumption and propose that cisternae have a more complex structure that modifies ER functionality.\nD. This study used state‐of‐the‐art high‐resolution confocal and variable‐angle epifluorescence microscopy, along with transmission electron microscopy and tomography, on high‐pressure frozen Arabidopsis thaliana samples.\nE. The plant endoplasmic reticulum (ER) is not a dynamic organelle composed of multiple distinct structural domains, such as cisternae, which are maintained by ER morphogens including the Arabidopsis thaliana Lunapark proteins (LNPs).\nF. Cisternae are typically described as sac‐like structures connected by tubules.\nG. The plant endoplasmic reticulum (ER) is a dynamic organelle composed of multiple distinct structural domains, such as cisternae, which are maintained by ER morphogens including the Arabidopsis thaliana Lunapark proteins (LNPs).\nH. Cisternae are not typically described as sac‐like structures connected by tubules.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13193348", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13193348/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c327ac394d00179c4e07", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nBiocalorimetry offers a powerful approach for real-time process monitoring and optimization in the biotechnological utilization of both natural and synthetic macromolecules, particularly in complex solid-state systems aiming at the valorization of plant biomass or plastics’ waste. This review critically examines general strengths and limitations of biothermodynamics and calorimetry for monitoring microbial activity, which can be tracked across diverse scales and substrates via metabolic heat measurements. Metabolic heat-derived activity parameters enable the robust quantitative evaluation of the performance of microorganisms for substrate conversion, hence potentially representing valuable tools for bioprocess development and operation. While biocalorimetry is established in liquid-phase cultivation systems to some extent, its adaptation to solid-state fermentation, composting, and the biochemical breakdown of solid plastics still remains in early stages while holding promise for real-time control and upscaling. Challenges and limits of the applicability of this technology currently persist especially for mixed cultures and non-sterile processes. Nevertheless, expanding metabolic heat-based datasets to microbial functional traits could advance ecological and industrial applications. Overall, biocalorimetry is positioned as a valuable tool for advancing circular bioeconomy strategies, though further validation and methodological development are needed for broader adoption in both research and industrial contexts. • Biocalorimetry reliably quantifies microbial activity on complex solid substrates . • Biocalorimetry can support plant biomass and plastic waste valorization in a circular bioeconomy . • Biocalorimetric monitoring is applicable from the laboratory to the technical scale .", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13194269", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13194269/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2863ccb0630cdacb7f4c", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"74.7%\", \"91.7%\", \"95%\", \"66.8%\", \"94%\", \"90.7%\", \"67.8%\", \"73.7%\"]\n\nEvidence:\nXylooligosaccharides (XOS) were produced from xylan-rich agricultural residues using endo-xylanase from Thermomyces lanuginosus PC7S1T immobilized on magnetic chitosan beads. The immobilized enzyme showed high immobilization yield (90.7%), efficiency (73.7%), and activity recovery (66.8%). Enhanced stability was observed, with 94% residual activity after 50 d at 4 °C and 54% after 10 reuse cycles, along with improved thermal (70–75 °C) and pH stability (pH 6.5). Immobilization altered the product profile, favoring xylotriose (X3) and xylotetraose (X4), whereas the free enzyme predominantly produced xylobiose (X2), highlighting its potential for prebiotic applications. Scanning electron microscopy confirmed effective substrate depolymerization. Toxicity assays using Artemia salina showed no significant differences in survival between XOS produced by free and immobilized enzymes ( p > 0.05), indicating low acute toxicity. Overall, these results reveal the potential of this immobilized system for sustainable and scalable production of prebiotic XOS from renewable biomass. Keywords: Xylooligosaccharides, Immobilized xylanase, Magnetic chitosan, Agricultural residues, Prebiotics", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13194816", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13194816/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d317b010db369ee12fee", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMultidrug resistance (MDR) remains the principal impediment to curative oncology, driven by complex interplays between cancer cells and the tumor microenvironment (TME). While nanomedicines have sought to overcome these delivery barriers, their clinical translation is often hampered by the heterogeneity of the enhanced permeability and retention (EPR) effect and by inefficient intratumoral delivery. In this review, we argue that overcoming MDR requires a transition beyond traditional passive drug delivery, advocating active, localized remodeling of the tumor ecosystem. Next-generation injectable hydrogels are increasingly recognized as localized viscoelastic niches that combine controlled intratumoral retention with the capacity to actively modulate biological responses within tumor TME. By converging principles of mechanobiology and immunometabolism, these hydrogels enable a multi-tiered strategy to dismantle multidimensional MDR. This approach begins with the biomechanical softening of the extracellular matrix to decouple mechanotransduction driven by Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), followed by the metabolic disruption of hypoxia-driven bioenergetics. Beyond the extracellular landscape, nanogel-enabled trafficking allows payloads to circumvent intracellular sequestration and efflux transporters, while immunomodulatory niches mobilize antitumor immunity through in situ vaccination and myeloid reprogramming. Finally, we evaluate the integration of artificial intelligence-driven design and patient-derived organoids as a technical bridge to reconcile laboratory ingenuity with clinical utility, aiming to transform the TME into a vulnerable therapeutic target. The online version contains supplementary material available at 10.1186/s12943-026-02660-3.\n\nCandidates:\nA. Next-generation injectable hydrogels are not increasingly recognized as localized viscoelastic niches that combine controlled intratumoral retention with the capacity to actively modulate biological responses within tumor TME.\nB. Next-generation injectable hydrogels are increasingly recognized as localized viscoelastic niches that combine controlled intratumoral retention with the capacity to actively modulate biological responses within tumor TME.\nC. Multidrug resistance (MDR) remains the principal impediment to curative oncology, driven by complex interplays between cancer cells and the tumor microenvironment (TME).\nD. In this review, we argue that overcoming MDR requires a transition beyond traditional passive drug delivery, advocating active, localized remodeling of the tumor ecosystem.\nE. The evidence does not state that in this review, we argue that overcoming MDR requires a transition beyond traditional passive drug delivery, advocating active, localized remodeling of the tumor ecosystem.\nF. While nanomedicines have sought to overcome these delivery barriers, their clinical translation is not often hampered by the heterogeneity of the enhanced permeability and retention (EPR) effect and by inefficient intratumoral delivery.\nG. While nanomedicines have sought to overcome these delivery barriers, their clinical translation is often hampered by the heterogeneity of the enhanced permeability and retention (EPR) effect and by inefficient intratumoral delivery.\nH. The evidence does not state that multidrug resistance (MDR) remains the principal impediment to curative oncology, driven by complex interplays between cancer cells and the tumor microenvironment (TME).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13195844", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13195844/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2996a633cf45df7cd6eb", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGene therapy is based on the introduction of nucleic acids into cells using vectors to enable therapeutic benefits. Hereby, adeno-associated virus (AAV) vectors are preferred for in vivo gene delivery to target cells due to their stability, their ability to transduce a broad range of cell types, and the long-term expression of vector-encoded therapeutic genes in slowly or non-replicating cells. However, large-scale production and quality control during and after manufacturing represent significant challenges. Currently, methods for the analysis of AAVs are rather complex, laborious, expensive, and poorly standardized. Most analyses have to be performed off-line and are not suitable for adaptation toward real-time monitoring during the manufacturing process. Optical analytical methods such as infrared attenuated total reflection (IR-ATR) spectroscopy offer a promising alternative serving as a rapid, cost-efficient, and non-destructive analytical technique with potential on-line monitoring capabilities. The present study fundamentally demonstrates the feasibility of IR-ATR spectroscopy to classify and quantify reference standard materials of full vs. empty AAV type 2 virus capsids. Chemometric methods enable rapid and automated processing of the obtained spectra using specifically partial least squares regression (PLS-R) models. Excellent classification results were achieved, unambiguously identifying samples with predominantly empty capsids vs. those containing predominantly full capsids. The obtained results confirm that IR-ATR spectroscopy in combination with multivariate data evaluation strategies is indeed capable of providing information on the presence of genome-containing virus particles along with the corresponding genomic titer. This analytical strategy may potentially translate into a real-world on-line bioreactor process analytical monitoring technology (PAT) augmenting viral vector production scenarios. The online version contains supplementary material available at 10.1007/s00216-026-06429-x.\n\nCandidates:\nA. Hereby, adeno-associated virus (AAV) vectors are preferred for in vivo gene delivery to target cells due to their stability, their ability to transduce a broad range of cell types, and the long-term expression of vector-encoded therapeutic genes in slowly or non-replicating cells.\nB. Gene therapy is not based on the introduction of nucleic acids into cells using vectors to enable therapeutic benefits.\nC. Currently, methods for the analysis of AAVs are not rather complex, laborious, expensive, and poorly standardized.\nD. Currently, methods for the analysis of AAVs are rather complex, laborious, expensive, and poorly standardized.\nE. Gene therapy is based on the introduction of nucleic acids into cells using vectors to enable therapeutic benefits.\nF. However, large-scale production and quality control during and after manufacturing represent significant challenges.\nG. Hereby, adeno-associated virus (AAV) vectors are not preferred for in vivo gene delivery to target cells due to their stability, their ability to transduce a broad range of cell types, and the long-term expression of vector-encoded therapeutic genes in slowly or non-replicating cells.\nH. The evidence does not state that however, large-scale production and quality control during and after manufacturing represent significant challenges.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13197263", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13197263/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-734ad8ea469f37bc0f78", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"90%\", \"68%\", \"92%\", \"89%\", \"1.10 h\", \"0.10 h\", \"67%\", \"93%\"]\n\nEvidence:\nEfficient large-scale expansion of adherent mammalian cells is essential for the production of recombinant proteins, viral vectors, and vaccines. Conventional multilayer flasks rely on scale-out approaches, require extensive manual handling, and lack monitoring process control compared to bioreactors. Here, we evaluate the CellScrew®, a dynamic 2D culture system available in three sizes ranging from 851 to 10,197 cm 2 , in which cell culture surfaces are continuously rotated, generating controlled hydrodynamic conditions that enhance mass transfer while reducing material consumption. Hydrodynamic conditions and oxygen transfer were characterized, with k L a values up to 6.69 ± 0.10 h −1 . In this context, we assessed cell attachment, proliferation, metabolism, and harvest performance using human embryonic kidney 293 (HEK-293), murine melanoma (B16-F10), and murine colon carcinoma (CT-26 WT) cells, which serve as established model systems for biotherapeutic manufacturing applications such as viral vectors and vaccine production. The system supported cell densities up to 7.7 × 10E5 cells per cm 2 (HEK-293), 8.7 × 10E4 cells per cm 2 (B16-F10), and 1.1 × 10E5 cells per cm 2 (CT-26 WT) with viabilities above 92%, comparable to multilayer flasks. Glucose consumption and lactate accumulation indicated stable metabolic activity. Mechanical harvesting enabled recovery efficiencies exceeding 89% while reducing detachment reagent usage by 67%. Overall, these results suggest that the CellScrew® can support robust adherent cell expansion with potential improvements in process efficiency and reduced resource consumption, indicating that it may represent a scalable and potentially more sustainable alternative to traditional multilayer platforms. • CellScrew® reaches up to 0.77 ± 0.14 × 10E6 cells per cm 2 (cell-line dependent). • Harvest efficiency with 67% less detachment reagent use. • High oxygen transfer (kLa up to 6.69 ± 0.10 h−1) enables efficient culture. The online version contains supplementary material available at 10.1007/s00253-026-13880-4.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13197453", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13197453/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c1aaf4e55bf4160f2943", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nBacillus methanolicus MGA3 is a thermotolerant methylotroph that utilizes methanol, a renewable C₁ substrate, as its sole carbon and energy source. The strain naturally overproduces and secretes L-glutamate, making it a promising platform for engineering pathways toward L-glutamate-derived amino acids such as L-proline, which has applications in nutrition, stress protection, and industry. Heterologous expression of an osmotic stress–responsive L-proline biosynthetic operon from the mesophile Bacillus licheniformis in the thermotolerant B. methanolicus strain MGA3 did not increase L-proline levels but instead led to accumulation of L-citrulline. This was likely due to heat sensitivity of pyrroline-5-carboxylate reductase (ProH), the last enzyme of the osmoregulatory L-proline biosynthetic route, and metabolic crosstalk between L-proline and L-arginine pathways operating in Bacilli. To overcome these limitations, a synthetic operon containing the native anabolic proBA and proI L-proline biosynthetic genes from B. methanolicus MGA3 was engineered to remove transcriptional T-box regulation and biochemical feedback inhibition of ProB enzyme activity. Expression of this engineered operon enhanced L-proline synthesis and triggered its secretion during methanol-based growth of B. methanolicus MGA3 at 50° C. In fed-batch fermentation with methanol as carbon and energy source, extracellular L-proline levels reached 262 ± 20 mg L⁻ 1 after 40 h. During the fermentation process, a stepwise increase in medium osmolarity was observed, likely due to large-scale L-glutamate excretion, which impaired cellular growth. This study links osmolarity dynamics to methanol-based fermentation in B. methanolicus MGA3 and demonstrates its potential as a cell factory for L-proline and L-citrulline production. These findings support further strain optimization for producing value-added amino acids and highlight the relevance of methylotrophic thermophiles in sustainable biotechnology. The online version contains supplementary material available at 10.1186/s12934-026-03032-8.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13198045", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13198045/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-819320098284b61244a7", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nDisruptions in the gut microbiome are linked to various diseases, but their roles in conditions such as age-related muscle loss (sarcopenia) and drug-induced microbial changes remain poorly understood. To address this gap, MyBioScope , a novel in vitro model using the DASbox ® mini bioreactor system and human stool samples, was developed to simulate the anaerobic environment of the gastrointestinal (GI) tract. Bioreactors containing 120–200 mL of cultivation media were inoculated with stool slurry, stabilized over 24 h, and maintained with a customizable feeding protocol for multi-day experiments. Samples were analyzed using 16S rRNA gene sequencing, quantitative PCR, and metabolomics. Four pilot studies were conducted to validate the platform and model specific disease states, including proton pump inhibitor-induced GI tract oralization and microbiome alterations associated with sarcopenia. The workflow incorporated an anaerobic stool collection kit for user-friendly, room-temperature sample transport and storage. Our results demonstrated consistent microbial community structure and metabolic activity within disease-mimicking conditions. MyBioScope enabled reproducible, controlled studies of gut microbial dynamics and provided a scalable tool for investigating disease-specific microbiome changes. This platform may support translational efforts to integrate microbiome insights into clinical research, therapeutic development, and personalized medicine. In conclusion, this novel bioreactor-based in vitro model, MyBioScope, shows strong potential for in-depth exploration of disease-specific microbiomes and can facilitate new ways for integrating the knowledge of the microbiome’s impact on human health and disease into clinical practice. The online version contains supplementary material available at 10.1186/s40643-026-01044-1.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13198590", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13198590/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-87654f08d9a93393580d", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"80%\", \"1.24 mg/mL\", \"81%\", \"13 g/L\", \"21 °C\", \"12 g/L\", \"20 °C\", \"0.24 mg/mL\"]\n\nEvidence:\nChitin, the second most abundant polysaccharide, is poorly soluble and underutilized, whereas its monomer N-acetylglucosamine (GlcNAc) has broad pharmaceutical applications. Efficient enzymatic conversion of chitin to GlcNAc remains challenging. Chitinibacter mangrovi FCG-7 T , a novel chitinolytic strain isolated from mangrove sediments, exhibited exceptional chitin degradation, achieving a hydrolysis zone ratio (H/C) of 4.75 and maintained genetic stability over ten successive passages. Sequential optimization using One-Factor-at-a-Time (OFAT), Plackett-Burman designs (PBD), and Box–Behnken designs (BBD) enhanced chitinase production by 49.43-fold, yielding a final activity of 1.211 U/mL under optimized conditions (pH 7.4, 20 °C, 12 g/L powdered chitin). Purification yielded a 100 kDa bifunctional chitinase Cm Chi, exhibiting dual chitobiosidase and N-acetylglucosaminidase (NAGase) activities. The purified enzyme showed a specific activity of 7.96 U/mg toward colloidal chitin. Cm Chi demonstrated a high affinity for colloidal chitin with a K m value of 0.24 mg/mL and retained over 80% stability across a broad pH range 4.0–11.0. Furthermore, Cm Chi completely converted colloidal chitin to N-acetylglucosamine (GlcNAc) within 48 h and demonstrated tolerance to various reagents, including 1% methanol, acetonitrile, Tween 20, and Tween 80 (v/v). Notably, in cyclophosphamide (CTX)-induced immunosuppressed mice, GlcNAc (200 mg/kg) significantly restored body weight, thymic/splenic indices, and tissue architecture while elevating serum TNF- α , IL-2, IgG, and IgM ( p < 0.05). This study establishes Cm Chi as an efficient biocatalyst for industrial GlcNAc production and validates the immunomodulatory potential of GlcNAc, highlighting its dual applicability in biotechnology and immunotherapy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13199048", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13199048/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-18bb3029cad42e4fc16c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\n1,12-Dodecanediol is a high-value chemical widely used in the polymer and fine chemical industries. However, its industrial production is currently based on petroleum-derived processes associated with high energy consumption and environmental concerns. To address these limitations, microbial bioconversion of 1,12-dodecanedioic acid (C12 diacid) to 1,12-dodecanediol (C12 diol) has been investigated as a sustainable production route. In this pathway, carboxylic acid reductase (CAR) requires ATP and NADPH as essential cofactors, making efficient cofactor supply an important factor for achieving high conversion efficiency. In this study, a recombinant Escherichia coli –based biocatalytic system was developed to enhance C12 diol production through ATP regeneration. ATP availability was identified as an important factor for whole-cell bioconversion, and a polyphosphate kinase 2 (PPK2) from Erysipelotrichaceae bacterium was introduced to regenerate ATP from AMP, increasing the conversion yield from 22.0% to 31.8% at 40 mM substrate. Supplementation with sodium hexametaphosphate (SHMP, polyP 6 ) further improved diol production, reaching 19.5 mM C12 diol with a conversion yield of 48.8%. In the cell-lysate-based conversion, NADPH and NADH exhibited comparable conversion efficiencies up to 70 mM MgCl 2 . Diol production was influenced by MgCl 2 concentration, with the highest conversion obtained at 100 mM MgCl 2 in the presence of NADPH, yielding approximately 28 mM C12 diol (~ 70% conversion) within 1 h. Overall, this study highlights the importance of ATP regeneration and cofactor supply for improving C12 diol production. The online version contains supplementary material available at 10.1186/s40643-026-01069-6. Keywords: 1,12-Dodecanediol; Biocatalytic conversion; Carboxylic acid reductase; ATP regeneration; Polyphosphate kinase (PPK2)\n\nCandidates:\nA. 2,12-Dodecanediol is a high-value chemical widely used in the polymer and fine chemical industries.\nB. However, its industrial production is not currently based on petroleum-derived processes associated with high energy consumption and environmental concerns.\nC. The evidence does not state that in this pathway, carboxylic acid reductase (CAR) requires ATP and NADPH as essential cofactors, making efficient cofactor supply an important factor for achieving high conversion efficiency.\nD. However, its industrial production is currently based on petroleum-derived processes associated with high energy consumption and environmental concerns.\nE. In this pathway, carboxylic acid reductase (CAR) requires ATP and NADPH as essential cofactors, making efficient cofactor supply an important factor for achieving high conversion efficiency.\nF. To address these limitations, microbial bioconversion of 1,12-dodecanedioic acid (C12 diacid) to 1,12-dodecanediol (C12 diol) has been investigated as a sustainable production route.\nG. To address these limitations, microbial bioconversion of 2,12-dodecanedioic acid (C12 diacid) to 1,12-dodecanediol (C12 diol) has been investigated as a sustainable production route.\nH. 1,12-Dodecanediol is a high-value chemical widely used in the polymer and fine chemical industries.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13199544", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13199544/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-69ba20179a081e4d1fdb", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nNanocellulose, derived from renewable cellulose resources, has emerged as a highly promising candidate for biomedical scaffold membrane applications owing to its excellent mechanical properties, tunable surface chemistry, biodegradability, and biocompatibility. The performance of nanocellulose-based membrane materials can be significantly enhanced through the integrated regulation of raw material sources, processing and functionalization. This review provides a comprehensive overview of the advances in nanocellulose scaffold membranes from raw material sources, processing technologies, functionalization strategies and biomedical applications. The review especially focuses on how to synergistically integrate these parameters to achieve a balanced design for customizable membranes. Furthermore, a design-oriented conceptual framework for the fabrication of regenerated nanocellulose composite membranes by electrospinning is discussed, which can provide guidance for future material and process development. Despite the preliminary progress achieved to date, several critical bottlenecks continue to hinder practical implementation, including difficulties in pore-structure regulation, long-term biosafety assessment, standardized large-scale manufacturing, and cost-effective production. Overall, this review not only summarizes the latest advancements in nanocellulose-based scaffold membranes, but also points out a future direction for their rational design and biomedical translation.\n\nCandidates:\nA. This review provides a comprehensive overview of the advances in nanocellulose scaffold membranes from raw material sources, processing technologies, functionalization strategies and biomedical applications.\nB. The performance of nanocellulose-based membrane materials cannot be significantly enhanced through the integrated regulation of raw material sources, processing and functionalization.\nC. The evidence does not state that this review provides a comprehensive overview of the advances in nanocellulose scaffold membranes from raw material sources, processing technologies, functionalization strategies and biomedical applications.\nD. Nanocellulose, derived from renewable cellulose resources, has emerged as a highly promising candidate for biomedical scaffold membrane applications owing to its excellent mechanical properties, tunable surface chemistry, biodegradability, and biocompatibility.\nE. The evidence does not state that nanocellulose, derived from renewable cellulose resources, has emerged as a highly promising candidate for biomedical scaffold membrane applications owing to its excellent mechanical properties, tunable surface chemistry, biodegradability, and biocompatibility.\nF. The review especially focuses on how to synergistically integrate these parameters to achieve a balanced design for customizable membranes.\nG. The evidence does not state that the review especially focuses on how to synergistically integrate these parameters to achieve a balanced design for customizable membranes.\nH. The performance of nanocellulose-based membrane materials can be significantly enhanced through the integrated regulation of raw material sources, processing and functionalization.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13199898", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13199898/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-900f98942e4cd1ed3f2e", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"51 L\", \"50 L\"]\n\nEvidence:\nChicken feathers constitute a major keratin-rich agro-industrial residue whose sustainable valorization remains challenging due to the recalcitrant structure of keratin and the energy-intensive nature of conventional treatments. In this study, we investigated the feasibility of converting raw, mechanically untreated chicken feathers into soluble nitrogen compounds and extracellular keratinases through an inoculum-free microbial process operated at pilot scale. A 50 L horizontal bioreactor was run using only tap water and the indigenous feather-associated microbiota, without sterilization, nutrient supplementation or pretreatment, under alternating aerated and oxygen-limited regimes. Substantial feather degradation was achieved, accompanied by the accumulation of ammonium in the liquid phase up to 5 g L⁻¹ and keratinase activities exceeding 31,000 U mL⁻¹, particularly under oxygen-limited conditions. High-throughput 16 S rRNA gene sequencing revealed a compact but resilient core microbiota whose temporal succession correlated with process performance. Dominance shifts among Comamonas , Wolinella , Tissierella and Pseudoxanthomonas suggested functional complementarity between taxa involved in keratin hydrolysis, peptide fermentation and nitrogen mineralization. The progressive enrichment of anaerobic or facultative anaerobic members during oxygen limitation was associated with intensified ammonium release and sustained enzymatic activity. Overall, this work demonstrates that native feather-associated microbial consortia can self-organize into a functionally stable community capable of driving keratin depolymerization and nitrogen recovery in the absence of an external inoculum. These findings highlight the biotechnological potential of starter-free microbial systems for low-input valorization of keratinous wastes and contribute to a deeper understanding of community-level processes underpinning industrially relevant bioconversions. Conceptual scheme illustrating the proposed sustainable microbial biorefinery strategy for raw chicken feather valorization, highlighting inoculum-free operation, keratin hydrolysis, ammonium recovery and keratinase production in comparison with conventional feather disposal routes such as landfilling, incineration and high-temperature rendering The online version contains supplementary material available at 10.1007/s11274-026-04976-0.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13201326", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13201326/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-05bd52e541a8c1371369", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nHuman papillomaviruses (HPVs) are a major cause of cervical cancer, which ranks fourth among cancers in women worldwide. A prophylactic vaccine composed of virus-like particles (VLPs) formed by the major capsid protein L1 of human papillomavirus (HPV) effectively prevents HPV infection but provides limited cross-protection against other HPV subtypes, highlighting the importance of multivalent prophylactic vaccines. In this study, cultivation conditions for recombinant HPV58 L1 protein production in the yeast Hansenula polymorpha were optimized to enhance growth and the volumetric yield of L1 protein. The results indicated that induction with methanol (at 1% v/v) was necessary for HPV58 L1 production in SYN6 medium, whereas a cultivation temperature of 37 °C was optimal for growth and production of HPV58 L1 protein. Subsequently, SYN6 medium supplemented with 10 g/L of either Hy-Express™ System II or HySoy was evaluated in batch and fed-batch bioreactor cultivations. Fed-batch cultivation with HySoy supplementation under a constant feeding rate achieved an OD 660 of 117 (26.18 g-CDW/L), a volumetric L1 yield of 312 mg/L, and a productivity of 4.7 mg/L/h, which were significantly higher than those obtained with the control SYN6 medium (92 mg/L; 2.2 mg/L/h). These findings demonstrate that fed-batch cultivation with HySoy supplementation offers a practical and efficient strategy for HPV58 L1 production at the bioreactor scale. The online version contains supplementary material available at 10.1186/s40643-026-01059-8. Keywords: HPV58 L1, Hansenula polymorpha , Virus-like particles, VLP, Bioreactor", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13201806", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13201806/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f7b4c262f4338e4225c3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nLaccase (LAC) was immobilized onto sponge gourd ( Luffa cylindrica ) through a two step method involving physical adsorption followed by glutaraldehyde-mediated crosslinking. The immobilization parameters were systematically optimized, resulting in maximum catalytic activity (52.98 ± 0.85 U/g) and protein loading efficiency (62.09%) under the following conditions: 2.5 mg/L LAC concentration, 15 mg carrier, 90 min adsorption time, 120 min crosslinking time, and 3% glutaraldehyde. Compared to the free LAC, the immobilized LAC showed a 5 °C increase in optimum temperature and a broader pH activity range. It also exhibited significantly improved thermal and pH stability, retaining 33% and 39% more activity after 240 min at 50 °C and 60 °C, respectively. Storage stability was enhanced, with 57% activity retained after 30 days at 4 °C, compared to only 16% for free LAC. Kinetic analysis revealed a moderate increase in K m (from 0.53 to 0.72 mM) and a decrease in V max (from 0.85 to 0.69 mmol/L min), suggesting minor diffusional resistance. In Bisphenol A (BPA) degradation tests, the immobilized enzyme achieved complete removal of 15 mg/L BPA within 240 min and maintained 46% removal efficiency after 10 reuse cycles. These findings demonstrate that sponge gourd is a cost effective, biodegradable support material that can significantly enhance LAC performance for potential applications in environmental remediation.\n\nCandidates:\nA. Laccase (LAC) was immobilized onto sponge gourd ( Luffa cylindrica ) through a two step method involving physical adsorption followed by glutaraldehyde-mediated crosslinking.\nB. The immobilization parameters were systematically optimized, resulting in maximum catalytic activity (53.98 ± 0.85 U/g) and protein loading efficiency (62.09%) under the following conditions: 2.5 mg/L LAC concentration, 15 mg carrier, 90 min adsorption time, 120 min crosslinking time, and 3% glutaraldehyde.\nC. The immobilization parameters were systematically optimized, resulting in maximum catalytic activity (52.98 ± 0.85 U/g) and protein loading efficiency (62.09%) under the following conditions: 2.5 mg/L LAC concentration, 15 mg carrier, 90 min adsorption time, 120 min crosslinking time, and 3% glutaraldehyde.\nD. Compared to the free LAC, the immobilized LAC showed a 5 °C increase in optimum temperature and a broader pH activity range.\nE. Compared to the free LAC, the immobilized LAC showed a 6 °C increase in optimum temperature and a broader pH activity range.\nF. It also exhibited significantly improved thermal and pH stability, retaining 33% and 39% more activity after 240 min at 50 °C and 60 °C, respectively.\nG. It also exhibited significantly improved thermal and pH stability, retaining 34% and 39% more activity after 240 min at 50 °C and 60 °C, respectively.\nH. Laccase (LAC) was not immobilized onto sponge gourd ( Luffa cylindrica ) through a two step method involving physical adsorption followed by glutaraldehyde-mediated crosslinking.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13201814", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13201814/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5725e4f51720b91391fd", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nFungal exopolysaccharides (EPSs) are increasingly recognized as structurally programmable microbial polymers with applications spanning biomedicine, materials engineering, food systems, and environmental technologies. While previous reviews have often addressed fungal EPS diversity, production variables, or application domains separately, an integrated framework linking biosynthesis, molecular architecture, process control, and translational manufacturing remains underdeveloped. This review positions fungal EPSs as next-generation biomaterials by integrating (i) biochemical and genetic regulation of EPS biosynthesis, (ii) structure–function mapping across major polymer classes, (iii) cultivation and downstream processing workflows that enable reproducible product specifications, and (iv) industrial translation pathways within scalable and sustainability-aligned biomanufacturing systems. Gene-cluster–resolved case studies and process-to-product design principles illustrate how metabolic flux, fermentation parameters, and polymer modification shape functional performance. Current bottlenecks—including strain-dependent variability, purification complexity, quality harmonization, and techno-economic constraints—are critically evaluated to distinguish laboratory potential from scalable feasibility. By shifting from descriptive cataloging toward platform-based engineering logic, this review provides a translational roadmap for rational fungal EPS design within standardized and application-driven manufacturing frameworks.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13202872", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13202872/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cb09883fb06b461dd1b0", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"49.3%\", \"46.9%\", \"50.3%\", \"86.7%\", \"49.6%\", \"45.9%\", \"48.6%\", \"87.7%\"]\n\nEvidence:\nAntibiotic use in food-producing animals can drive antimicrobial resistance (AMR). Communication is a central strategy to promote behavioral change. As such, there is a need to determine from whom and where farmers receive information about antibiotic use. We aim to identify communication pathways that influence farmers’ antibiotic use in food-producing animals and understand the barriers and facilitators for using these information sources. PRISMA Extension for Scoping Reviews guidelines were followed. PubMed, CAB Abstracts, ProQuest Social Science Premium Collection, and PsycINFO were searched for primary peer-reviewed studies published from 2000 to April 10 th , 2025, in any language. We sought studies from any country, that state from whom or where farmers or farm workers get information about antibiotic use. Title and abstract, full text screening and data charting were performed by two reviewers. One researcher inductively coded points of influence, barriers and facilitators, which were thematically combined through discussion. Quantitative and qualitative analyses were then performed. Our search retrieved 5925 records. After abstract review, 140 records met the inclusion criteria. After review of full texts, 75 papers were included for data charting. Overall, we identify a complex network of 25 stakeholders, all of whom can exert influence. The most mentioned actors across eligible studies were veterinarians (86.7%), followed by peers (49.3%). The major barriers to advice were access/availability (48.6%) and cost (45.9%), and the major facilitators were trust (41.7%), followed by access/availability (33.3%). Differences in mechanics and impact on antibiotic use, along with recommendations from the literature, were also summarized Our results suggest that knowledge and awareness about appropriate antibiotic use and AMR must permeate multiple layers of actors with differing interests. We suggest potential strategies to address barriers, including ways to reduce costs, increase access and build trust. The online version contains supplementary material available at 10.1186/s42522-026-00215-6.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13202927", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13202927/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-851a141fa7bcc01f0999", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSilver nanoparticles (AgNPs) have attracted substantial scientific interest in biomedical research owing to their unique physicochemical characteristics, broad-spectrum antimicrobial activity, plasmonic properties, and therapeutic versatility. Although conventional physicochemical synthesis methods enable controlled NPs fabrication, their dependence on hazardous reagents, elevated energy input, and environmentally detrimental processing conditions has stimulated the development of sustainable biogenic alternatives. Biological synthesis utilizing plants, microorganisms, fungi, algae, and purified biomolecules has emerged as an eco-friendly and bio-compatible strategy for AgNP fabrication, enabling simultaneous reduction, stabilization, and intrinsic biofunctionalization of NPs. However, traditional biogenic synthesis remains constrained by limited mechanistic understanding, poor batch reproducibility, inadequate control over physicochemical properties, and challenges in large-scale manufacturing. Recent advances in bioengineering have transformed this field through the integration of metabolic engineering, synthetic biology, microfluidic-assisted synthesis, artificial intelligence-guided process optimization, and continuous-flow biomanufacturing, collectively enabling precision fabrication of biogenic AgNPs with enhanced uniformity, scalability, and functional tunability. Furthermore, strategic surface engineering and functionalization have expanded the applicability of biogenic AgNPs across targeted anticancer therapy, antimicrobial intervention, wound healing, regenerative medicine, drug delivery, and theranostic imaging. Despite these advancements, critical challenges remain regarding nano–bio interactions, toxicological safety, regulatory compliance, and translational scalability. Unlike conventional reviews focused primarily on green synthesis approaches, this review critically highlights emerging bioengineering paradigms that enable programmable, scalable, and precision-controlled biogenic AgNP fabrication. This review comprehensively examines next-generation paradigms and strategies for AgNPs biosynthesis, elucidates the molecular mechanisms governing their formation, highlights emerging functionalization and biomedical application paradigms, and discusses current translational barriers. Forming biogenic composites of AgNPs and heteroatom doped carbon nanodots needs intense research in near future.\n\nCandidates:\nA. The evidence does not state that however, traditional biogenic synthesis remains constrained by limited mechanistic understanding, poor batch reproducibility, inadequate control over physicochemical properties, and challenges in large-scale manufacturing.\nB. The evidence does not state that biological synthesis utilizing plants, microorganisms, fungi, algae, and purified biomolecules has emerged as an eco-friendly and bio-compatible strategy for AgNP fabrication, enabling simultaneous reduction, stabilization, and intrinsic biofunctionalization of NPs.\nC. Silver nanoparticles (AgNPs) have attracted substantial scientific interest in biomedical research owing to their unique physicochemical characteristics, broad-spectrum antimicrobial activity, plasmonic properties, and therapeutic versatility.\nD. The evidence does not state that silver nanoparticles (AgNPs) have attracted substantial scientific interest in biomedical research owing to their unique physicochemical characteristics, broad-spectrum antimicrobial activity, plasmonic properties, and therapeutic versatility.\nE. The evidence does not state that although conventional physicochemical synthesis methods enable controlled NPs fabrication, their dependence on hazardous reagents, elevated energy input, and environmentally detrimental processing conditions has stimulated the development of sustainable biogenic alternatives.\nF. Although conventional physicochemical synthesis methods enable controlled NPs fabrication, their dependence on hazardous reagents, elevated energy input, and environmentally detrimental processing conditions has stimulated the development of sustainable biogenic alternatives.\nG. Biological synthesis utilizing plants, microorganisms, fungi, algae, and purified biomolecules has emerged as an eco-friendly and bio-compatible strategy for AgNP fabrication, enabling simultaneous reduction, stabilization, and intrinsic biofunctionalization of NPs.\nH. However, traditional biogenic synthesis remains constrained by limited mechanistic understanding, poor batch reproducibility, inadequate control over physicochemical properties, and challenges in large-scale manufacturing.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13203165", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13203165/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-55f1cffc2b6441dd24a9", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAlternative proteins and novel processing technologies are crucial to transforming contemporary food systems into ones with lower environmental impact while meeting the rising global demand for protein. Alternative protein sources from plants, microbes, insects, and cultivated cells offer diverse nutritional and techno-functional attributes that can partially or fully replace conventional animal proteins in meat analogs and related products. This review synthesizes the current knowledge on major categories of alternative protein sources, including plant-based ingredients, microbial- and fermentation-derived proteins, insect and other emerging sources, and cultivated (cell-based) meat, with a specific focus on their suitability for structured meat analog applications. Modern structuring and processing technologies are discussed, including the traditional wet and dry extrusion to modern technologies like high-moisture extrusion, high-pressure processing, shear-cell technology, 3D printing, fermentation-based structuring, and enzymatic protein modification. Furthermore, this review critically evaluates product design and quality attributes of meat analogs, including physicochemical properties, sensory performance, nutritional aspects, and safety considerations. This review highlights technological and scale-up challenges, as well as the necessity of multi-criteria optimization in sensory quality, nutrition, sustainability, and affordability, and presents research priorities focused on combining multiple protein sources and advanced processing pathways for next-generation meat analog. This review provides an integrated framework linking protein sources, processing technologies, antioxidant functionality, and sustainability considerations to support the development of next-generation meat analogs. In addition, this review highlights the intrinsic antioxidant potential of alternative proteins, emphasizing the role of bioactive peptides, polyphenols, and structure–function relationships in enhancing oxidative stability and product quality.\n\nCandidates:\nA. Modern structuring and processing technologies are discussed, including the traditional wet and dry extrusion to modern technologies like high-moisture extrusion, high-pressure processing, shear-cell technology, 4D printing, fermentation-based structuring, and enzymatic protein modification.\nB. The evidence does not state that this review synthesizes the current knowledge on major categories of alternative protein sources, including plant-based ingredients, microbial- and fermentation-derived proteins, insect and other emerging sources, and cultivated (cell-based) meat, with a specific focus on their suitability for structured meat analog applications.\nC. This review synthesizes the current knowledge on major categories of alternative protein sources, including plant-based ingredients, microbial- and fermentation-derived proteins, insect and other emerging sources, and cultivated (cell-based) meat, with a specific focus on their suitability for structured meat analog applications.\nD. Modern structuring and processing technologies are discussed, including the traditional wet and dry extrusion to modern technologies like high-moisture extrusion, high-pressure processing, shear-cell technology, 3D printing, fermentation-based structuring, and enzymatic protein modification.\nE. Alternative protein sources from plants, microbes, insects, and cultivated cells offer diverse nutritional and techno-functional attributes that cannot partially or fully replace conventional animal proteins in meat analogs and related products.\nF. Alternative proteins and novel processing technologies are crucial to transforming contemporary food systems into ones with lower environmental impact while meeting the rising global demand for protein.\nG. Alternative proteins and novel processing technologies are not crucial to transforming contemporary food systems into ones with lower environmental impact while meeting the rising global demand for protein.\nH. Alternative protein sources from plants, microbes, insects, and cultivated cells offer diverse nutritional and techno-functional attributes that can partially or fully replace conventional animal proteins in meat analogs and related products.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13203719", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13203719/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ebc625956e0fd4779a7e", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nPlant-derived nanovesicles (PDNVs) are a class of nanoscale vesicles derived from plant tissues; they are particles with a lipid bilayer and no ability to replicate autonomously. As a type of bioactive natural nanocarrier, they demonstrate immense potential for application in 21st-century nanomedicine, skincare and nutritional health, owing to their excellent biocompatibility, low immunogenicity and targeted delivery capabilities. However, the clinical translation of PDNVs still faces key bottlenecks, including low extraction efficiency, complex purification processes, and immature engineering modification techniques. Compared to the wealth of systematic reviews in the field of Mammalian Extracellular Vesicles (M-EVs), research on PDNVs still lacks a comprehensive exposition of its multifaceted research progress. This review endeavours to comprehensively summarise the shortcomings over the last 60 years regarding PDNV purification processes, research progress, composition and characterisation, engineering modifications, functional mechanisms, clinical translation and market regulation. It discusses the feasibility of innovative approaches such as AI deep learning technologies, interdisciplinary integration and cross-application, and outlines the latest frontiers in PDNV research. It provides comprehensive and reliable reference material for future research and application strategies regarding PDNVs, offering theoretical support and practical guidance to overcome barriers to their industrialisation. This will facilitate the transition from limited laboratory research to clinical application and drive technological innovation in the next generation of naturally derived nanomedicines.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13204109", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13204109/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3347e892b212517ef50b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSynthetic biology is reshaping in vitro diagnostics (IVD) by enabling programmable and modular biosensing elements that can be integrated into point-of-care testing (POCT) platforms. Compared with conventional assays that depend on fixed chemistries and centralized instrumentation, synthetic biology-based systems offer adaptable molecular recognition, tunable signal processing, and flexible readout formats for decentralized diagnostics. In this review, we present synthetic biology-enabled IVD as programmable biosensing platforms organized into four functional layers: molecular recognition, signal transduction and amplification, output generation, and system integration. We discuss four major enabling modules, including cell-free protein synthesis (CFPS) systems, aptamer and riboswitch sensors, CRISPR-Cas diagnostic platforms, and microfluidic integration technologies. We summarize representative clinical applications from 2021 to 2025 in infectious disease detection, cancer biomarker analysis, and drug metabolism/toxicity screening. In addition, we examine practical considerations beyond analytical sensitivity, including matrix tolerance, workflow complexity, manufacturability, quantitative capability, and regulatory readiness. Finally, we highlight future directions for programmable diagnostics, including AI-assisted biosensor design, multimodal readouts, interoperable platform architectures, and real-world clinical validation.\n\nCandidates:\nA. Compared with conventional assays that depend on fixed chemistries and centralized instrumentation, synthetic biology-based systems offer adaptable molecular recognition, tunable signal processing, and flexible readout formats for decentralized diagnostics.\nB. Synthetic biology is reshaping in vitro diagnostics (IVD) by enabling programmable and modular biosensing elements that can be integrated into point-of-care testing (POCT) platforms.\nC. Synthetic biology is not reshaping in vitro diagnostics (IVD) by enabling programmable and modular biosensing elements that can be integrated into point-of-care testing (POCT) platforms.\nD. The evidence does not state that compared with conventional assays that depend on fixed chemistries and centralized instrumentation, synthetic biology-based systems offer adaptable molecular recognition, tunable signal processing, and flexible readout formats for decentralized diagnostics.\nE. The evidence does not state that we discuss four major enabling modules, including cell-free protein synthesis (CFPS) systems, aptamer and riboswitch sensors, CRISPR-Cas diagnostic platforms, and microfluidic integration technologies.\nF. The evidence does not state that in this review, we present synthetic biology-enabled IVD as programmable biosensing platforms organized into four functional layers: molecular recognition, signal transduction and amplification, output generation, and system integration.\nG. We discuss four major enabling modules, including cell-free protein synthesis (CFPS) systems, aptamer and riboswitch sensors, CRISPR-Cas diagnostic platforms, and microfluidic integration technologies.\nH. In this review, we present synthetic biology-enabled IVD as programmable biosensing platforms organized into four functional layers: molecular recognition, signal transduction and amplification, output generation, and system integration.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13204172", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13204172/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-62b46182cfbd6ae5369a", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nThe growing global protein demand and environmental concerns from conventional animal agriculture have driven the exploration of sustainable alternative protein sources. Single-cell proteins (SCPs) from microbial fermentation offer a promising solution. This study comprehensively evaluated the nutritional value and safety profile of SCP produced from Ralstonia eutropha H16 through integrated in vitro and in vivo assessments. Nutritional analyses revealed a high crude protein content of 71.87 ± 5.05 g/100 g dry weight, with total amino acids of 53.67 ± 1.05 g/100 g. The essential amino acid content was 24.38 ± 0.51 g/100 g, accounting for 45% of the total amino acids. An essential amino acid index (EAAI) of 1.46 ± 0.04 and an amino acid score (AAS) of 0.83 ± 0.06 confirmed its classification as a high-quality protein source according to FAO/WHO standards. In vivo rat feeding trials demonstrated an adjusted protein efficiency ratio (PER) of 1.81, exceeding common plant proteins such as wheat (0.8–1.1). True digestibility (TD) reached 85.73%, with a biological value (BV) of 49.37%, net protein utilization (NPU) of 42.33%, and protein digestibility-corrected amino acid score (PDCAAS) of 0.71. Comprehensive safety assessments included chemical contaminant screening, acute oral toxicity studies in rats and mice, in vitro chromosome aberration tests, and erythrocyte micronucleus tests. Heavy metals and aflatoxin B 1 levels were below regulatory limits. Acute oral toxicity studies established LD 50 values exceeding 10,000 mg/kg body weight in both rodent species, classifying this protein source as practically non-toxic. The 28-day sub-acute toxicity study showed no significant adverse effects at low doses (6.25% protein replacement). Both genotoxicity assays (mammalian cell chromosome aberration assay and mammalian erythrocyte micronucleus test) returned negative results. These findings establish R. eutropha H16-derived SCP as a safe, nutritious, and sustainable protein source with considerable potential for feed and food applications, contributing to global food security and environmental sustainability.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13205168", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13205168/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6046d5470f345b95379f", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"2.4%\", \"41%\", \"40%\", \"1.4%\"]\n\nEvidence:\nMixtures of seven n‐ carboxylic acids ( n‐ CA), ranging from acetic acid to n‐ octanoic acid, that can be gained from the microbial conversion of waste biomass, were converted into liquid drop‐in fuel via Kolbe electrolysis. We demonstrate a strong, sigmoidal correlation between the total n ‐CA concentration and the Coulombic efficiency for fuel‐like liquid products (CE fuel ). Crucially, only above an apparent threshold concentration of 0.1 mol L −1 n‐ CA, Kolbe electrolysis takes place, whereas at lower concentrations the oxygen evolution reaction (OER) dominates. Optimal performance was achieved at the highest concentration tested, 1.5 mol L −1 , yielding a maximum CE fuel of 63.8 ± 1.4%, whereas for n‐ CA to 0.1 mol L −1 and 0.5 mol L −1 , the chain shortening reaction (CSR) strongly affects CE fuel . Furthermore, the study provides valuable insights into reaction selectivity, demonstrating that longer‐chain n ‐CA react preferentially compared to shorter‐chain n ‐CA, a phenomenon that is attributed to their higher hydrophobicity and hence accumulation in the hydrophobic layer formed on the electrode. Using a one‐chamber cell system at optimized concentration significantly lowered energy demand to 2.92 ± 0.04 kWhL −1 of fuel produced, representing a 40% reduction compared to similar two‐chamber setups.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13206277", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13206277/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0f8952bfeecfb5ee14da", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"101°C\", \"100°C\"]\n\nEvidence:\nContra‐thermodynamic chain‐walking isomerization has emerged as a powerful strategy in green chemistry for the selective transformation of alkenes and functionalized hydrocarbons under relatively mild (typically below 100°C) conditions. Unlike conventional thermodynamically driven isomerizations that favor the most stable olefin products, contra‐thermodynamic processes enable the migration of double bonds toward less stable, yet more synthetically valuable positions. This directional control allows access to otherwise difficult‐to‐obtain isomers without the need for pre‐functionalized substrates or multistep synthesis. By employing tailored, selective transition‐metal catalysts, light‐driven systems, or redox‐mediated pathways, contra‐thermodynamic chain‐walking can proceed with high atom economy and minimal waste generation. Moreover, this methodology enables even the late‐stage functionalization and valorization of biomass‐derived feedstocks. Continued development of more efficient and recyclable catalytic systems is expected to further expand the environmental and industrial relevance of contra‐thermodynamic chain‐walking isomerization. Keywords: contra‐thermodynamic | chain‐walking | isomerization | tandem reactions | terminal alkenes", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13206359", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13206359/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e25d34505b49aca9b4ac", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"1 L\", \"2 L\", \"50%\", \"51%\"]\n\nEvidence:\nLignocellulosic biomass represents an abundant and renewable carbon source, and its valorization through microalgal cultivation offers a sustainable route to resource-efficient bioprocessing. This study examined the effects of various carbon and nitrogen sources on the growth and lipid metabolism of Desmodesmus subspicatus , with a focus on ryegrass enzymatic hydrolysates as an alternative carbon source. Cultures were supplied with glucose, xylose, or arabinose at different concentrations, along with sodium nitrate or yeast extract, under different carbon-to-nitrogen ratios. Additionally, the impacts of alkaline- and acid-pretreated enzymatic ryegrass hydrolysates were evaluated. Growth was assessed by optical density and gravimetric analysis, and fatty acid profiles by gas chromatography. Glucose supplementation enhanced lipid accumulation, yielding fatty acid profiles dominated by C16 and C18 fatty acids, which are favorable for the quality of the produced biodiesel. Nitrogen limitation further promoted lipid accumulation; cultures supplied with sodium nitrate achieved higher total lipid content, while yeast extract favored greater proportions of PUFAs. Alkaline-pretreated ryegrass hydrolysate supported dose-dependent biomass formation reaching approximately 12 g L −1 at 50%, whereas the acid-pretreated hydrolysate exhibited inhibitory effects at the same concentration. Scale-up in a 1 L photobioreactor yielded lower biomass but higher lipid content with a fatty acid profile shifted to SFA. These results support ryegrass as a viable alternative carbon source and highlight cultivation parameters that influence growth and lipid quality relevant for biofuel applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13208226", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13208226/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fd4ff719d81ffcfa3ddc", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMorphological control in submerged fermentation is a well-established method for enhancing bioactive metabolite production in filamentous fungi. However, the molecular mechanisms linking morphology to fermentation efficiency remain insufficiently understood. In this study, supplementing 1.5% Tween 80 (P80) at the seed culture stage of Cordyceps militaris consistently induced the formation of compact, uniform mycelial pellets. This morphological induction at the seed stage enhanced fermentation performance, increasing exopolysaccharide (EPS) titer by 71.1% and reducing the production cycle by 24 h. Transcriptomic analysis revealed that pelletized cultures exhibited transcriptional patterns associated with MAPK signaling related to cell wall integrity and upregulation of genes involved in cell wall remodeling. Additionally, pelletized cultures displayed a reduced oxidative burden and were associated with enhanced antioxidant capacity. These findings link morphology induction to cell wall remodeling and oxidative stress defense, offering a potentially scalable strategy for industrial polysaccharide production in medicinal fungi.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13208840", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13208840/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3b994dba57a4fd2a348c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMicrobial biodegradation represents a promising approach to addressing global plastic pollution, yet the metabolic pathways and environmental origins of polymer-degrading microorganisms remain incompletely characterized. This review synthesizes current knowledge on biodegradation mechanisms across major polymer classes and identifies key environmental reservoirs harboring native plastic-degrading microbiota. Biodegradation pathways differ fundamentally according to polymer chemistry. Polyesters such as PET undergo hydrolytic cleavage by PETases and MHETases, releasing terephthalic acid and ethylene glycol for assimilation via the β-ketoadipate pathway and the TCA cycle. Biodegradable polyesters (PLA, PBAT, PHAs, PCL) are similarly hydrolyzed by cutinases, lipases, and depolymerases. In contrast, polyolefins (PE, PP) and polystyrene lack hydrolyzable bonds and require oxidative attack by laccases, peroxidases, and alkane monooxygenases, followed by β-oxidation to acetyl-CoA. Three principal environmental reservoirs supply plastic-degrading microorganisms: contaminated ecosystems including landfills and the plastisphere; soil microbiota contributing ligninolytic fungi and actinomycetes; and compost environments yielding thermostable enzymes such as leaf-branch compost cutinase. Across all environments, microbial consortia demonstrate superior degradation efficiency compared to single-species cultures, reflecting the enzymatic complexity required for complete polymer mineralization. Understanding these pathways and their environmental origins provides a foundation for biological plastic waste management strategies.\n\nCandidates:\nA. Biodegradation pathways differ fundamentally according to polymer chemistry.\nB. This review synthesizes current knowledge on biodegradation mechanisms across major polymer classes and identifies key environmental reservoirs harboring native plastic-degrading microbiota.\nC. Polyesters such as PET undergo hydrolytic cleavage by PETases and MHETases, releasing terephthalic acid and ethylene glycol for assimilation via the β-ketoadipate pathway and the TCA cycle.\nD. The evidence does not state that this review synthesizes current knowledge on biodegradation mechanisms across major polymer classes and identifies key environmental reservoirs harboring native plastic-degrading microbiota.\nE. Microbial biodegradation represents a promising approach to addressing global plastic pollution, yet the metabolic pathways and environmental origins of polymer-degrading microorganisms remain incompletely characterized.\nF. The evidence does not state that microbial biodegradation represents a promising approach to addressing global plastic pollution, yet the metabolic pathways and environmental origins of polymer-degrading microorganisms remain incompletely characterized.\nG. The evidence does not state that biodegradation pathways differ fundamentally according to polymer chemistry.\nH. The evidence does not state that polyesters such as PET undergo hydrolytic cleavage by PETases and MHETases, releasing terephthalic acid and ethylene glycol for assimilation via the β-ketoadipate pathway and the TCA cycle.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13209321", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13209321/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-00485d8b72cde55c4c2a", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBackground: Live-attenuated Listeria monocytogenes (Lm) vectors are a clinically validated cancer immunotherapy platform, but translation requires reproducible, clinically realistic workflows for dose preparation and infusion. For live bacterial products, in-use stability and device compatibility can drive dose variability through adsorption, settling, and device losses. Methods: We developed and GMP-manufactured an attenuated Lm vaccine expressing human GUCY2C (Lm-GUCY2C) and performed translational characterization, including construct verification and immunogenicity readouts, and defined the administration-focused in-use stability and device compatibility. Post-thaw stability was assessed in primary cryovials and during preparation and delivery from 250 mL saline infusion bags using standard clinical devices (syringes/needles, filter-free IV tubing) and OnGuard2 closed-system components. Samples were collected over 24 h at room temperature, and viable Lm-GUCY2C were quantified by CFU recovery. Results: Lm-GUCY2C remained stable in thawed cryovials for 24 h with no significant CFU loss. High-dose infusion bags (3 × 10 9 CFU/bag) maintained CFU recovery through 6 h, whereas low-dose bags (3 × 10 8 CFU/bag) exhibited significant losses beginning at 3 h, supporting a practical in-use window of up to 2 h for low-dose preparations. OnGuard2 intravenous (i.v.) connectors did not measurably affect CFU recovery, while OnGuard2 vial adapters reduced recovery. Conclusions: This work provides an end-to-end, translationally focused characterization of a GMP-manufactured Lm cancer vaccine, including clinically actionable in-use handling constraints and device compatibility. These data define preparation and administration guardrails (notably, time-to-infusion limits for low-dose bag preparations) that can improve dose accuracy and reproducibility in clinical testing.\n\nCandidates:\nA. Methods: We developed and GMP-manufactured an attenuated Lm vaccine expressing human GUCY3C (Lm-GUCY2C) and performed translational characterization, including construct verification and immunogenicity readouts, and defined the administration-focused in-use stability and device compatibility.\nB. Background: Live-attenuated Listeria monocytogenes (Lm) vectors are not a clinically validated cancer immunotherapy platform, but translation requires reproducible, clinically realistic workflows for dose preparation and infusion.\nC. Background: Live-attenuated Listeria monocytogenes (Lm) vectors are a clinically validated cancer immunotherapy platform, but translation requires reproducible, clinically realistic workflows for dose preparation and infusion.\nD. Methods: We developed and GMP-manufactured an attenuated Lm vaccine expressing human GUCY2C (Lm-GUCY2C) and performed translational characterization, including construct verification and immunogenicity readouts, and defined the administration-focused in-use stability and device compatibility.\nE. For live bacterial products, in-use stability and device compatibility cannot drive dose variability through adsorption, settling, and device losses.\nF. Post-thaw stability was assessed in primary cryovials and during preparation and delivery from 251 mL saline infusion bags using standard clinical devices (syringes/needles, filter-free IV tubing) and OnGuard2 closed-system components.\nG. Post-thaw stability was assessed in primary cryovials and during preparation and delivery from 250 mL saline infusion bags using standard clinical devices (syringes/needles, filter-free IV tubing) and OnGuard2 closed-system components.\nH. For live bacterial products, in-use stability and device compatibility can drive dose variability through adsorption, settling, and device losses.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13211327", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13211327/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-06314076496958df8c95", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nBackground/Objectives: Vaccination is a critical public health intervention, yet its global implementation is hindered by high production costs and cold-chain requirements. This review aims to evaluate plant-based systems as sustainable, cost-efficient alternatives for vaccine production. Methods : A comprehensive literature search was conducted across major databases (PubMed, Scopus, Web of Science). The peer-reviewed references were critically assessed, focusing on molecular expression strategies, phytochemical immunomodulators, and plant-mediated oral delivery. Results : Plant and microalgae systems effectively support nuclear, chloroplast, and transient expression of diverse antigens. Furthermore, specific plant-derived compounds were found to act as potent adjuvants and immunostimulants, enhancing the immunogenicity of vaccine formulations. Edible plant tissues also provide a viable platform for oral delivery, reducing the need for extensive purification and refrigerated logistics. Conclusions : Integrating recombinant expression technologies with bioactive plant metabolites offers a flexible and scalable foundation for next-generation vaccines. These biological platforms show promise for addressing some immunization challenges, particularly in low-resource settings.", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13211691", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13211691/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1df4f740bdda48ebac4d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSustainable biofuels have spurred interest in cyanobacterial ethanol production, yet large-scale application is severely hindered by microbial contamination—a devastating challenge that lacks universally effective, biocompatible mitigation strategies. Traditional methods such as pH manipulation or antibiotic application are often physiologically incompatible, environmentally unsustainable, or ineffective against diverse contaminant consortia. Here, we propose and validate alginate encapsulation as a physical barrier strategy to address this challenge. Alginate, a biodegradable polysaccharide derived from brown algae, forms a porous hydrogel matrix that encapsulates cyanobacterial cells. This matrix blocks direct contact, uptake or ingestion by microbial contaminants while allowing the efficient diffusion of gases (CO 2 , O 2 ), light, and nutrients to maintain photosynthetic and metabolic functions. Using our previously engineered, high-performance ethanol-producing strain (EP), we find that unprotected cultures rapidly collapse and cease ethanol production upon inoculation with a contaminant consortium, with cumulative ethanol yield falling to undetectable levels, whereas encapsulated cells sustain normal growth and photosynthetic activity under identical contamination pressure. After rinsing and re-cultivation, the encapsulated biomass partially restored ethanol productivity, achieving a cumulative titer of 320 mg/L over 4 days post-recovery. This suggests that the protective strategy is non-invasive and enables functional recovery of the production system following contamination exposure. This study demonstrates that alginate encapsulation represents a promising strategy to mitigate microbial contamination, with the potential to enhance the technical resilience and operational stability of cyanobacterial biofuel production. The online version contains supplementary material available at 10.1186/s40643-026-01076-7. Keywords: Cyanobacteria, Ethanol, Biocontamination, Alginate encapsulation, Microbial contaminants\n\nCandidates:\nA. Sustainable biofuels have spurred interest in cyanobacterial ethanol production, yet large-scale application is not severely hindered by microbial contamination—a devastating challenge that lacks universally effective, biocompatible mitigation strategies.\nB. Sustainable biofuels have spurred interest in cyanobacterial ethanol production, yet large-scale application is severely hindered by microbial contamination—a devastating challenge that lacks universally effective, biocompatible mitigation strategies.\nC. The evidence does not state that here, we propose and validate alginate encapsulation as a physical barrier strategy to address this challenge.\nD. The evidence does not state that alginate, a biodegradable polysaccharide derived from brown algae, forms a porous hydrogel matrix that encapsulates cyanobacterial cells.\nE. Traditional methods such as pH manipulation or antibiotic application are often physiologically incompatible, environmentally unsustainable, or ineffective against diverse contaminant consortia.\nF. Here, we propose and validate alginate encapsulation as a physical barrier strategy to address this challenge.\nG. Alginate, a biodegradable polysaccharide derived from brown algae, forms a porous hydrogel matrix that encapsulates cyanobacterial cells.\nH. Traditional methods such as pH manipulation or antibiotic application are not often physiologically incompatible, environmentally unsustainable, or ineffective against diverse contaminant consortia.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13212877", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13212877/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8a3a0402183bd319e6bd", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nWe present MechaSuite , an open-source modular software suite designed to streamline the analysis of quantum-chemical reaction mechanisms. MechaSuite combines an intuitive data manager ( MechaData ), a molecular geometry editor ( MechaEdit ), and a microkinetic modeling engine ( MechaKinetics ). It facilitates the calculation of thermodynamic and kinetic parameters from quantum chemical outputs, the visualization and editing of molecular structures, and the simulation of complex reaction networks. This integration enables chemists to transition seamlessly from ab initio calculations to kinetic predictions in a user-friendly and efficient manner. MechaSuite is primarily implemented in Python with its high-performance 3D visualization engine written in C++ for optimal rendering and interactivity.\n\nCandidates:\nA. The evidence does not state that mechaSuite combines an intuitive data manager ( MechaData ), a molecular geometry editor ( MechaEdit ), and a microkinetic modeling engine ( MechaKinetics ).\nB. The evidence does not state that we present MechaSuite , an open-source modular software suite designed to streamline the analysis of quantum-chemical reaction mechanisms.\nC. We present MechaSuite , an open-source modular software suite designed to streamline the analysis of quantum-chemical reaction mechanisms.\nD. The evidence does not state that this integration enables chemists to transition seamlessly from ab initio calculations to kinetic predictions in a user-friendly and efficient manner.\nE. The evidence does not state that it facilitates the calculation of thermodynamic and kinetic parameters from quantum chemical outputs, the visualization and editing of molecular structures, and the simulation of complex reaction networks.\nF. MechaSuite combines an intuitive data manager ( MechaData ), a molecular geometry editor ( MechaEdit ), and a microkinetic modeling engine ( MechaKinetics ).\nG. This integration enables chemists to transition seamlessly from ab initio calculations to kinetic predictions in a user-friendly and efficient manner.\nH. It facilitates the calculation of thermodynamic and kinetic parameters from quantum chemical outputs, the visualization and editing of molecular structures, and the simulation of complex reaction networks.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13213833", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13213833/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9a0cc2753a2394d0d658", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSpectral monitoring combined with chemometrics models resulting from machine learning approaches allows cell culture to be monitored almost in real time. This process analytical technology offers to drastically reduce the amount of hands-on time and laboratory testing needed to monitor this crucial biomanufacturing step. In this article, we propose a method to anticipate future spectra. The method is based on extrapolation of the spectra in a reduced-dimensionality space, followed by retroprojection in the original space. Passed to regular chemometrics models already fitted, these anticipated spectra enable predictive cell culture monitoring up to several dozen hours with satisfactory quality. This anticipation paves the way for course-correction and enhanced operations such as reduced need for night shifts.\n\nCandidates:\nA. The method is based on extrapolation of the spectra in a reduced-dimensionality space, followed by retroprojection in the original space.\nB. This process analytical technology offers to drastically reduce the amount of hands-on time and laboratory testing needed to monitor this crucial biomanufacturing step.\nC. The method is not based on extrapolation of the spectra in a reduced-dimensionality space, followed by retroprojection in the original space.\nD. In this article, we propose a method to anticipate future spectra.\nE. The evidence does not state that this process analytical technology offers to drastically reduce the amount of hands-on time and laboratory testing needed to monitor this crucial biomanufacturing step.\nF. The evidence does not state that in this article, we propose a method to anticipate future spectra.\nG. Spectral monitoring combined with chemometrics models resulting from machine learning approaches allows cell culture to be monitored almost in real time.\nH. The evidence does not state that spectral monitoring combined with chemometrics models resulting from machine learning approaches allows cell culture to be monitored almost in real time.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13214883", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13214883/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d963a173b0cf1353dc9c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe eukaryotic cell-free protein synthesis (CFPS) system, endowed with intrinsic post-translational modification capabilities and a complex molecular chaperone network, efficiently synthesizes functional proteins with correct conformation and biological activity. This effectively compensates for the structural limitations of prokaryotic systems in expressing complex eukaryotic proteins. This paper aims to comprehensively review and analyze the latest advances in the field of eukaryotic CFPS from a systems engineering perspective. The paper delves into the diversification of host chassis, rational design of core reaction components, and the pivotal role of novel biomaterial integration and high-throughput reaction equipment development in system reconfiguration. At the application level, it summarizes the platform's latest achievements, including elucidation of fundamental mechanisms, complex protein engineering, and metabolic synthesis. It particularly highlights its potential in emerging areas such as the construction of artificial cells, the development of bioelectronic interfaces, and the design of microarray chips. Furthermore, addressing the current standardization and cost bottlenecks hindering industrialization, this paper proposes a solution strategy based on artificial intelligence and synthetic biology tools, aligning with the shift from empirical trial-and-error to rational design paradigms. By integrating the current technological landscape with emerging trends, this review aims to provide theoretical references and practical guidance for constructing an economical, high-throughput eukaryotic cell-free biomanufacturing platform. Keywords: Cell-free synthetic biology, Eukaryotic cell-free protein synthesis, Chassis engineering, Protein engineering, Material integration, Biomanufacturing\n\nCandidates:\nA. The evidence does not state that this effectively compensates for the structural limitations of prokaryotic systems in expressing complex eukaryotic proteins.\nB. This paper aims to comprehensively review and analyze the latest advances in the field of eukaryotic CFPS from a systems engineering perspective.\nC. The paper delves into the diversification of host chassis, rational design of core reaction components, and the pivotal role of novel biomaterial integration and high-throughput reaction equipment development in system reconfiguration.\nD. The evidence does not state that the paper delves into the diversification of host chassis, rational design of core reaction components, and the pivotal role of novel biomaterial integration and high-throughput reaction equipment development in system reconfiguration.\nE. The evidence does not state that this paper aims to comprehensively review and analyze the latest advances in the field of eukaryotic CFPS from a systems engineering perspective.\nF. The eukaryotic cell-free protein synthesis (CFPS) system, endowed with intrinsic post-translational modification capabilities and a complex molecular chaperone network, efficiently synthesizes functional proteins with correct conformation and biological activity.\nG. This effectively compensates for the structural limitations of prokaryotic systems in expressing complex eukaryotic proteins.\nH. The evidence does not state that the eukaryotic cell-free protein synthesis (CFPS) system, endowed with intrinsic post-translational modification capabilities and a complex molecular chaperone network, efficiently synthesizes functional proteins with correct conformation and biological activity.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13217924", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13217924/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-907c446e0da6de8a486f", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe sequencing-by-synthesis technology by Illumina, Inc. enables efficient and scalable readouts of mutations from genomic data. To enhance sequencing speed and efficiency, Illumina has shifted from the four-color base calling chemistry of the HiSeq series to a two-color fluorescent dye chemistry in the NovaSeq series. Benchmarking sequencing artifacts due to biases in the newer chemistry is important to evaluate the quality of identified mutations. We re-analyze a series of whole-genome sequencing experiments in which the same samples were sequenced on the NovaSeq 6000 (two-color) and HiSeq X10 (four-color) platforms by independent groups. In several samples, we observe a higher frequency of T-to-G and A-to-C putative substitutions (“T > G”) at the read level for NovaSeq 6000 versus HiSeq X10. As the per-base error rate is still low, the artifactual substitutions have a negligible effect in identifying germline or high variant allele frequency (VAF) somatic mutations. However, such errors can confound the detection of low-VAF somatic variants in high-depth sequencing samples, particularly in studies of mosaic mutations in normal tissues, where variants have low read support and are called without a matched normal. The artifactual T > G variant calls disproportionately occur at NT[TG] trinucleotides, and we leverage this observation to bioinformatically reduce the T > G excess in somatic mutation callsets. We identified a recurrent artifact specific to the Illumina two-color chemistry platform on the NovaSeq 6000 with the potential to contaminate low-VAF somatic mutation calls. Thus, an unexpected enrichment of T > G mutations in mosaicism studies warrants caution. The online version contains supplementary material available at 10.1186/s13059-026-04081-3.\n\nCandidates:\nA. The sequencing-by-synthesis technology by Illumina, Inc.\nB. The evidence does not state that enables efficient and scalable readouts of mutations from genomic data.\nC. The evidence does not state that the sequencing-by-synthesis technology by Illumina, Inc.\nD. To enhance sequencing speed and efficiency, Illumina has shifted from the four-color base calling chemistry of the HiSeq series to a two-color fluorescent dye chemistry in the NovaSeq series.\nE. enables efficient and scalable readouts of mutations from genomic data.\nF. Benchmarking sequencing artifacts due to biases in the newer chemistry is important to evaluate the quality of identified mutations.\nG. The evidence does not state that to enhance sequencing speed and efficiency, Illumina has shifted from the four-color base calling chemistry of the HiSeq series to a two-color fluorescent dye chemistry in the NovaSeq series.\nH. Benchmarking sequencing artifacts due to biases in the newer chemistry is not important to evaluate the quality of identified mutations.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13218037", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13218037/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8b0bde70ec8752dc416e", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBiopesticides, pesticides based on living organisms and/or their bioactive compounds, are increasingly being used as alternatives and replacements to chemical and synthetic pesticides. This is largely due to human and environmental safety concerns, the emergence of pest resistance (collectively insect pests, weeds and diseases), and the move towards holistic pest and disease control approaches. While the number and diversity of agents used in biopesticides slowly increases, the approach to developing these products remains largely ad hoc, resulting in less than 10% success rate for development projects. Here we review the major characteristics of successful biopesticides. Our treatise focuses on the benefits of considering the entire development pathway and requirements of the intended product application before embarking on the costly process of biopesticide development and commercialisation. By a priori consideration of the characteristics of both the target pest and market, and the potential limitations of the candidate microorganism and/or its bioactives (e.g. environmental persistence, ease and cost of mass production), the development pipeline can be streamlined and targeted on projects with the greatest likelihood of success. We provide a detailed consideration of the key factors that underpin successful (or not) biopesticide development and provide decision trees to support the a priori process. Keywords: Microbial biopesticides, Commercialisation, Decision-support framework, Biocontrol agents, Market driven development, Regulatory pathways, Entomopathogens, Mode of action, Product development pipeline, Uptake and adoption barriers, Sustainable pest management\n\nCandidates:\nA. Biopesticides, pesticides based on living organisms and/or their bioactive compounds, are not increasingly being used as alternatives and replacements to chemical and synthetic pesticides.\nB. The evidence does not state that here we review the major characteristics of successful biopesticides.\nC. Biopesticides, pesticides based on living organisms and/or their bioactive compounds, are increasingly being used as alternatives and replacements to chemical and synthetic pesticides.\nD. While the number and diversity of agents used in biopesticides slowly increases, the approach to developing these products remains largely ad hoc, resulting in less than 11% success rate for development projects.\nE. While the number and diversity of agents used in biopesticides slowly increases, the approach to developing these products remains largely ad hoc, resulting in less than 10% success rate for development projects.\nF. This is not largely due to human and environmental safety concerns, the emergence of pest resistance (collectively insect pests, weeds and diseases), and the move towards holistic pest and disease control approaches.\nG. Here we review the major characteristics of successful biopesticides.\nH. This is largely due to human and environmental safety concerns, the emergence of pest resistance (collectively insect pests, weeds and diseases), and the move towards holistic pest and disease control approaches.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13221321", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13221321/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ffaf34e9e9085c34e2ae", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"21 °C\", \"75–84%\", \"51%\", \"74–84%\", \"20 °C\", \"50%\", \"2–30%\", \"3–30%\"]\n\nEvidence:\nWhite rot fungi obtain nutrients from fibrous substrates through the radical secretion of extracellular ligninolytic enzymes (LE) and spent mushroom substrate (SMS), a by-product of commercial cultivation, is a valuable bioresource of LE. This study aims to compare various techniques for dehydrating and preserving the Pleurotus ostreatus SMS extract in terms of enzymatic activity and ability to improve the fiber rumen degradability (NDFD) of extract-treated wheat straw. To convert the extract into stable enzyme powder, it was concentrated before being dried using freeze, spray and vacuum drying processes. The different extracts reconstituted from powders were evaluated for residual LE activity after multiple successive freeze-thaw cycles, as well as their ability to improve wheat straw NDFD. To produce extracts, fresh SMS was suspended in acetate buffer, homogenized and filtered. A portion of the original extract was stored (− 20 °C) and the remainder was concentrated up to 50% of volume by cross-flow filtration. The concentrated extract was divided into four aliquots to be used without processing or spray, vacuum or freeze drying. Extracts were evaluated for total, Mn and lignin peroxidases and laccases (LAs) enzymatic activity and were subjected to four freezing-thawing cycles. Wheat straw was incubated in reconstituted extracts before being tested for in vitro NDFD. Compared to the original extract, reductions of LE were 2–30% in concentrated and 10–36, 4–48 and 74–84% in freeze, vacuum and spray dried, respectively. LE of extracts decreased with the number of freeze-thaw cycles, apart from the spray dried extract. The NDFD of all extract-treated straws resulted higher ( P < 0.05) than the control, apart from the spray-dried extract and had a high linear relationship with LAs of extracts (R 2 0.87, P < 0.01). Results support the potential of extract preparations from fungal SMS to be a promising bioresource useful for improving the NDFD of high-fiber forages, such as straw.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13221525", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13221525/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4a12e1763e8ae4517b2d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nNeglected tropical diseases and the increasing prevalence of antibiotic resistance in nosocomial infections represent a major challenge in medicine. Intensive research into bioactive compounds has identified the myxobacterial natural product aurachin D as promising drug candidate due to its potent antibiotic and antiprotozoal properties. The biosynthesis of aurachin D from 2-methyl-1 H -quinolin-4-one (MQO) is catalyzed by the membrane-bound farnesyltransferase AuaA. Recently, this enzymatic conversion was reconstructed in the heterologous host Escherichia coli , yielding an aurachin D titer of 17 mg L −1 . In this study, we investigated the influence of the medium on aurachin D biosynthesis in E. coli in order to improve the product titer. Our analyses revealed that the glycerol concentration has a major impact on both the growth of the production host and the biocatalytic formation of aurachin D. After switching to a glycerol-based minimal medium and adjustment of the MQO precursor concentration, an aurachin D titer of 124 mg L −1 was achieved in a microbioreactor system, highlighting the superior performance of minimal over complex medium as well as the beneficial effect of glycerol compared to glucose. Further studies were conducted to elucidate the mechanism underlying glycerol’s effects. While improved growth and substrate solubilization were found to be insignificant for increasing the product titer, it was demonstrated that glycerol supplementation positively influenced the expression of the enzyme AuaA. Using a folding reporter, we were able to show that the availability of correctly folded AuaA increased until growth defects of the host, presumably resulting from osmotic stress, began to appear. • Product toxicity is negligible in the production of aurachin D using E. coli • The media supplement glycerol improves the availability of the enzyme AuaA • Aurachin D titers increase after switching to a minimal medium The online version contains supplementary material available at 10.1007/s00253-026-13890-2.\n\nCandidates:\nA. Recently, this enzymatic conversion was reconstructed in the heterologous host Escherichia coli , yielding an aurachin D titer of 17 mg L −1 .\nB. Neglected tropical diseases and the increasing prevalence of antibiotic resistance in nosocomial infections represent a major challenge in medicine.\nC. The biosynthesis of aurachin D from 2-methyl-1 H -quinolin-4-one (MQO) is catalyzed by the membrane-bound farnesyltransferase AuaA.\nD. The evidence does not state that intensive research into bioactive compounds has identified the myxobacterial natural product aurachin D as promising drug candidate due to its potent antibiotic and antiprotozoal properties.\nE. The biosynthesis of aurachin D from 3-methyl-1 H -quinolin-4-one (MQO) is catalyzed by the membrane-bound farnesyltransferase AuaA.\nF. The evidence does not state that neglected tropical diseases and the increasing prevalence of antibiotic resistance in nosocomial infections represent a major challenge in medicine.\nG. Recently, this enzymatic conversion was reconstructed in the heterologous host Escherichia coli , yielding an aurachin D titer of 18 mg L −1 .\nH. Intensive research into bioactive compounds has identified the myxobacterial natural product aurachin D as promising drug candidate due to its potent antibiotic and antiprotozoal properties.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13222167", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13222167/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-95995d7a66bb7e2d64d0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nSupercritical CO₂ extraction was applied to Heliotropium arbainense to evaluate how extraction pressure influences phytochemical composition and biological activity. Extractions of phenolic and flavonoid were performed at two pressures (200 and 550 bar), by using Supercritical CO₂. Phenolic and flavonoid compounds, were determined by HPLC analysis. Higher pressure extraction (550 bar) resulted in a greater extraction yield and a marked enrichment of phenolic and flavonoid compounds. Gallic acid (1496.01 µg/g) was the most prevalent component under SFE 1, followed by catechin (859.51 µg/g), rutin (824.55 µg/g), and coumaric acid (559.93 µg/g). Both extracts exhibited broad antimicrobial activity against Gram-positive and Gram-negative bacteria as well as Candida albicans , with consistently lower MIC and MBC/MFC values observed for the high-pressure extract. Cytotoxic evaluation against SKOV3 ovarian cancer cells revealed a clear dose-dependent reduction in cell viability. The calculated IC₅₀ values further confirm the greater cytotoxic potency of extract at 550 bar, with a lower IC₅₀ (153.04 ± 0.4 µg/mL) compared to extract at 200 bar (183.18 ± 2.29 µg/mL) against SKOV3 cells. However, flow cytometry revealed that extract at 200 bar produced slightly higher early apoptosis (43.79% vs. 43.39%) and greater necrosis (21.83% vs. 13.29%) in SKOV3 ovarian cells overall. Anti-inflammatory activity, assessed by protein denaturation inhibition, was observed for both extracts, with the lower-pressure extract showing slightly stronger inhibitory efficiency. Pressure-dependent supercritical CO₂ extraction significantly influenced the phytochemical richness and biological performance of H. arbainense . These findings highlight the importance of extraction conditions in maximizing the functional potential of plant-derived bioactive compounds and support further investigation of H. arbainense as a source of biologically active phenolics.\n\nCandidates:\nA. Extractions of phenolic and flavonoid were performed at two pressures (201 and 550 bar), by using Supercritical CO₂.\nB. Phenolic and flavonoid compounds, were determined by HPLC analysis.\nC. Higher pressure extraction (551 bar) resulted in a greater extraction yield and a marked enrichment of phenolic and flavonoid compounds.\nD. Phenolic and flavonoid compounds, were not determined by HPLC analysis.\nE. Extractions of phenolic and flavonoid were performed at two pressures (200 and 550 bar), by using Supercritical CO₂.\nF. Supercritical CO₂ extraction was applied to Heliotropium arbainense to evaluate how extraction pressure influences phytochemical composition and biological activity.\nG. Supercritical CO₂ extraction was not applied to Heliotropium arbainense to evaluate how extraction pressure influences phytochemical composition and biological activity.\nH. Higher pressure extraction (550 bar) resulted in a greater extraction yield and a marked enrichment of phenolic and flavonoid compounds.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13222921", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13222921/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0f288842d473cd61eb1d", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis study evaluated the anti-acne potential of extracellular proteins derived from Lactobacillus gasseri (LG-EPs) by integrating physicochemical characterization, antibacterial assessment, cellular assays, and an in vivo sebaceous gland model. LG-EPs were obtained by ammonium sulfate precipitation and characterized using gel permeation chromatography/light scattering (GPC/LS) and liquid chromatography–tandem mass spectrometry (LC–MS/MS). The molecular weight of LG-EPs was mainly distributed between 1.0 × 10⁴ and 2.0 × 10⁴ g/mol, and peptide analysis identified four peptides with antimicrobial potential and twelve peptides with antioxidant potential. LG-EPs exhibited direct antibacterial activity against acne-associated bacteria, with MIC and MBC values of 600 µg/mL against Cutibacterium acnes and MIC and MBC values of 700 µg/mL and 1 mg/mL, respectively, against Staphylococcus aureus . Growth-curve and biofilm adhesion assays further showed that LG-EPs inhibited bacterial proliferation and adhesion. In LPS-stimulated HaCaT cells, LG-EPs reduced the secretion of pro-inflammatory cytokines, including IL-6, IL-8, IL-1β, and TNF-α, while increasing the expression of barrier-related factors such as AQP3, FLG, and LOR. In a golden hamster model, topical LG-EP treatment decreased sebum production and downregulated the transcription of lipogenesis-related genes, including SREBP-1, FAS, and ACC1. These effects were associated with reduced transcript levels of PI3K, AKT, and mTOR, while the anti-sebum effect was accompanied by increased AMPK transcription. Overall, LG-EPs showed multi-target anti-acne potential through antibacterial, anti-inflammatory, barrier-protective, and sebum-suppressive effects.\n\nCandidates:\nA. LG-EPs were not obtained by ammonium sulfate precipitation and characterized using gel permeation chromatography/light scattering (GPC/LS) and liquid chromatography–tandem mass spectrometry (LC–MS/MS).\nB. LG-EPs exhibited direct antibacterial activity against acne-associated bacteria, with MIC and MBC values of 600 µg/mL against Cutibacterium acnes and MIC and MBC values of 700 µg/mL and 1 mg/mL, respectively, against Staphylococcus aureus .\nC. LG-EPs were obtained by ammonium sulfate precipitation and characterized using gel permeation chromatography/light scattering (GPC/LS) and liquid chromatography–tandem mass spectrometry (LC–MS/MS).\nD. This study evaluated the anti-acne potential of extracellular proteins derived from Lactobacillus gasseri (LG-EPs) by integrating physicochemical characterization, antibacterial assessment, cellular assays, and an in vivo sebaceous gland model.\nE. The molecular weight of LG-EPs was mainly distributed between 2.0 × 10⁴ and 2.0 × 10⁴ g/mol, and peptide analysis identified four peptides with antimicrobial potential and twelve peptides with antioxidant potential.\nF. LG-EPs exhibited direct antibacterial activity against acne-associated bacteria, with MIC and MBC values of 601 µg/mL against Cutibacterium acnes and MIC and MBC values of 700 µg/mL and 1 mg/mL, respectively, against Staphylococcus aureus .\nG. The evidence does not state that this study evaluated the anti-acne potential of extracellular proteins derived from Lactobacillus gasseri (LG-EPs) by integrating physicochemical characterization, antibacterial assessment, cellular assays, and an in vivo sebaceous gland model.\nH. The molecular weight of LG-EPs was mainly distributed between 1.0 × 10⁴ and 2.0 × 10⁴ g/mol, and peptide analysis identified four peptides with antimicrobial potential and twelve peptides with antioxidant potential.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13222925", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13222925/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-80ae2c7e457a12be51ec", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThis review develops a region-specific Multi-Criteria Decision Analysis (MCDA)-based screening framework for evaluating food-processing residues for biochar applications, offering a transparent and replicable decision-support tool for policymakers and bioeconomy stakeholders in Turkey and beyond. Using Turkey as a region-specific case context, ten underutilized residues—boza fermentation residue, tarhana fines, rosehip seed cake, mulberry syrup press-cake, carob syrup pulp residue, pumpkin seed oil cake, saffron floral by-products, fig-jam seed fraction, lupin brining sediment, and date syrup filter cake—were compiled from the literature and characterized in terms of moisture, ash, organic fractions, higher heating value, and macro-mineral composition. Drawing on thermochemical fundamentals, the review synthesizes how these traits influence biochar properties relevant to fuel use, soil amendment, pollutant adsorption, anaerobic digestion (AD) enhancement, and composite materials, and qualitatively links residue groups to suitable conversion windows such as hydrothermal carbonization and low- or high-severity slow pyrolysis. To convert this information into a transparent screening tool, all indicators were normalized via min–max transformation and aggregated into four mechanistic proxies capturing fuel quality, nutrient release, an adsorption-oriented screening proxy, and AD compatibility. A Simple Additive Weighting (SAW) method was then used to calculate 0–1 suitability scores and 0–100 indices for five application domains: fuel, soil amendment, adsorption/remediation, AD enhancement, and composite/material use. Under the selected criteria and weighting assumptions, rosehip seed cake, pumpkin seed oil cake, carob syrup pulp residue, and fig-jam seed fraction emerged as comparatively high-priority feedstocks, whereas saffron floral by-products and lupin brining sediment showed consistently low relative suitability. By linking feedstock chemistry to application-oriented screening scores, the framework supports rapid comparison of residue-to-application pathways while acknowledging that rankings may evolve as additional performance data, logistical constraints, or alternative weighting scenarios are incorporated. The online version contains supplementary material available at 10.1186/s40643-026-01067-8. Keywords: Biochar, Pyrolysis, Food-processing residues, Multi-criteria decision analysis (MCDA), Circular bioeconomy\n\nCandidates:\nA. To convert this information into a transparent screening tool, all indicators were not normalized via min–max transformation and aggregated into four mechanistic proxies capturing fuel quality, nutrient release, an adsorption-oriented screening proxy, and AD compatibility.\nB. This review develops a region-specific Multi-Criteria Decision Analysis (MCDA)-based screening framework for evaluating food-processing residues for biochar applications, offering a transparent and replicable decision-support tool for policymakers and bioeconomy stakeholders in Turkey and beyond.\nC. To convert this information into a transparent screening tool, all indicators were normalized via min–max transformation and aggregated into four mechanistic proxies capturing fuel quality, nutrient release, an adsorption-oriented screening proxy, and AD compatibility.\nD. The evidence does not state that using Turkey as a region-specific case context, ten underutilized residues—boza fermentation residue, tarhana fines, rosehip seed cake, mulberry syrup press-cake, carob syrup pulp residue, pumpkin seed oil cake, saffron floral by-products, fig-jam seed fraction, lupin brining sediment, and date syrup filter cake—were compiled from the literature and characterized in terms of moisture, ash, organic fractions, higher heating value, and macro-mineral composition.\nE. The evidence does not state that this review develops a region-specific Multi-Criteria Decision Analysis (MCDA)-based screening framework for evaluating food-processing residues for biochar applications, offering a transparent and replicable decision-support tool for policymakers and bioeconomy stakeholders in Turkey and beyond.\nF. The evidence does not state that drawing on thermochemical fundamentals, the review synthesizes how these traits influence biochar properties relevant to fuel use, soil amendment, pollutant adsorption, anaerobic digestion (AD) enhancement, and composite materials, and qualitatively links residue groups to suitable conversion windows such as hydrothermal carbonization and low- or high-severity slow pyrolysis.\nG. Using Turkey as a region-specific case context, ten underutilized residues—boza fermentation residue, tarhana fines, rosehip seed cake, mulberry syrup press-cake, carob syrup pulp residue, pumpkin seed oil cake, saffron floral by-products, fig-jam seed fraction, lupin brining sediment, and date syrup filter cake—were compiled from the literature and characterized in terms of moisture, ash, organic fractions, higher heating value, and macro-mineral composition.\nH. Drawing on thermochemical fundamentals, the review synthesizes how these traits influence biochar properties relevant to fuel use, soil amendment, pollutant adsorption, anaerobic digestion (AD) enhancement, and composite materials, and qualitatively links residue groups to suitable conversion windows such as hydrothermal carbonization and low- or high-severity slow pyrolysis.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13222927", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13222927/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0c3e0d2be045b8ca717e", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nFungal solid-state fermentation can improve the nutritional quality of plant-based foods, yet most processes rely on substrates whose properties are inherited from raw materials rather than intentionally designed. This perspective addresses this limitation by reframing the substrate as an active process component. Integrating biochemical, mechanical, and architectural principles into substrate design enables the development of engineered substrates that promote volumetric and spatially uniform bioconversion during solid-state fermentation of food materials.\n\nCandidates:\nA. The evidence does not state that integrating biochemical, mechanical, and architectural principles into substrate design enables the development of engineered substrates that promote volumetric and spatially uniform bioconversion during solid-state fermentation of food materials.\nB. Fungal solid-state fermentation can improve the nutritional quality of plant-based foods, yet most processes rely on substrates whose properties are inherited from raw materials rather than intentionally designed.\nC. Fungal solid-state fermentation can improve the nutritional quality of plant-based foods, yet most processes rely on substrates whose properties are not inherited from raw materials rather than intentionally designed.\nD. This perspective addresses this limitation by reframing the substrate as an active process component.\nE. The evidence does not state that this perspective addresses this limitation by reframing the substrate as an active process component.\nF. Integrating biochemical, mechanical, and architectural principles into substrate design enables the development of engineered substrates that promote volumetric and spatially uniform bioconversion during solid-state fermentation of food materials.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13223221", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13223221/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2b05ac0782361780f865", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe ubiquitin-like domain 2 (Ubl2) of the SARS-CoV-2 virus is necessary for the stability and catalytic efficiency of its papain-like protease (PLpro). Our crystallographic study reveals that the Ubl2 domain exhibits notable flexibility and can adopt a conformation that places itself away from the PLpro catalytic domain, representing a new conformation from those reported so far for SARS-CoV-1 or SARS-CoV-2. The structural flexibility of Ubl2 could be allosterically related to the stability of the zinc-finger domain.\n\nCandidates:\nA. Our crystallographic study reveals that the Ubl3 domain exhibits notable flexibility and can adopt a conformation that places itself away from the PLpro catalytic domain, representing a new conformation from those reported so far for SARS-CoV-1 or SARS-CoV-2.\nB. The ubiquitin-like domain 2 (Ubl2) of the SARS-CoV-2 virus is necessary for the stability and catalytic efficiency of its papain-like protease (PLpro).\nC. The structural flexibility of Ubl3 could be allosterically related to the stability of the zinc-finger domain.\nD. The structural flexibility of Ubl2 could be allosterically related to the stability of the zinc-finger domain.\nE. Our crystallographic study reveals that the Ubl2 domain exhibits notable flexibility and can adopt a conformation that places itself away from the PLpro catalytic domain, representing a new conformation from those reported so far for SARS-CoV-1 or SARS-CoV-2.\nF. The ubiquitin-like domain 3 (Ubl2) of the SARS-CoV-2 virus is necessary for the stability and catalytic efficiency of its papain-like protease (PLpro).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13224804", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13224804/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-207d3e7ea2af27895c63", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"6.49 g/L\", \"5.46 g/L\", \"4.46 g/L\", \"4.82 g/L\", \"49.8 g/L\", \"5.82 g/L\", \"50.8 g/L\", \"5.49 g/L\"]\n\nEvidence:\nl -Histidine is an essential amino acid with important applications in pharmaceuticals and nutrition, highlighting the demand for efficient microbial production platforms. This study developed a high-performance Escherichia coli cell factory through systematic metabolic engineering. First, we mined a feedback-resistant hisG∗ smar and the entire mutant his operon from a previously obtained high l -histidine-producing mutant of Serratia marcescens , offering novel enzymatic parts beyond conventional sources. Combined with precursor supply enhancement and redox balancing, the engineered strain yielded 4.46 g/L l -histidine. Second, a machine learning-based platform (TransDW) was utilized to predict and validate a novel efflux transporter, Cgl1374, increasing titer to 4.82 g/L. Third, we implemented a growth phase-dependent system to dynamically regulate pgi expression, redirecting carbon flux and achieving 5.49 g/L in shake flasks. Finally, applying a novel carbon evolution rate (CER)-based control strategy in fed-batch fermentation, the optimized strain achieved 49.8 g/L of l -histidine in a 5-L bioreactor, with a yield of 0.265 g/g glucose, which is the highest yield reported for engineered E. coli . This work establishes a synergistic framework combining non-model gene discovery, computational transport engineering, and real-time physiological feedback control, providing a versatile blueprint for next-generation microbial cell factories.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13226134", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13226134/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7703791c6afe843763f7", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nAntimicrobial resistance (AMR) and emerging contaminants (ECs), including pharmaceuticals, personal care products, microplastics, and endocrine-disrupting chemicals, pose interconnected threats to environmental and human health. Nature-based solutions (NbS) have emerged as sustainable and cost-effective approaches for mitigating these challenges through ecosystem-driven processes. This review follows a PRISMA-guided narrative-systematic synthesis of literature published between 2000 and 2024, using data sources including Scopus, Web of Science, and PubMed. The analysis integrates evidence on microbial mechanisms, NbS platform performance, and environmental AMR-EC interactions. The synthesis highlights that microbial-driven NbS exploit metabolic diversity, functional plasticity, and plant-microbe interactions to degrade, transform, immobilize, or eliminate contaminants in soil, water, and wastewater systems. Advances in microbial ecology, synthetic biology, and omics approaches have enabled the design of functional microbial consortia capable of targeting antibiotic residues, resistance genes, and recalcitrant pollutants. NbS platforms such as constructed wetlands, rhizosphere-based systems, biofilters, and microbial electrochemical technologies demonstrate variable performance influenced by microbial diversity, redox processes, and system design. However, trade-offs exist, including the potential for microbial biofilms to act as reservoirs of antibiotic resistance genes. Despite their potential, microbial-driven NbS face challenges related to scalability, long-term performance, ecological risks, and regulatory acceptance. This review proposes a microbial NbS decision framework linking environmental sources, microbial mechanisms, platform design, and monitoring indicators to support sustainable and risk-aware implementation. Overall, the effectiveness of NbS depends on optimizing microbial functional diversity, system design, and resistance suppression strategies to ensure long-term environmental and public health benefits.\n\nCandidates:\nA. This review follows a PRISMA-guided narrative-systematic synthesis of literature published between 2001 and 2024, using data sources including Scopus, Web of Science, and PubMed.\nB. Nature-based solutions (NbS) have emerged as sustainable and cost-effective approaches for mitigating these challenges through ecosystem-driven processes.\nC. The analysis integrates evidence on microbial mechanisms, NbS platform performance, and environmental AMR-EC interactions.\nD. Antimicrobial resistance (AMR) and emerging contaminants (ECs), including pharmaceuticals, personal care products, microplastics, and endocrine-disrupting chemicals, pose interconnected threats to environmental and human health.\nE. This review follows a PRISMA-guided narrative-systematic synthesis of literature published between 2000 and 2024, using data sources including Scopus, Web of Science, and PubMed.\nF. The evidence does not state that nature-based solutions (NbS) have emerged as sustainable and cost-effective approaches for mitigating these challenges through ecosystem-driven processes.\nG. The evidence does not state that the analysis integrates evidence on microbial mechanisms, NbS platform performance, and environmental AMR-EC interactions.\nH. The evidence does not state that antimicrobial resistance (AMR) and emerging contaminants (ECs), including pharmaceuticals, personal care products, microplastics, and endocrine-disrupting chemicals, pose interconnected threats to environmental and human health.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13226581", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13226581/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-623437d775a08b23e3ee", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe Yemeni Music Genres Dataset (YMGD) is the first curated benchmark for Arabic Yemeni musical traditions, comprising a balanced collection of five genres, Sana'ani, Hadhrami, Tihami, Lahji, and Adeni, with 230 audio recordings per class, each standardized to 30 s. In addition to the audio content, the dataset includes comprehensive metadata for each track, including artist name, genre label, and title, enabling structured analysis and reproducibility. The dataset was manually collected from publicly available sources, primarily YouTube, and subsequently annotated by domain experts in Yemeni music to ensure high labeling fidelity. Inter-annotator agreement was quantified using Fleiss’ Kappa, yielding a score of 0.85, indicating strong consistency and reliability in the annotation process. This dataset provides a robust foundation for a wide range of research applications, including music information retrieval, the development and evaluation of machine learning models for genre classification, recommendation systems, and computational cultural analysis. By combining expert validation with balanced representation and standardized preprocessing, it establishes a high-quality benchmark resource for both research and educational use in low-resource musical domains. The dataset will be accessible at the following link: https://doi.org/10.5281/zenodo.19543208 . Keywords: Data science, Music genres classification, Spectrogram analysis, Dataset benchmarking, Yemeni music dataset\n\nCandidates:\nA. The Yemeni Music Genres Dataset (YMGD) is the first curated benchmark for Arabic Yemeni musical traditions, comprising a balanced collection of five genres, Sana'ani, Hadhrami, Tihami, Lahji, and Adeni, with 230 audio recordings per class, each standardized to 30 s.\nB. Inter-annotator agreement was quantified using Fleiss’ Kappa, yielding a score of 1.85, indicating strong consistency and reliability in the annotation process.\nC. Inter-annotator agreement was quantified using Fleiss’ Kappa, yielding a score of 0.85, indicating strong consistency and reliability in the annotation process.\nD. The evidence does not state that in addition to the audio content, the dataset includes comprehensive metadata for each track, including artist name, genre label, and title, enabling structured analysis and reproducibility.\nE. The dataset was manually collected from publicly available sources, primarily YouTube, and subsequently annotated by domain experts in Yemeni music to ensure high labeling fidelity.\nF. The dataset was not manually collected from publicly available sources, primarily YouTube, and subsequently annotated by domain experts in Yemeni music to ensure high labeling fidelity.\nG. The Yemeni Music Genres Dataset (YMGD) is the first curated benchmark for Arabic Yemeni musical traditions, comprising a balanced collection of five genres, Sana'ani, Hadhrami, Tihami, Lahji, and Adeni, with 231 audio recordings per class, each standardized to 30 s.\nH. In addition to the audio content, the dataset includes comprehensive metadata for each track, including artist name, genre label, and title, enabling structured analysis and reproducibility.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13226877", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13226877/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0d2e1aec579de54b5df9", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPlastic pollution originating from fossil-based materials persists as a critical environmental concern due to their resistance to degradation, as well as their role in the build-up of microplastics and the release of greenhouse gases across waste-management processes. Polyhydroxyalkanoates (PHAs) are considered effective, biodegradable and bio-based alternatives. However, financial and process constraints limit the industrial-scale use of PHAs despite considerable investigations. Several bacteria can synthesise PHAs using different substrates and store them as granules under nutrient-limited conditions. Bacterial species such as Bacillus , Pseudomonas , Halomonas and Cupriavidus necator have been used to produce PHAs. This study provides an analytical and comprehensive evaluation of PHAs as multifaceted biopolymers, evaluating existing developments and highlighting major constraints. This review evaluates comparatively different feedstocks for microbial production and optimisation approaches, highlighting their effectiveness and limitations. It further evaluates developments in metabolic engineering, including CRISPR/Cas9-mediated genome editing systems, bridge recombination, and synthetic biological engineering, which aim to increase yield and modify monomer characteristics. Moreover, it evaluates downstream processing techniques, such as chemical, enzymatic, mechanical and biological extraction methods, emphasising their sustainability and scalability. Significantly, this review highlights key barriers to the large-scale production of PHA, such as high manufacturing costs, variation in raw materials, and operational limitations while suggesting a strategic pathway for future research. Finally, this paper offers an integrated viewpoint that connects the production, properties, and applications of PHAs, providing the understanding necessary to enhance their relevance as sustainable alternatives to conventional plastics.\n\nCandidates:\nA. Several bacteria can synthesise PHAs using different substrates and store them as granules under nutrient-limited conditions.\nB. However, financial and process constraints limit the industrial-scale use of PHAs despite considerable investigations.\nC. Several bacteria cannot synthesise PHAs using different substrates and store them as granules under nutrient-limited conditions.\nD. Plastic pollution originating from fossil-based materials persists as a critical environmental concern due to their resistance to degradation, as well as their role in the build-up of microplastics and the release of greenhouse gases across waste-management processes.\nE. The evidence does not state that plastic pollution originating from fossil-based materials persists as a critical environmental concern due to their resistance to degradation, as well as their role in the build-up of microplastics and the release of greenhouse gases across waste-management processes.\nF. Polyhydroxyalkanoates (PHAs) are not considered effective, biodegradable and bio-based alternatives.\nG. The evidence does not state that however, financial and process constraints limit the industrial-scale use of PHAs despite considerable investigations.\nH. Polyhydroxyalkanoates (PHAs) are considered effective, biodegradable and bio-based alternatives.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13227510", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13227510/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e414f2bbbd52db31bd25", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nPlants represent commercially relevant production systems for recombinant proteins and chemical compounds. Effective genetic engineering depends on precise control of heterologous gene expression, which remains challenging due to complex transcriptional and post-transcriptional regulation, endogenous gene silencing mechanisms, and notably because of limited number of tools for allowing robust, fine-tuned control of expression levels across different systems/organisms. Some of the most common issues associated with plant expression systems are addressed with our plant-optimized version of a previously developed fungal universal synthetic expression system (SES). Plant SES demonstrates several favorable characteristics for robust heterologous gene expression, including highly constitutive function with apparently reduced sensitivity to endogenous silencing in transient assays, without requiring p19 co‑expression under the tested conditions. These together provide simple, predictable tuning of gene expression levels, and the potential for very high expression levels of the target genes. In all these features, SES shows higher and more stable transcript levels than Cauliflower Mosaic Virus (CaMV) 35 S promoter-based constructs in our experimental setups. The functionality of plant SES was tested by expressing mCherry and three commercially relevant proteins: fungal glucose oxidase (GOX), protein A and human vascular endothelial growth factor 165 (VEGF16) from diverse organisms, supporting high-level accumulation of recombinant proteins. In addition, plant SES retains full functionality in both plant and fungal hosts, which makes this expression system a useful tool for a multitude of genetic engineering applications in other eukaryotic organisms. The online version contains supplementary material available at 10.1007/s11103-026-01725-7.\n\nCandidates:\nA. The evidence does not state that effective genetic engineering depends on precise control of heterologous gene expression, which remains challenging due to complex transcriptional and post-transcriptional regulation, endogenous gene silencing mechanisms, and notably because of limited number of tools for allowing robust, fine-tuned control of expression levels across different systems/organisms.\nB. Plant SES demonstrates several favorable characteristics for robust heterologous gene expression, including highly constitutive function with apparently reduced sensitivity to endogenous silencing in transient assays, without requiring p19 co‑expression under the tested conditions.\nC. Plants represent commercially relevant production systems for recombinant proteins and chemical compounds.\nD. Some of the most common issues associated with plant expression systems are addressed with our plant-optimized version of a previously developed fungal universal synthetic expression system (SES).\nE. Effective genetic engineering depends on precise control of heterologous gene expression, which remains challenging due to complex transcriptional and post-transcriptional regulation, endogenous gene silencing mechanisms, and notably because of limited number of tools for allowing robust, fine-tuned control of expression levels across different systems/organisms.\nF. Some of the most common issues associated with plant expression systems are not addressed with our plant-optimized version of a previously developed fungal universal synthetic expression system (SES).\nG. Plant SES demonstrates several favorable characteristics for robust heterologous gene expression, including highly constitutive function with apparently reduced sensitivity to endogenous silencing in transient assays, without requiring p20 co‑expression under the tested conditions.\nH. The evidence does not state that plants represent commercially relevant production systems for recombinant proteins and chemical compounds.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13230335", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13230335/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-41a18a57151ee061ca98", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"192 h\", \"31.29%\", \"32.29%\", \"193 h\", \"92%\", \"93%\"]\n\nEvidence:\nPolyurethane (PU) is an industrially versatile polymer whose environmental persistence pose significant waste management challenges. Although several PU-degrading microorganisms have been reported, studies evaluating degradation across chemically distinct PU substrate remain limited. The present study explores PU biodegradation potential of a soil-borne fungus Fusarium parceramosum , with two chemically different PU substrates—Impranil (PU dispersion), and PU foam—under unoptimized conditions. The fungus achieved 92% degradation of Impranil within 192 h, and 31.29% weight loss of PU foam over 60 d. Degradation kinetics were evaluated using zero-order, first-order, Langmuir, and Freundlich models, with zero-and first-order models showing best fit with experimental data. Enzymatic assay revealed significant urease and aliphatic carbamate-hydrolyzing activities associated with degradation. For Impranil, urease and aliphatic carbamate-hydrolyzing activities were 202.03 U/mL and 412.47 U/mL, respectively, whereas for PU foam they were 338.12 U/mL and 284.17 U/mL, respectively. Structural and chemical alterations were confirmed through SEM, FTIR, XRD and GC–MS analysis. Metabolite identification indicated the formation of degradation intermediates, suggesting their possible involvement in subsequent microbial processes. Overall, this study provides the first evidence of dual-substrate specificity of Fusarium parceramosum highlighting its potential in sustainable waste management approaches. Keywords: Polyurethane, Fusarium parceramosum , Biodegradation, Urease, Aliphatic carbamate hydrolyzing activity, Physicochemical characterization, Kinetic modelling, Metabolic pathway", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13230457", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13230457/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c7e7af287dcb6d72a74c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMyotonic dystrophy type 1 (DM1) is caused by an expanded CTG repeat in the DMPK gene, resulting in mutant transcripts that form expanded CUG (CUG exp ) RNA foci and sequester muscleblind-like (MBNL) RNA-binding proteins. DM1 is multisystemic, with progressive worsening of disease manifestations in affected tissues. Disease progression is attributed to somatic expansion of the CTG repeats with age, resulting in production of CUG exp RNA with enhanced intrinsic toxicity due to increased MBNL sequestration. To determine the degree to which cardiac disease progression can occur independently of repeat expansion, we used a transgenic DM1 mouse model with inducible heart-specific expression of a stable, interrupted 960-CUG-repeat RNA. Sustained CUG exp RNA expression caused progressive cardiac enlargement, contractile dysfunction, conduction delay, myocardial fibrosis, and reduced survival, while MBNL-dependent splicing defects remained static, consistent with the stable repeat length. We also determined the degree of reversibility after different periods of CUG exp RNA expression by shutting off the repeat-containing transgene. Suppression of CUG exp RNA expression rescued cardiac abnormalities, but reversibility declined with longer exposure to the toxic RNA. These findings demonstrate that prolonged expression of stable CUG exp RNA drives progressive cardiac pathology, revealing a mechanism of disease progression in DM1 in addition to somatic expansion. Keywords: Cardiology, Genetics Keywords: Cardiovascular disease, Genetic diseases, Molecular biology\n\nCandidates:\nA. Myotonic dystrophy type 2 (DM1) is caused by an expanded CTG repeat in the DMPK gene, resulting in mutant transcripts that form expanded CUG (CUG exp ) RNA foci and sequester muscleblind-like (MBNL) RNA-binding proteins.\nB. Myotonic dystrophy type 1 (DM1) is caused by an expanded CTG repeat in the DMPK gene, resulting in mutant transcripts that form expanded CUG (CUG exp ) RNA foci and sequester muscleblind-like (MBNL) RNA-binding proteins.\nC. Disease progression is attributed to somatic expansion of the CTG repeats with age, resulting in production of CUG exp RNA with enhanced intrinsic toxicity due to increased MBNL sequestration.\nD. To determine the degree to which cardiac disease progression can occur independently of repeat expansion, we used a transgenic DM2 mouse model with inducible heart-specific expression of a stable, interrupted 960-CUG-repeat RNA.\nE. To determine the degree to which cardiac disease progression can occur independently of repeat expansion, we used a transgenic DM1 mouse model with inducible heart-specific expression of a stable, interrupted 960-CUG-repeat RNA.\nF. DM1 is multisystemic, with progressive worsening of disease manifestations in affected tissues.\nG. Disease progression is not attributed to somatic expansion of the CTG repeats with age, resulting in production of CUG exp RNA with enhanced intrinsic toxicity due to increased MBNL sequestration.\nH. DM2 is multisystemic, with progressive worsening of disease manifestations in affected tissues.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13232024", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13232024/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4c8202910a466a22926f", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe functional and structural reconstruction of the tendon-bone interface (TBI) is a major challenge in orthopedics and sports medicine. Under the influence of chronic degenerative pathologies such as aging, diabetes, and rheumatoid arthritis, the cascading collapse of the local immune-metabolic network disrupts the regenerative microenvironment of the tissue, making the clinical translation of traditional inert physical scaffolds extremely difficult. This review systematically summarizes the latest paradigm shifts in “bone immunoengineering” aimed at overcoming the complex challenges of TBI regeneration. We first decode the core regulatory networks that control interface heterogeneity remodeling, thoroughly analyzing the spatiotemporal polarization dynamics of macrophages, the double-edged effects of the Piezo1-YAP mechanotransduction axis, and the “neuro-immune-skeletal” ternary communication mechanism. Based on this pathological framework, we comprehensively overview next-generation intelligent biophysical and chemical intervention strategies for actively reprogramming extreme microenvironments. These strategies include piezoelectric nanohydrogels for electromechanical-metabolic coupling, Janus asymmetric microfluidic interfaces for multi-ion spatiotemporal rectification, and precise spatial delivery platforms for targeted clearance of senescent cells and engineered exosomes. Furthermore, to overcome the translational barriers between underlying mechanisms and clinical applications, we focus on the cross-scale evolution of preclinical evaluation systems, elaborating on the core value of three-dimensional tendon-bone organoids, microfluidic organ-on-chip systems, and high-resolution spatial transcriptomics. Finally, this review envisions advanced microphysiological systems characterized by closed-loop dynamic adaptive biomaterials, spatiotemporal matching of degradation kinetics, and deep integration with artificial intelligence (AI), highlighting their broad prospects in driving the next-generation of personalized, precise regenerative medicine in orthopedics.\n\nCandidates:\nA. We first decode the core regulatory networks that control interface heterogeneity remodeling, thoroughly analyzing the spatiotemporal polarization dynamics of macrophages, the double-edged effects of the Piezo1-YAP mechanotransduction axis, and the “neuro-immune-skeletal” ternary communication mechanism.\nB. The evidence does not state that under the influence of chronic degenerative pathologies such as aging, diabetes, and rheumatoid arthritis, the cascading collapse of the local immune-metabolic network disrupts the regenerative microenvironment of the tissue, making the clinical translation of traditional inert physical scaffolds extremely difficult.\nC. We first decode the core regulatory networks that control interface heterogeneity remodeling, thoroughly analyzing the spatiotemporal polarization dynamics of macrophages, the double-edged effects of the Piezo2-YAP mechanotransduction axis, and the “neuro-immune-skeletal” ternary communication mechanism.\nD. The evidence does not state that this review systematically summarizes the latest paradigm shifts in “bone immunoengineering” aimed at overcoming the complex challenges of TBI regeneration.\nE. The functional and structural reconstruction of the tendon-bone interface (TBI) is not a major challenge in orthopedics and sports medicine.\nF. Under the influence of chronic degenerative pathologies such as aging, diabetes, and rheumatoid arthritis, the cascading collapse of the local immune-metabolic network disrupts the regenerative microenvironment of the tissue, making the clinical translation of traditional inert physical scaffolds extremely difficult.\nG. This review systematically summarizes the latest paradigm shifts in “bone immunoengineering” aimed at overcoming the complex challenges of TBI regeneration.\nH. The functional and structural reconstruction of the tendon-bone interface (TBI) is a major challenge in orthopedics and sports medicine.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13233466", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13233466/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b655f457011b976f6ae4", "task": "text_evidence_extraction", "prompt": "Some candidate values differ from the evidence by only one number. Select only the strings that appear verbatim, preserving candidate order. Return JSON using exactly this schema: {\"reported_values\":[\"candidate string\"]}. Candidates: [\"63%\", \"501 µg/mL\", \"80.39%\", \"49 h\", \"48 h\", \"500 µg/mL\", \"64%\", \"79.39%\"]\n\nEvidence:\nUltraviolet B (UVB) radiation is the primary cause of photodamage to the skin, triggering the oxidative stress, mitochondrial dysfunction, and impaired barrier integrity. We extracted polysaccharides from Passiflora edulis Sims peel via microbial fermentation (PP-FP) and characterized their structural properties using fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and molecular weight analyses. Photoprotective effects were evaluated in UVB- irradiated HaCaT cells and BALB/c mice dorsal skin by measuring of oxidative stress markers, mitochondrial function, DNA damage, and skin barrier proteins. PP-FP contains a furanose ring structure, exhibits an irregular, loosely layered morphology with attached filamentous and porous structures, and shows a number-average molecular weight (Mn) of 3.519 kDa and a weight-average molecular weight (Mw) of 6.66 kDa. In UVB-irradiated HaCaT cells, PP-FP (500 µg/mL) significantly enhanced cell migration capacity (achieving 79.39% wound closure at 48 h), reduced reactive oxygen species (ROS) accumulation, alleviated mitochondrial membrane potential aberrations, and decreased interleukin-17 (IL-17) release by 63%. Concurrently, it markedly elevated key skin barrier protein levels in HaCaT cells, notably enhancing filaggrin (FLG, 1.82-fold) and lamellar oligomerizing protein (LOR, 2.04-fold) expression. Furthermore, PP-FP maintained epidermal thickness, suppressed formation of DNA damage marker γ-H2AX, downregulated matrix metalloproteinase-3/9 (MMP3/9) expression by 40–50%, and effectively preserving epidermal barrier function. This study demonstrates that fermentation-derived Passiflora edulis Sims polysaccharides mitigate UVB damage through a dual mechanisms—antioxidant protection and barrier reinforcement—providing a sustainable strategy to repurpose agricultural byproducts for high-efficacy skincare formulations.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13234045", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13234045/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cd85e15874a99ac9fa45", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTo address the challenges of high-density animal cell culture, this study developed a high-density culture system comprising a single-use bioreactor (SUB) with a nominal volume of 500 mL and a pulsed tangential flow filtration (ITF) unit. The reactor can be configured with two layers of 35 mm diameter impellers—either double Elephant Ear (EE-EE) or Elephant Ear combined with Ribbon (EE-RB). The flow field characteristics were rigorously characterized through CFD simulations (60–240 rpm; 90–480 mL) validated by experimental data. Engineering analysis revealed the system’s robust culture environment: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${k}_{L}a$$\\end{document} values ranging from 2 to 15 h −1 (60–180 rpm; 200–400 mL; 30–150 mL/min aeration), \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P/V$$\\end{document} values from 0.1 to 10 W/m 3 , and mixing times between 1 and 10 s. Crucially, the system maintains a mild shear environment with an average shear strain rate (SSR) below 25 s −1 , fully within the physiological tolerance range for mammalian cells. This low-shear, high-mass-transfer design was validated through perfusion cultures of CHO and HEK293 cells. The system achieved exceptionally high cell densities of 8.32 × 10 7 cells/mL and 1.17 × 10 8 cells/mL, respectively, with cell viability consistently exceeding 90%, demonstrating its suitability for high-intensity biopharmaceutical production. The online version contains supplementary material available at 10.1186/s40643-026-01077-6.\n\nCandidates:\nA. The flow field characteristics were rigorously characterized through CFD simulations (60–240 rpm; 90–480 mL) validated by experimental data.\nB. The reactor can be configured with two layers of 35 mm diameter impellers—either double Elephant Ear (EE-EE) or Elephant Ear combined with Ribbon (EE-RB).\nC. Engineering analysis revealed the system’s robust culture environment: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${k}_{L}a$$\\end{document} values ranging from 2 to 15 h −1 (60–180 rpm; 200–400 mL; 30–150 mL/min aeration), \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P/V$$\\end{document} values from 0.1 to 10 W/m 3 , and mixing times between 1 and 10 s.\nD. Engineering analysis revealed the system’s robust culture environment: \\documentclass[13pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${k}_{L}a$$\\end{document} values ranging from 2 to 15 h −1 (60–180 rpm; 200–400 mL; 30–150 mL/min aeration), \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P/V$$\\end{document} values from 0.1 to 10 W/m 3 , and mixing times between 1 and 10 s.\nE. To address the challenges of high-density animal cell culture, this study developed a high-density culture system comprising a single-use bioreactor (SUB) with a nominal volume of 500 mL and a pulsed tangential flow filtration (ITF) unit.\nF. To address the challenges of high-density animal cell culture, this study developed a high-density culture system comprising a single-use bioreactor (SUB) with a nominal volume of 501 mL and a pulsed tangential flow filtration (ITF) unit.\nG. The flow field characteristics were rigorously characterized through CFD simulations (61–240 rpm; 90–480 mL) validated by experimental data.\nH. The reactor can be configured with two layers of 36 mm diameter impellers—either double Elephant Ear (EE-EE) or Elephant Ear combined with Ribbon (EE-RB).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13234075", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13234075/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5ea1ff69870536803737", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nEtoposide, a semisynthetic derivative of podophyllotoxin originally isolated from the rhizomes of mayapple ( Podophyllum peltatum ) and indian Podophyllum ( P. hexandrum ) plants, is one of the most powerful chemotherapeutic agents used to treat various types of solid tumors and blood malignancies. Despite its clinical importance, its supply is recurrently constrained due to a heavy reliance on plant extraction, where low natural precursor abundance and increasing climate-related pressures limit production scalability. Developing alternative manufacturing routes has therefore become a major objective, though reconstruction of this complex biosynthetic pathway has long posed significant challenges, even with recent advances in synthetic biology and metabolic engineering. Yeast has emerged as a robust cellular chassis for reconstituting, either partially or entirely, plant secondary metabolite pathways, and enabling cost-effective bioproduction. Here, we established an integrated biotechnological strategy for the sustainable production of advanced etoposide intermediates using engineered yeast cell factories. By combining pathway refactoring, gene copy number optimization, and tailored co-enzyme compatibility, we established an efficient heterologous pathway converting yatein into (−)-4′-desmethyl-epipodophyllotoxin (4′dEPT) in yeast. Iterative strain engineering improved metabolic flux distribution, leading to enhanced titers and accelerated production kinetics, while process engineering proved essential to maximizing overall system performance. Finally, we also demonstrated the viability of coupling bioproduction in cell factories with downstream, semisynthetic conversion by successfully isolating bioreactor-derived 4′dEPT and converting it into etoposide. In parallel, identifying resilient plant resources that can accumulate high levels of YAT provides a complementary strategy for securing the precursor supply at scale. Overall, this report validates the concept of a hybrid etoposide production platform integrating controlled plant biomass sourcing, engineered yeast cell factories, and chemical transformation steps. Keywords: Bioproduction, Cell factories, Metabolic engineering, Bioreactor, Etoposide, Lignan", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13234597", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13234597/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-364a963e8a49318ac8a4", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nHuman buccal mucosa contains epithelial, stromal, and resident immune cell populations, but isolating these cells as a high-viability single-cell suspension is technically challenging. Here, we describe a gentle dissociation workflow for human buccal punch biopsies that combines controlled enzymatic digestion (collagenase and nuclease) with optimized speed, temperature, and incubation time to efficiently release heterogeneous cell populations while minimizing cell damage. The protocol reliably yields a heterogeneous single-cell suspension compatible with flow cytometry and single-cell RNA sequencing, with typical viabilities ≥80%. Subject areas: Health sciences, Clinical protocol, Immunology\n\nCandidates:\nA. The evidence does not state that subject areas: Health sciences, Clinical protocol, Immunology\nB. The protocol reliably yields a heterogeneous single-cell suspension compatible with flow cytometry and single-cell RNA sequencing, with typical viabilities ≥81%.\nC. Human buccal mucosa contains epithelial, stromal, and resident immune cell populations, but isolating these cells as a high-viability single-cell suspension is not technically challenging.\nD. The protocol reliably yields a heterogeneous single-cell suspension compatible with flow cytometry and single-cell RNA sequencing, with typical viabilities ≥80%.\nE. The evidence does not state that here, we describe a gentle dissociation workflow for human buccal punch biopsies that combines controlled enzymatic digestion (collagenase and nuclease) with optimized speed, temperature, and incubation time to efficiently release heterogeneous cell populations while minimizing cell damage.\nF. Human buccal mucosa contains epithelial, stromal, and resident immune cell populations, but isolating these cells as a high-viability single-cell suspension is technically challenging.\nG. Here, we describe a gentle dissociation workflow for human buccal punch biopsies that combines controlled enzymatic digestion (collagenase and nuclease) with optimized speed, temperature, and incubation time to efficiently release heterogeneous cell populations while minimizing cell damage.\nH. Subject areas: Health sciences, Clinical protocol, Immunology", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13235443", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13235443/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aa65a5248a7f69af0dd0", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nEfficient access to soluble recombinant proteins remains a major bottleneck in biochemical and structural studies. We describe an aqueous, solvent-centric, fully miniaturized 96-well workflow to screen extraction conditions that preserve soluble recombinant protein during lysis and clarification in a single working day. Liquid-nitrogen-frozen E. coli pellets are cryogenically bead-milled with stainless-steel beads, retaining the native intracellular milieu while ensuring uniform disruption. The resulting wet, frozen cell powder can therefore be extracted with user-defined solvent, enabling systematic exploration of pH, ionic strength, detergents, and chaotropes. Protein solubility is assessed by a 1 μL chromogenic anti-His dot-blot. We demonstrate the use of the protocol by solubilizing a set of highly challenging de novo -generated proteins and show that dot-blot intensity provides a practical semiquantitative proxy for successful extraction of soluble proteins. We also provide experimentally supported guidelines on the influence of solvent reagents on subsequent steps of protein production, SDS-PAGE, and Ni-NTA purification. This workflow is compatible with upstream genetic solubility-enhancement, chassis- and cultivation-based strategies and enables direct transition from screening hits to scale-up. Because the workflow uses standard molecular biology equipment and inexpensive consumables, it can be readily adopted or automated in most laboratories.\n\nCandidates:\nA. We describe an aqueous, solvent-centric, fully miniaturized 97-well workflow to screen extraction conditions that preserve soluble recombinant protein during lysis and clarification in a single working day.\nB. coli pellets are cryogenically bead-milled with stainless-steel beads, retaining the native intracellular milieu while ensuring uniform disruption.\nC. The evidence does not state that efficient access to soluble recombinant proteins remains a major bottleneck in biochemical and structural studies.\nD. The resulting wet, frozen cell powder can therefore be extracted with user-defined solvent, enabling systematic exploration of pH, ionic strength, detergents, and chaotropes.\nE. coli pellets are not cryogenically bead-milled with stainless-steel beads, retaining the native intracellular milieu while ensuring uniform disruption.\nF. The resulting wet, frozen cell powder cannot therefore be extracted with user-defined solvent, enabling systematic exploration of pH, ionic strength, detergents, and chaotropes.\nG. We describe an aqueous, solvent-centric, fully miniaturized 96-well workflow to screen extraction conditions that preserve soluble recombinant protein during lysis and clarification in a single working day.\nH. Efficient access to soluble recombinant proteins remains a major bottleneck in biochemical and structural studies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13235557", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13235557/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-668bf26fa6fb09783de4", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nNewcastle disease virus (NDV) has been the subject of extensive research as a potential oncolytic virus for various types of cancer. While clinical studies are ongoing, the manufacturing of high NDV doses on a large scale is challenging. It has previously been documented that Vero cells are capable of producing 2.4 × 10 8 TCID 50 /mL in suspension batch production. However, the requirement of higher input doses and the challenging nature of establishing high cell density processes with Vero cells are significant obstacles in the effective utilization of these therapies. In pursuit of enhanced process intensification and the generation of viral vectors at elevated cell densities, EB66 cells have been identified as a highly effective producer cell line. In this study, the characteristics of EB66 cells in regard to NDV production were examined, with particular reference to cell growth and cell-specific virus productivity in batch and semi-perfusion mode. Optimal infection conditions for producing NDV in batch and semi-perfusion modes were identified for cultivation parameters including temperature, protease concentration (TrypLE), and multiplicity of infection. The favorable production conditions were then transferred to different batch processes using a stirred tank bioreactor and an orbital shaken bioreactor. These processes yielded up to 4.2 × 10 8 TCID 50 /mL of the NDV LaSota strain with a cell-specific virus yield of 41 TCID 50 /cell. First semi-perfusion runs resulted in concentrations of 65 × 10 6 cells/mL and an infectious virus titer of 7.5 × 10 8 TCID 50 /mL. Finally, the potency of the produced viruses was evaluated, and a reduction in tumor size in mice after NDV injection was demonstrated. Overall, these results indicate that EB66 cells could be a viable host for producing oncolytic NDV. Keywords: Oncolytic virus production, Process development, Bioreactor, Semi-perfusion\n\nCandidates:\nA. However, the requirement of higher input doses and the challenging nature of establishing high cell density processes with Vero cells are significant obstacles in the effective utilization of these therapies.\nB. While clinical studies are ongoing, the manufacturing of high NDV doses on a large scale is challenging.\nC. Newcastle disease virus (NDV) has been the subject of extensive research as a potential oncolytic virus for various types of cancer.\nD. The evidence does not state that newcastle disease virus (NDV) has been the subject of extensive research as a potential oncolytic virus for various types of cancer.\nE. However, the requirement of higher input doses and the challenging nature of establishing high cell density processes with Vero cells are not significant obstacles in the effective utilization of these therapies.\nF. While clinical studies are ongoing, the manufacturing of high NDV doses on a large scale is not challenging.\nG. It has previously been documented that Vero cells are capable of producing 2.4 × 10 8 TCID 50 /mL in suspension batch production.\nH. It has previously been documented that Vero cells are capable of producing 3.4 × 10 8 TCID 50 /mL in suspension batch production.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13236843", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13236843/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b3f1f3acd6ea4c7757eb", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nTemperature is a key factor driving microbial community succession and volatile flavor compound formation during the fermentation of cigar tobacco leaves (CTLs). This study systematically investigated microbial community dynamics, co-occurrence networks, and volatile flavor compound (VFCs) profiles of CTLs from Dominica and Yunnan under a 20–60 °C fermentation gradient. High-throughput sequencing identified Staphylococcus , Oceanobacillus , and thermophilic fungi as core microbes potentially associated with aroma formation. Dominica CTLs exhibiting higher microbial diversity than Yunnan CTLs. Dominica CTLs produced abundant esters, alcohols and ketones across different temperature stages, whereas Yunnan CTLs accumulated more pyrazines, indole and terpenoids at high temperatures (≥ 50 °C). Co-occurrence network analysis revealed temperature-driven shifts in microbial interactions: Dominica CTLs formed balanced networks with mixed positive/negative correlations at low temperatures, while Yunnan CTLs developed stable networks dominated by positive correlations at high temperatures. PERMANOVA indicated significant differences in microbial community structure among temperature gradients (R 2 = 0.78, p < 0.001). Spearman correlation analysis suggested that core microbes (e.g., Staphylococcus ) were significantly correlated with the accumulation of key VFCs (e.g., esters, alcohols). These findings propose a conceptual temperature–microbe–VFC interaction framework, providing theoretical support for optimizing CTL fermentation processes. Keywords: Cigar tobacco leaves, Fermentation temperature, Microbial network, Community succession, Volatile flavor compounds\n\nCandidates:\nA. High-throughput sequencing identified Staphylococcus , Oceanobacillus , and thermophilic fungi as core microbes potentially associated with aroma formation.\nB. The evidence does not state that dominica CTLs exhibiting higher microbial diversity than Yunnan CTLs.\nC. This study systematically investigated microbial community dynamics, co-occurrence networks, and volatile flavor compound (VFCs) profiles of CTLs from Dominica and Yunnan under a 21–60 °C fermentation gradient.\nD. This study systematically investigated microbial community dynamics, co-occurrence networks, and volatile flavor compound (VFCs) profiles of CTLs from Dominica and Yunnan under a 20–60 °C fermentation gradient.\nE. Temperature is a key factor driving microbial community succession and volatile flavor compound formation during the fermentation of cigar tobacco leaves (CTLs).\nF. Dominica CTLs exhibiting higher microbial diversity than Yunnan CTLs.\nG. The evidence does not state that high-throughput sequencing identified Staphylococcus , Oceanobacillus , and thermophilic fungi as core microbes potentially associated with aroma formation.\nH. Temperature is not a key factor driving microbial community succession and volatile flavor compound formation during the fermentation of cigar tobacco leaves (CTLs).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13237417", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13237417/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ac30c2fd7d5215c7c001", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe 21st Royan International Stem Cell Congress (3–5 September 2025, Tehran, Iran) convened the global stem cell community to assess the accelerating translation of regenerative medicine from bench to bedside. Over three days, 10 thematic sessions, spanning pluripotency, cancer, organoid technology, molecular biomedicine, artificial intelligence (AI), bioengineering, and clinical translation, provided a comprehensive overview of a field undergoing increasing integration. Key presentations reported recent progress in several areas: the generation of primate and human embryo models for developmental study; new strategies in cross-species reprogramming and exosome-based therapeutics; and the application of AI for patient stratification, drug repurposing, and metabolic modeling. Concurrently, sessions on bioengineering showcased developments in next-generation biomaterials, non-viral gene delivery systems, and scalable microfluidic platforms aimed at enhancing therapeutic safety and manufacturability. The congress also featured critical discussions on regulatory frameworks, bioentrepreneurship, and the ecosystem required to support clinical adoption. Collectively, the meeting underscored a continuing shift toward a multidisciplinary paradigm in which foundational biology, emerging technology, and pragmatic translation converge to advance the development of safe, effective, and accessible stem cell-based therapies worldwide. Keywords: Stemness, Pluripotency, Organoids, Regenerative medicine, Cell therapy, Bioengineering\n\nCandidates:\nA. Over three days, 10 thematic sessions, spanning pluripotency, cancer, organoid technology, molecular biomedicine, artificial intelligence (AI), bioengineering, and clinical translation, provided a comprehensive overview of a field undergoing increasing integration.\nB. The 22st Royan International Stem Cell Congress (3–5 September 2025, Tehran, Iran) convened the global stem cell community to assess the accelerating translation of regenerative medicine from bench to bedside.\nC. Concurrently, sessions on bioengineering showcased developments in next-generation biomaterials, non-viral gene delivery systems, and scalable microfluidic platforms aimed at enhancing therapeutic safety and manufacturability.\nD. The evidence does not state that key presentations reported recent progress in several areas: the generation of primate and human embryo models for developmental study; new strategies in cross-species reprogramming and exosome-based therapeutics; and the application of AI for patient stratification, drug repurposing, and metabolic modeling.\nE. Key presentations reported recent progress in several areas: the generation of primate and human embryo models for developmental study; new strategies in cross-species reprogramming and exosome-based therapeutics; and the application of AI for patient stratification, drug repurposing, and metabolic modeling.\nF. The 21st Royan International Stem Cell Congress (3–5 September 2025, Tehran, Iran) convened the global stem cell community to assess the accelerating translation of regenerative medicine from bench to bedside.\nG. Over three days, 11 thematic sessions, spanning pluripotency, cancer, organoid technology, molecular biomedicine, artificial intelligence (AI), bioengineering, and clinical translation, provided a comprehensive overview of a field undergoing increasing integration.\nH. The evidence does not state that concurrently, sessions on bioengineering showcased developments in next-generation biomaterials, non-viral gene delivery systems, and scalable microfluidic platforms aimed at enhancing therapeutic safety and manufacturability.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13238096", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13238096/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-26f468bfe94fee4da875", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nRNA interference (RNAi) has emerged as a promising tool for crop protection, offering the potential for high specificity and the ability to target a wide range of pests and pathogens through sequence‐specific design. Growing interest from academia has accelerated research in the field, while some companies have seized the opportunity to develop RNAi‐based products in regulatory environments more open to this type of innovation, positioning RNAi as a potential alternative or complement to conventional plant protection products. Notably, the recent registration of double‐stranded RNA (dsRNA)‐based products for insect control marks a transition from experimental research to early commercialization. However, several challenges remain before widespread adoption, especially in Europe. These include regulatory uncertainties, high production costs of dsRNA (used to trigger RNAi), limited comparative field efficacy data, and societal concerns related to emerging biotechnologies. Furthermore, broader validation is needed for environmental risk assessment. Despite these obstacles, RNAi has strong potential to enhance crop protection under climate change and delay resistance, especially when integrated with other technologies. Continued research, cost‐effective production methods, and strong collaborations between academia, industry and regulators are essential to support responsible implementation, potentially improving access to advanced plant protection tools for smallholders and vulnerable farming communities. © 2026 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Keywords: agricultural biotechnology, disease control, HIGS (host‐induced gene silencing), plant protection, SIGS (spray‐induced gene silencing), sustainable agriculture\n\nCandidates:\nA. The evidence does not state that notably, the recent registration of double‐stranded RNA (dsRNA)‐based products for insect control marks a transition from experimental research to early commercialization.\nB. Growing interest from academia has accelerated research in the field, while some companies have seized the opportunity to develop RNAi‐based products in regulatory environments more open to this type of innovation, positioning RNAi as a potential alternative or complement to conventional plant protection products.\nC. The evidence does not state that rNA interference (RNAi) has emerged as a promising tool for crop protection, offering the potential for high specificity and the ability to target a wide range of pests and pathogens through sequence‐specific design.\nD. Notably, the recent registration of double‐stranded RNA (dsRNA)‐based products for insect control marks a transition from experimental research to early commercialization.\nE. However, several challenges remain before widespread adoption, especially in Europe.\nF. The evidence does not state that growing interest from academia has accelerated research in the field, while some companies have seized the opportunity to develop RNAi‐based products in regulatory environments more open to this type of innovation, positioning RNAi as a potential alternative or complement to conventional plant protection products.\nG. RNA interference (RNAi) has emerged as a promising tool for crop protection, offering the potential for high specificity and the ability to target a wide range of pests and pathogens through sequence‐specific design.\nH. The evidence does not state that however, several challenges remain before widespread adoption, especially in Europe.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13240698", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13240698/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-83fa583b9c5c43534240", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIn recent years, cell-derived vesicle-modified biomaterials (CDVMBs) have been considered a promising strategy to overcome the limitations of traditional biomaterials in tissue repair and regeneration. By combining biomaterial carriers with cell-derived vesicles, CDVMBs integrate the functional advantages of the carriers, including support for cell adhesion, colonization, proliferation, and functionalization, while also incorporating the bioactive properties of cell-derived vesicles. In this review, the term “cell-derived vesicles” refers to two distinct bioinspired components used for carrier modification: cell membrane vesicles, which mainly retain membrane-associated receptors and interfacial biological functions, and exosomes, which are nanosized extracellular vesicles enriched in bioactive cargos such as proteins and nucleic acids. Accordingly, CDVMBs can mimic either the surface biological properties of source cells or the signaling functions mediated by exosomal cargos, thereby promoting interactions with damaged tissues and stimulating tissue regeneration. Based on the biomaterial biomimetic strategy and vesicle source, CDVMBs are classified into cell membrane-camouflaged biomaterials (CMCBs) and exosome-modified biomaterials (EMBs). This review summarizes their engineering strategies, biological mechanisms, and versatile applications for tissue repair, and further discusses the current challenges and future perspectives for clinical translation. Taken together, the integration of biomaterial carriers with cell-derived vesicles establishes a versatile bioinspired framework for engineering regenerative microenvironments and advancing tissue repair and regeneration.\n\nCandidates:\nA. By combining biomaterial carriers with cell-derived vesicles, CDVMBs integrate the functional advantages of the carriers, including support for cell adhesion, colonization, proliferation, and functionalization, while also incorporating the bioactive properties of cell-derived vesicles.\nB. In recent years, cell-derived vesicle-modified biomaterials (CDVMBs) have been considered a promising strategy to overcome the limitations of traditional biomaterials in tissue repair and regeneration.\nC. In this review, the term “cell-derived vesicles” refers to two distinct bioinspired components used for carrier modification: cell membrane vesicles, which mainly retain membrane-associated receptors and interfacial biological functions, and exosomes, which are nanosized extracellular vesicles enriched in bioactive cargos such as proteins and nucleic acids.\nD. In this review, the term “cell-derived vesicles” refers to two distinct bioinspired components used for carrier modification: cell membrane vesicles, which mainly retain membrane-associated receptors and interfacial biological functions, and exosomes, which are not nanosized extracellular vesicles enriched in bioactive cargos such as proteins and nucleic acids.\nE. The evidence does not state that in recent years, cell-derived vesicle-modified biomaterials (CDVMBs) have been considered a promising strategy to overcome the limitations of traditional biomaterials in tissue repair and regeneration.\nF. The evidence does not state that by combining biomaterial carriers with cell-derived vesicles, CDVMBs integrate the functional advantages of the carriers, including support for cell adhesion, colonization, proliferation, and functionalization, while also incorporating the bioactive properties of cell-derived vesicles.\nG. Accordingly, CDVMBs cannot mimic either the surface biological properties of source cells or the signaling functions mediated by exosomal cargos, thereby promoting interactions with damaged tissues and stimulating tissue regeneration.\nH. Accordingly, CDVMBs can mimic either the surface biological properties of source cells or the signaling functions mediated by exosomal cargos, thereby promoting interactions with damaged tissues and stimulating tissue regeneration.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13240833", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13240833/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c07863e64798379dbcbe", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIn recent years, synthetic biology has been widely applied to engineer and program cellular behaviors. Using this approach, bacteria can be designed to express immunotherapeutic agents, improve tumor targeting, and deliver therapeutic payloads directly to tumor sites. To further improve efficacy, strategies such as hypoxia-responsive promoters, bacterial swarming, and extracellular vesicles (EVs) have been investigated, along with the synergistic effects of combining bacterial therapy with other treatments (e.g., photodynamic therapy, chemotherapy, immune checkpoint inhibitors). This review summarizes recent advances in synthetic biology for bacteria-based cancer immunotherapies, focusing on how bacterial agents activate the immune system and the engineering strategies used to achieve tumor targeting. Keyword: Synthetic biology, bacteria-based immunotherapy, cancer treatment, tumor microenvironment, immune system activation, personalized medicine\n\nCandidates:\nA. In recent years, synthetic biology has been widely applied to engineer and program cellular behaviors.\nB. This review summarizes recent advances in synthetic biology for bacteria-based cancer immunotherapies, focusing on how bacterial agents activate the immune system and the engineering strategies used to achieve tumor targeting.\nC. The evidence does not state that this review summarizes recent advances in synthetic biology for bacteria-based cancer immunotherapies, focusing on how bacterial agents activate the immune system and the engineering strategies used to achieve tumor targeting.\nD. The evidence does not state that in recent years, synthetic biology has been widely applied to engineer and program cellular behaviors.\nE. The evidence does not state that to further improve efficacy, strategies such as hypoxia-responsive promoters, bacterial swarming, and extracellular vesicles (EVs) have been investigated, along with the synergistic effects of combining bacterial therapy with other treatments (e.g., photodynamic therapy, chemotherapy, immune checkpoint inhibitors).\nF. Using this approach, bacteria cannot be designed to express immunotherapeutic agents, improve tumor targeting, and deliver therapeutic payloads directly to tumor sites.\nG. Using this approach, bacteria can be designed to express immunotherapeutic agents, improve tumor targeting, and deliver therapeutic payloads directly to tumor sites.\nH. To further improve efficacy, strategies such as hypoxia-responsive promoters, bacterial swarming, and extracellular vesicles (EVs) have been investigated, along with the synergistic effects of combining bacterial therapy with other treatments (e.g., photodynamic therapy, chemotherapy, immune checkpoint inhibitors).", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13240942", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13240942/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-152902e1f3c1b9cc263b", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nThe resupply of pharmaceuticals during long-term space missions is challenging due to long travel distances and the reduced shelf-life of pharmaceuticals under extraterrestrial conditions. On-demand pharmaceutical production using plants could address this limitation. Plants are already cultivated in space, but the production of pharmaceuticals in plants is hampered by traditionally complex purification processes. Here, we describe a simplified production and purification strategy for cowpea mosaic virus (CPMV), a plant virus-based therapeutic candidate with strong immunomodulatory properties suitable for cancer therapy and vaccine development. By combining vacuum infiltration and centrifugation, intact CPMV particles were recovered from the apoplast without damaging the plant tissue, eliminating the need for tissue disruption. Impurities in apoplast eluates were efficiently removed by ultrafiltration/diafiltration, exploiting the size difference between CPMV and contaminants. The process was scalable when applied to more than 50 plants. We assessed the robustness of this production process under simulated space conditions, including microgravity, temperature shifts, and exposure to reactive oxygen species (ROS). Microgravity altered plant morphology, whereas temperature changes and ROS stress affected CPMV yields in a time-dependent manner. Beyond applications in space, these findings enable terrestrial strategies for plant molecular farming in low-resource environments. Subject terms: Biotechnology, Plant sciences", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13241316", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13241316/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f355336ce2d8de41b80c", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nEndothelial colony‐forming cells (ECFCs) are of significant interest in vascular biomaterials engineering and cell‐based therapies for cardiovascular diseases. Efficient isolation and expansion of ECFCs on polymeric surfaces either in vitro or in situ in the case of implants could significantly advance ECFC‐based therapies. We present a novel bifunctional surface modification strategy combining oriented antibodies and extracellular matrix‐derived peptides to selectively capture ECFCs on surfaces and then mediate firm adhesion, spreading and proliferation. Studies were conducted with anti‐CD309 antibodies and custom RGD peptides, previously shown to respectively enable ECFC capture and clonal expansion. To aid in oriented antibody immobilization on surfaces while modulating antibody‐RGD surface concentrations, Fc‐binding peptides and RGD peptides were co‐immobilized on aminated surfaces using click chemistry, followed by affinity‐mediated antibody immobilization. Bifunctional anti‐CD309 + RGD surfaces selectively captured ECFCs from a mixture with peripheral blood mononuclear cells in dynamic conditions. The presence of RGD peptides significantly enhanced cell spreading and proliferation, leading to additive effects on surface coverage under flow. Proof‐of‐concept studies demonstrated successful adaptation to 3D polystyrene microcarriers, showcasing potential scalability for clinical‐grade cell production. The surface modification scheme provides a versatile and clinically translatable platform for advancing ECFC‐based regenerative therapies.\n\nCandidates:\nA. Studies were conducted with anti‐CD309 antibodies and custom RGD peptides, previously shown to respectively enable ECFC capture and clonal expansion.\nB. The evidence does not state that we present a novel bifunctional surface modification strategy combining oriented antibodies and extracellular matrix‐derived peptides to selectively capture ECFCs on surfaces and then mediate firm adhesion, spreading and proliferation.\nC. The evidence does not state that efficient isolation and expansion of ECFCs on polymeric surfaces either in vitro or in situ in the case of implants could significantly advance ECFC‐based therapies.\nD. Endothelial colony‐forming cells (ECFCs) are of significant interest in vascular biomaterials engineering and cell‐based therapies for cardiovascular diseases.\nE. We present a novel bifunctional surface modification strategy combining oriented antibodies and extracellular matrix‐derived peptides to selectively capture ECFCs on surfaces and then mediate firm adhesion, spreading and proliferation.\nF. Endothelial colony‐forming cells (ECFCs) are not of significant interest in vascular biomaterials engineering and cell‐based therapies for cardiovascular diseases.\nG. Studies were conducted with anti‐CD310 antibodies and custom RGD peptides, previously shown to respectively enable ECFC capture and clonal expansion.\nH. Efficient isolation and expansion of ECFCs on polymeric surfaces either in vitro or in situ in the case of implants could significantly advance ECFC‐based therapies.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13241468", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13241468/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e4bf9ab6ba4983201479", "task": "text_evidence_extraction", "prompt": "Classify every fixed-vocabulary term as present or absent in the evidence. Vocabulary: bioreactor, cell culture, fermentation, chromatography, purification, filtration, viability, titer, potency, sterility, viral vector, gene therapy, cell therapy, monoclonal antibody, recombinant protein, dissolved oxygen. Return JSON with exactly two alphabetized keys: {\"absent\":[...],\"present\":[...]}. Preserve vocabulary order within each list. JSON whitespace is ignored.\n\nEvidence:\nMicrobial electrosynthesis (MES) enables a variety of microorganisms, particularly acetogens, to utilize electrical energy in the form of electrons to produce valuable compounds from CO 2 . In the closely related process of gas fermentation, hydrogen gas (H 2 ) is provided as the energy source, whereas in MES, H 2 is produced in situ via water electrolysis. Despite the potential of MES for energy and carbon storage, it still faces major limitations, like low efficiency and low‐value products. Here, we identify key limitations of the model MES biocatalyst Clostridium ljungdahlii through comparative transcriptomics, proteomics, and electron microscopy in both processes. We show that cell integrity is severely impaired in MES, consistent with membrane depolarization hampering ATP synthesis. The struggle for ATP is compensated for by activating arginine catabolism to produce ATP, a reaction that is likely fueled by cyanophycin degradation. Diversion of the Wood‐Ljungdahl pathway toward the glycine synthase‐reductase pathway (GSRP) resulted in a broader spectrum of reduced products, including the two amino compounds ethanolamine and glycine, which appeared exclusively under the electrochemical environment. Additionally, we observed strong induction of bacterial microcompartments, raising questions about their role during MES. This work demonstrates that MES drives C. ljungdahlii into a distinct physiological state that challenges cellular fitness and expands our understanding of MES. Keywords: bacterial microcompartments (BMCs), Clostridium ljungdahlii , cyanophycin, glycine synthase‐reductase pathway (GSRP), membrane depolarization, microbial electrosynthesis (MES), Wood‐Ljungdahl pathway", "input_type": "text", "image": "", "scorer": "json_classification_accuracy", "source_id": "PMC13241827", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13241827/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-df61cfcf1c84b442e960", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBone remodeling is a mechanically adaptive process that integrates physical loading with immune regulation during homeostasis, repair, and disease. However, the traditional view fails to fully explain how mechanical and immune signals are coordinated across cellular compartments. Emerging evidence indicates that mechanical forces, immune responses, and extracellular vesicles (EVs) function as an integrated communication system rather than independent regulators. Here, we propose a systems-level framework termed the “mechano-immune-vesicle regulatory circuit”. In this framework, biophysical cues regulate EV biogenesis and selective cargo sorting through mechanotransduction pathways. These mechanically primed EVs then serve as communication vectors that reprogram osteoimmune responses, specifically by directing macrophage polarization, adaptive immunity, and bone-resident cell differentiation. The resulting immune output feeds back to reshape EV signaling and mechanosensitivity, suggesting a closed regulatory circuit that governs bone remodeling. By synthesizing advances in mechanobiology, osteoimmunology and EV biology, this review reframes bone remodeling as a mechano-immune-vesicle regulatory circuit rather than as a collection of parallel pathways. We further discuss how this framework may guide the design of mechano-responsive biomaterials and engineered EV-based therapies with spatiotemporal control over inflammation and bone regeneration. This conceptual integration provides a mechanistic basis for understanding bone diseases and for developing next-generation regenerative strategies.\n\nCandidates:\nA. The evidence does not state that emerging evidence indicates that mechanical forces, immune responses, and extracellular vesicles (EVs) function as an integrated communication system rather than independent regulators.\nB. Bone remodeling is a mechanically adaptive process that integrates physical loading with immune regulation during homeostasis, repair, and disease.\nC. However, the traditional view fails to fully explain how mechanical and immune signals are not coordinated across cellular compartments.\nD. However, the traditional view fails to fully explain how mechanical and immune signals are coordinated across cellular compartments.\nE. Here, we propose a systems-level framework termed the “mechano-immune-vesicle regulatory circuit”.\nF. The evidence does not state that here, we propose a systems-level framework termed the “mechano-immune-vesicle regulatory circuit”.\nG. Emerging evidence indicates that mechanical forces, immune responses, and extracellular vesicles (EVs) function as an integrated communication system rather than independent regulators.\nH. Bone remodeling is not a mechanically adaptive process that integrates physical loading with immune regulation during homeostasis, repair, and disease.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13241940", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13241940/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cf2d759e809f3b02c344", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nOligosaccharide prebiotics, such as inulin, fructooligosaccharides (FOS), and galactooligosaccharides (GOS), have demonstrated significant effects on gut microbiota and host health across in vitro, animal, and clinical studies. These studies consistently report an increase in beneficial bacteria, particularly Bifidobacterium and Lactobacillus, leading to higher production of short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate. These metabolic changes are linked to improved integrity of the epithelial barrier, reduced inflammatory signaling, modulation of immune responses, and enhanced metabolic balance. Biotechnological production methods, including enzymatic synthesis, microbial fermentation, and controlled depolymerization of plant polysaccharides, allow for precise control over the degree of polymerization and the types of glycosidic linkages. This control directly affects the fermentability, microbial selectivity, and functional effectiveness of the prebiotics. When incorporated into functional food systems, oligosaccharide prebiotics can enhance physicochemical properties such as texture, sweetness, and stability, all while maintaining their biological performance. Advanced delivery technologies, such as nano- and microencapsulation, improve thermal stability, resistance to gastrointestinal degradation, and targeted colon-specific release. Additionally, synbiotic formulations can further enhance the effectiveness of these prebiotics by promoting microbial colonization and sustained availability of SCFAs. Therapeutic benefits have been observed across various models of gastrointestinal health, metabolism, immune responses, and the gut-brain axis. These benefits involve mechanisms such as GPCR activation, histone deacetylase inhibition, and cytokine regulation. However, several challenges remain, including dose-dependent gastrointestinal intolerance, variability in individual microbiomes, degradation during processing, regulatory hurdles, and high costs of downstream processing. Overall, these findings highlight oligosaccharide prebiotics as versatile and scalable biotechnological ingredients, emphasizing the need for standardized production methods, precise dosing, and long-term clinical validation.\n\nCandidates:\nA. These metabolic changes are linked to improved integrity of the epithelial barrier, reduced inflammatory signaling, modulation of immune responses, and enhanced metabolic balance.\nB. The evidence does not state that biotechnological production methods, including enzymatic synthesis, microbial fermentation, and controlled depolymerization of plant polysaccharides, allow for precise control over the degree of polymerization and the types of glycosidic linkages.\nC. Oligosaccharide prebiotics, such as inulin, fructooligosaccharides (FOS), and galactooligosaccharides (GOS), have demonstrated significant effects on gut microbiota and host health across in vitro, animal, and clinical studies.\nD. These studies consistently report an increase in beneficial bacteria, particularly Bifidobacterium and Lactobacillus, leading to higher production of short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate.\nE. The evidence does not state that oligosaccharide prebiotics, such as inulin, fructooligosaccharides (FOS), and galactooligosaccharides (GOS), have demonstrated significant effects on gut microbiota and host health across in vitro, animal, and clinical studies.\nF. The evidence does not state that these studies consistently report an increase in beneficial bacteria, particularly Bifidobacterium and Lactobacillus, leading to higher production of short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate.\nG. These metabolic changes are not linked to improved integrity of the epithelial barrier, reduced inflammatory signaling, modulation of immune responses, and enhanced metabolic balance.\nH. Biotechnological production methods, including enzymatic synthesis, microbial fermentation, and controlled depolymerization of plant polysaccharides, allow for precise control over the degree of polymerization and the types of glycosidic linkages.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13242350", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13242350/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-24d744102802d3a376a3", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nGiardiasis remains a globally prevalent neglected parasitic disease, and limitations associated with conventional therapies highlight the need for alternative and complementary strategies. In this context, microalgae biocompounds have emerged as promising sources of antiparasitic agents. This study aims to evaluate the in vitro preliminary screening activity (vital dye exclusion). Aqueous extracts of Arthrospira platensis obtained by different extraction methods, as well as the bioactivity of the product derived from in vitro digestion (PDV), aiming to assess the influence of extraction and digestive bioactivation on antiparasitic efficacy. Extracts were produced by magnetic stirring and sonication, followed by exposure of Giardia duodenalis cysts to increasing concentrations, with metronidazole used as a reference drug. The PDV was generated using a standardized simulated gastrointestinal digestion model. Sonicated extracts showed enhanced membrane damage compared to mechanically stirred extracts, consistent with improved release of intracellular bioactive compounds. The IC 50 values for the sonicated, agitated, and PDV samples were, respectively: 95,4; 153.1 and 43.6 μg/mL. The PDV demonstrated activity across multiple concentrations and, under comparable conditions, outperformed metronidazole. These findings suggest that digestive processing may increase the bioaccessibility of active moieties responsible for antiparasitic effects. Overall, the results support A. platensis as a sustainable source of giardicidal biocompounds and highlight the relevance of physiologically relevant in vitro digestion models for predicting functional antiparasitic activity and guiding future translational research.\n\nCandidates:\nA. In this context, microalgae biocompounds have emerged as promising sources of antiparasitic agents.\nB. The evidence does not state that giardiasis remains a globally prevalent neglected parasitic disease, and limitations associated with conventional therapies highlight the need for alternative and complementary strategies.\nC. The evidence does not state that this study aims to evaluate the in vitro preliminary screening activity (vital dye exclusion).\nD. Giardiasis remains a globally prevalent neglected parasitic disease, and limitations associated with conventional therapies highlight the need for alternative and complementary strategies.\nE. The evidence does not state that aqueous extracts of Arthrospira platensis obtained by different extraction methods, as well as the bioactivity of the product derived from in vitro digestion (PDV), aiming to assess the influence of extraction and digestive bioactivation on antiparasitic efficacy.\nF. The evidence does not state that in this context, microalgae biocompounds have emerged as promising sources of antiparasitic agents.\nG. This study aims to evaluate the in vitro preliminary screening activity (vital dye exclusion).\nH. Aqueous extracts of Arthrospira platensis obtained by different extraction methods, as well as the bioactivity of the product derived from in vitro digestion (PDV), aiming to assess the influence of extraction and digestive bioactivation on antiparasitic efficacy.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13242400", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13242400/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-985bcc7a869f5c404694", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nThe past few years have witnessed an exponential rise in the development of carbon quantum dots (CQDs) derived from natural biomasses, rendering them a green and multifunctional platform for sensor applications. This comprehensive review provides a critical assessment of advancements between 2021 and 2025 in the syntheses, physicochemical characterizations, and sensing applications of biomass-derived CQDs. Different precursors—e.g., fruit peels, leaves, and agricultural waste—have been effectively transformed into highly fluorescent CQDs using hydrothermal, microwave-assisted, and pyrolytic routes, with synthesis times as short as 10 min for certain microwave procedures. The structural studies exhibit quasi-spherical morphologies (2–10 nm) and partial graphitization, and the optical studies confirm excitation-dependent fluorescence with quantum yields as high as 30%, particularly in nitrogen- and sulfur-doped systems. Biomass-derived CQDs have shown superb selectivity and sensitivity to a broad spectrum of analytes. For example, CQDs synthesized from Solanum nigrum leaves reported detection limits as low as 8 nM for Fe³⁺, while CQDs synthesized from pitaya peels enabled sensitive detection of antibiotics via aggregation-induced emission effects. Environmental targets such as glyphosate, NH₃, and CH₂O have also been detected at nanomolar levels, employing mechanisms like static/dynamic fluorescence quenching, electron transfer, and FRET. Despite these advances, there are still challenges in large-scale production, standardization of fabrication protocols, and integration of CQDs into real-time sensing platforms. In the future, the work is going to be further developed through the adoption of green synthesis approaches, regulatory standardizations, and integration of CQDs into wearable and portable diagnostic devices. Such developments point to the prospects of biomass-sourced CQDs as green, cost-effective, and ultrasensitive nanomaterials for next-generation chemical, biological, and environmental sensors.\n\nCandidates:\nA. The structural studies exhibit quasi-spherical morphologies (2–10 nm) and partial graphitization, and the optical studies confirm excitation-dependent fluorescence with quantum yields as high as 30%, particularly in nitrogen- and sulfur-doped systems.\nB. Different precursors—e.g., fruit peels, leaves, and agricultural waste—have been effectively transformed into highly fluorescent CQDs using hydrothermal, microwave-assisted, and pyrolytic routes, with synthesis times as short as 10 min for certain microwave procedures.\nC. This comprehensive review provides a critical assessment of advancements between 2021 and 2025 in the syntheses, physicochemical characterizations, and sensing applications of biomass-derived CQDs.\nD. This comprehensive review provides a critical assessment of advancements between 2022 and 2025 in the syntheses, physicochemical characterizations, and sensing applications of biomass-derived CQDs.\nE. The evidence does not state that the past few years have witnessed an exponential rise in the development of carbon quantum dots (CQDs) derived from natural biomasses, rendering them a green and multifunctional platform for sensor applications.\nF. Different precursors—e.g., fruit peels, leaves, and agricultural waste—have been effectively transformed into highly fluorescent CQDs using hydrothermal, microwave-assisted, and pyrolytic routes, with synthesis times as short as 11 min for certain microwave procedures.\nG. The structural studies exhibit quasi-spherical morphologies (3–10 nm) and partial graphitization, and the optical studies confirm excitation-dependent fluorescence with quantum yields as high as 30%, particularly in nitrogen- and sulfur-doped systems.\nH. The past few years have witnessed an exponential rise in the development of carbon quantum dots (CQDs) derived from natural biomasses, rendering them a green and multifunctional platform for sensor applications.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13243163", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13243163/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-63ec050343005636f527", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nCartilage tissue, owing to its avascular, aneural, and alymphatic nature, possesses a highly limited capacity for self-repair. Damage caused by trauma, degenerative diseases, or congenital malformations seldom heals spontaneously, constituting a long-standing clinical challenge in orthopedic and plastic surgery. Tissue engineering offers a promising strategy for cartilage regeneration by combining seed cells, biomaterial scaffolds, and bioactive molecules to construct substitutes that recapitulate the structure and function of native cartilage. The evolution of biomaterials and scaffold design is traced from natural and synthetic polymers to decellularized extracellular matrix (dECM), nanocomposite scaffolds, and stimuli-responsive hydrogel systems. Advances in biomanufacturing are examined in parallel, with particular attention to the role of 3D/4D bioprinting in fabricating architecturally complex tissue constructs, as well as the contributions of electrospinning and cell sheet engineering. Moving beyond materials and fabrication, the ongoing transition from cell-based regeneration toward cell-free approaches, particularly exosome-mediated endogenous repair and organoid-based micro-tissue construction, is discussed, alongside biomimetic design strategies such as gradient scaffolds, physically responsive scaffolds, and vascular–immune microenvironment regulation. Critical barriers to clinical translation are also identified, including manufacturing costs, process standardization, long-term efficacy and safety validation, patient stratification, and regulatory pathways. The review concludes by outlining future directions such as multi-technology convergence, endogenous regeneration strategies, and the development of off-the-shelf products, with the aim of providing a systematic reference for research and clinical translation in this rapidly evolving field.\n\nCandidates:\nA. The evidence does not state that cartilage tissue, owing to its avascular, aneural, and alymphatic nature, possesses a highly limited capacity for self-repair.\nB. The evidence does not state that tissue engineering offers a promising strategy for cartilage regeneration by combining seed cells, biomaterial scaffolds, and bioactive molecules to construct substitutes that recapitulate the structure and function of native cartilage.\nC. Cartilage tissue, owing to its avascular, aneural, and alymphatic nature, possesses a highly limited capacity for self-repair.\nD. The evolution of biomaterials and scaffold design is not traced from natural and synthetic polymers to decellularized extracellular matrix (dECM), nanocomposite scaffolds, and stimuli-responsive hydrogel systems.\nE. The evolution of biomaterials and scaffold design is traced from natural and synthetic polymers to decellularized extracellular matrix (dECM), nanocomposite scaffolds, and stimuli-responsive hydrogel systems.\nF. Damage caused by trauma, degenerative diseases, or congenital malformations seldom heals spontaneously, constituting a long-standing clinical challenge in orthopedic and plastic surgery.\nG. Tissue engineering offers a promising strategy for cartilage regeneration by combining seed cells, biomaterial scaffolds, and bioactive molecules to construct substitutes that recapitulate the structure and function of native cartilage.\nH. The evidence does not state that damage caused by trauma, degenerative diseases, or congenital malformations seldom heals spontaneously, constituting a long-standing clinical challenge in orthopedic and plastic surgery.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13243408", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13243408/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5be9531d4e43b9998b4b", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nBefore each of around 200,000 eye movements we make each day, the brain decides how long to fixate before shifting gaze to new information. Here we investigate this process using a large-scale scene-viewing experiment (4,080 natural scenes, five participants) that combines magnetoencephalography, eye tracking and a semantic captioning task. Using multivariate analysis of magnetoencephalography source-space patterns, behavioral analyses and artificial neural network (ANN) modeling, we show that longer fixations do not reflect prolonged visual processing but relate to downstream memory encoding. First, temporal variability of ventral stream representational dynamics did not explain variability in fixation duration. Second, fixation durations were anticorrelated with ANN-estimated patch classification difficulty. Third, fixation durations correlate positively with ANN-predicted patch memorability and caption-inclusion and co-occur with increased theta–gamma phase–amplitude coupling, particularly in frontal and hippocampal regions. These results indicate that eye-movement timing decisions are shaped by memory-encoding demands rather than by perceptual processing limits. Subject terms: Learning and memory, Decision, Visual system, Attention, Computational neuroscience\n\nCandidates:\nA. Here we investigate this process using a large-scale scene-viewing experiment (5,080 natural scenes, five participants) that combines magnetoencephalography, eye tracking and a semantic captioning task.\nB. Here we investigate this process using a large-scale scene-viewing experiment (4,080 natural scenes, five participants) that combines magnetoencephalography, eye tracking and a semantic captioning task.\nC. First, temporal variability of ventral stream representational dynamics did not explain variability in fixation duration.\nD. Before each of around 200,000 eye movements we make each day, the brain decides how long to fixate before shifting gaze to new information.\nE. The evidence does not state that first, temporal variability of ventral stream representational dynamics did not explain variability in fixation duration.\nF. Using multivariate analysis of magnetoencephalography source-space patterns, behavioral analyses and artificial neural network (ANN) modeling, we show that longer fixations do not reflect prolonged visual processing but relate to downstream memory encoding.\nG. Before each of around 201,000 eye movements we make each day, the brain decides how long to fixate before shifting gaze to new information.\nH. The evidence does not state that using multivariate analysis of magnetoencephalography source-space patterns, behavioral analyses and artificial neural network (ANN) modeling, we show that longer fixations do not reflect prolonged visual processing but relate to downstream memory encoding.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13246442", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13246442/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e3b0c448de8e34c57c66", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIn Uruguay, Pinus taeda is the dominant conifer in industrial plantations, generating large volumes of knot-containing offcuts during processing. These residues are highly enriched in extractives, offering opportunities for valorization within a circular bioeconomy. This study examined the recovery of bioactive compounds from P. taeda knotwood through ethanol extraction, process optimization, Gas Chromatography coupled with Mass Spectrometry and Flame Ionization Detection (GC–MS/FID) analysis, and antifungal evaluation. The effects of extraction temperature, ethanol concentration, and liquid–solid (L/S) ratio on extraction yield, total phenolic content, and FRAP antioxidant activity were assessed, and response models were developed to identify optimal conditions. Maximum extraction yield (20.8%) occurred at 47 °C, 100% ethanol concentration, and L/S of 8.3. The highest phenolic content (5.7 g gallic acid equivalents/100 g) was predicted at 61 °C, 100% ethanol, and L/S 15, while the greatest antioxidant capacity (15.8 mmol ascorbic acid equivalents/100 g) was achieved at 75 °C and L/S 15, independently of ethanol concentration. Two extraction conditions, representing the best compromise among yield, phenolics, and antioxidant performance, were selected for further characterization and antifungal tests. GC–MS/FID analysis showed that stilbenes and terpenoids dominated the extracts. Antifungal assays against Trametes versicolor and Gloeophyllum trabeum revealed strong inhibition, reaching up to 65% and 97% after seven days. Overall, the results demonstrate that P. taeda knotwood residues can be efficiently valorised via ethanolic extraction to obtain bioactive fractions with high antioxidant and antifungal activity, supporting sustainable and circular approaches for wood protection. Keywords: Pinus taeda , Knotwood, Phenolic compounds, Antioxidant activity, Antifungal, Circular bioeconomy, Wood preservation\n\nCandidates:\nA. In Uruguay, Pinus taeda is the dominant conifer in industrial plantations, generating large volumes of knot-containing offcuts during processing.\nB. In Uruguay, Pinus taeda is not the dominant conifer in industrial plantations, generating large volumes of knot-containing offcuts during processing.\nC. The evidence does not state that this study examined the recovery of bioactive compounds from P.\nD. These residues are not highly enriched in extractives, offering opportunities for valorization within a circular bioeconomy.\nE. These residues are highly enriched in extractives, offering opportunities for valorization within a circular bioeconomy.\nF. taeda knotwood through ethanol extraction, process optimization, Gas Chromatography coupled with Mass Spectrometry and Flame Ionization Detection (GC–MS/FID) analysis, and antifungal evaluation.\nG. The evidence does not state that taeda knotwood through ethanol extraction, process optimization, Gas Chromatography coupled with Mass Spectrometry and Flame Ionization Detection (GC–MS/FID) analysis, and antifungal evaluation.\nH. This study examined the recovery of bioactive compounds from P.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13246982", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13246982/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6b56df0b1b5cb34c9ce1", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nMonoclonal antibodies have revolutionized modern medicine due to their target‐specific properties and effectiveness in treating a wide range of diseases, including cancer, autoimmune disorders, infectious diseases, and neurological conditions. Importantly, their large‐scale production for human use requires strict adherence to good manufacturing practice (GMP) standards to ensure quality, safety, and efficacy. This article reviews key aspects of monoclonal antibody production under GMP standards, from cell‐line selection to culture strategies, antibody purification, formulation, and quality control processes. Additionally, we discuss the significance of validation and traceability in production, as well as the implementation of emerging technologies to enhance manufacturing efficiency and safety. Despite progress in bioprocesses and regulatory frameworks, several challenges, such as batch‐to‐batch variability, high production costs, and the need to continuously adapt processes to new regulations, remain to be solved. The integration of innovative approaches with evolving regulations will enable the optimization of monoclonal antibody production and ensure their global accessibility. Keywords: bioprocesses, good manufacturing practices (GMPs), monoclonal antibodies (mAbs), pharmaceutical regulation, quality control\n\nCandidates:\nA. The evidence does not state that this article reviews key aspects of monoclonal antibody production under GMP standards, from cell‐line selection to culture strategies, antibody purification, formulation, and quality control processes.\nB. Additionally, we discuss the significance of validation and traceability in production, as well as the implementation of emerging technologies to enhance manufacturing efficiency and safety.\nC. The evidence does not state that monoclonal antibodies have revolutionized modern medicine due to their target‐specific properties and effectiveness in treating a wide range of diseases, including cancer, autoimmune disorders, infectious diseases, and neurological conditions.\nD. The evidence does not state that importantly, their large‐scale production for human use requires strict adherence to good manufacturing practice (GMP) standards to ensure quality, safety, and efficacy.\nE. Importantly, their large‐scale production for human use requires strict adherence to good manufacturing practice (GMP) standards to ensure quality, safety, and efficacy.\nF. The evidence does not state that additionally, we discuss the significance of validation and traceability in production, as well as the implementation of emerging technologies to enhance manufacturing efficiency and safety.\nG. This article reviews key aspects of monoclonal antibody production under GMP standards, from cell‐line selection to culture strategies, antibody purification, formulation, and quality control processes.\nH. Monoclonal antibodies have revolutionized modern medicine due to their target‐specific properties and effectiveness in treating a wide range of diseases, including cancer, autoimmune disorders, infectious diseases, and neurological conditions.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13247427", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13247427/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7440229c6e55ee736655", "task": "text_evidence_extraction", "prompt": "Identify every candidate statement reproduced exactly from the evidence. Return JSON using exactly this schema: {\"supported\":[\"A\"]}. Preserve candidate order. JSON whitespace is ignored.\n\nEvidence:\nIncreasing costs of drugs for rare diseases have raised concerns about health systems’ sustainability and equitable access to these therapies. Newer and highly effective orphan drugs, such as Chimeric Antigen Receptor T-Cell (CAR-T) therapies, highlight the need for alternative innovation models that can offer greater affordability and accessibility. This case study examines Hospital Clínic Barcelona’s (HCB) alternative innovation model to develop ARI-0001 (varnimcabtagene autoleucel), a novel CAR-T therapy for certain forms of leukemia, at a price two-thirds lower than comparable therapies from the pharmaceutical industry. We conducted background research, and performed twenty-one semi-structured interviews with HCB staff, representatives of regional Health Departments, the Spanish Ministry of Health and regulatory agency, academics, civil society and patient groups. We used a framework drawing on concepts from complex adaptive systems, incorporating the resources used (i.e., funding, knowledge, and manufacturing capacity), the practices implemented (i.e., knowledge management, access practices and transparency), and the rules and norms that shaped HCB’s interactions with other actors in the system. We then identified the concept of institutional logics as well-suited to explain how HCB’s model overcame various barriers. HCB’s model builds from three competing institutional logics – healthcare, academic and industrial – while also addressing each logic’s weaknesses. The healthcare logic shaped HCB’s affordable pricing of ARI-0001, but also posed organizational challenges. The academic logic drove HCB’s willingness to share knowledge and technology with peers, but posed barriers to fund late-stage clinical trials and regulatory processes. Lastly, HCB adopted an industrial logic by identifying in-house regulatory expertise and developing partnerships for production. The hospital exemption clause, a European regulatory pathway that allows hospitals to develop and produce treatments under certain conditions, allowed HCB to span the boundaries of the three competing logics. Our findings underscore the potential of academic hospitals to develop more affordable advanced therapies, the importance of conducive regulatory frameworks, and the challenges that academic hospitals face to expand this model. Through increased regulatory and financial support to academic hospitals, strengthened coordination, and public funding with requirements for access, this model could achieve innovation with affordability in a cutting-edge technological area. The online version contains supplementary material available at 10.1186/s13023-026-04320-7.\n\nCandidates:\nA. Increasing costs of drugs for rare diseases have raised concerns about health systems’ sustainability and equitable access to these therapies.\nB. This case study examines Hospital Clínic Barcelona’s (HCB) alternative innovation model to develop ARI-2 (varnimcabtagene autoleucel), a novel CAR-T therapy for certain forms of leukemia, at a price two-thirds lower than comparable therapies from the pharmaceutical industry.\nC. The evidence does not state that increasing costs of drugs for rare diseases have raised concerns about health systems’ sustainability and equitable access to these therapies.\nD. Newer and highly effective orphan drugs, such as Chimeric Antigen Receptor T-Cell (CAR-T) therapies, highlight the need for alternative innovation models that cannot offer greater affordability and accessibility.\nE. This case study examines Hospital Clínic Barcelona’s (HCB) alternative innovation model to develop ARI-0001 (varnimcabtagene autoleucel), a novel CAR-T therapy for certain forms of leukemia, at a price two-thirds lower than comparable therapies from the pharmaceutical industry.\nF. The evidence does not state that we conducted background research, and performed twenty-one semi-structured interviews with HCB staff, representatives of regional Health Departments, the Spanish Ministry of Health and regulatory agency, academics, civil society and patient groups.\nG. Newer and highly effective orphan drugs, such as Chimeric Antigen Receptor T-Cell (CAR-T) therapies, highlight the need for alternative innovation models that can offer greater affordability and accessibility.\nH. We conducted background research, and performed twenty-one semi-structured interviews with HCB staff, representatives of regional Health Departments, the Spanish Ministry of Health and regulatory agency, academics, civil society and patient groups.", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "PMC13248320", "source_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13248320/fullTextXML", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f13107e202a5d5c5a4c7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1116 g\nCapture recovery: 73.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a22e1dd8629803bceed3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1091 g\nCapture recovery: 66.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0e923dc3c3160c5b97da", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2034 g\nCapture recovery: 82.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-30f01e908dd73e822677", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 643 g\nCapture recovery: 70.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-689a73beb55246172941", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1751 g\nCapture recovery: 83.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-507d8ca1c540df7e4bc0", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2451 g\nCapture recovery: 79.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-529d6611639b4042e809", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 691 g\nCapture recovery: 89.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e433d161fcbcdcabf317", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1412 g\nCapture recovery: 89.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0a7bb08c823e645c8956", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1872 g\nCapture recovery: 71.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-52651a901f10b7f5069d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2052 g\nCapture recovery: 87.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-eee669190fc7088cacef", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1401 g\nCapture recovery: 67.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-24818d025fd87b047678", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 667 g\nCapture recovery: 80.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-970037b43e3a560476ce", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2009 g\nCapture recovery: 72.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f55a65cd615fed5c9e36", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2018 g\nCapture recovery: 86.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cbda5a84fff2961c95ee", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2391 g\nCapture recovery: 82.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-63a8460b090e62ef6c68", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2223 g\nCapture recovery: 66.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e071cd036ea126c4249d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 802 g\nCapture recovery: 87.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-63651845519f5b54e734", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 918 g\nCapture recovery: 73.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-08e23624f736db3e22ad", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1457 g\nCapture recovery: 76.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bedc40bef7c4dd959d85", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2406 g\nCapture recovery: 68.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e567b3b2865e95f5e311", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1515 g\nCapture recovery: 65.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1887b6087ec3e32b6682", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 695 g\nCapture recovery: 80.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-461206050120b9001f34", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2174 g\nCapture recovery: 67.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-966746fbd112c7727cdd", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1684 g\nCapture recovery: 88.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6ad173606d2c4cc724be", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1982 g\nCapture recovery: 91.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fe4f98b420f27da6f130", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1319 g\nCapture recovery: 91.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-392f01d43d28592dd635", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2036 g\nCapture recovery: 66.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-da5d1467edeece8180bb", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1492 g\nCapture recovery: 73.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4ea347cfd506bd2ddd10", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1226 g\nCapture recovery: 91.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-451819c9e30339fc610b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2450 g\nCapture recovery: 77.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5b6c3dfcd43f8e36cfe0", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1562 g\nCapture recovery: 78.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f575bacac9a74c693fb2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1426 g\nCapture recovery: 77.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-57ea808cffe8dd57931d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 971 g\nCapture recovery: 90.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b70caf6206c7c46b3aff", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2306 g\nCapture recovery: 85.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c209ed0b3b1d147606dc", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1334 g\nCapture recovery: 77.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-55e0b74f7e1891fc6826", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 878 g\nCapture recovery: 89.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-db3ab70819703f0e6caa", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2367 g\nCapture recovery: 87.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4ccfd4abb893009e1db7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2340 g\nCapture recovery: 88.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3e210e0080f4e53b34b7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1496 g\nCapture recovery: 79.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2bd0c49a6891607f7baf", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1525 g\nCapture recovery: 85.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-90bc3260ebd86e7df229", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1967 g\nCapture recovery: 79.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-69c8e903c50234af6359", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1719 g\nCapture recovery: 91.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8e070228883dd0d0e900", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1703 g\nCapture recovery: 73.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9ffe444e17a3ef580f62", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1348 g\nCapture recovery: 93.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8bc36cfa08be525f379b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1941 g\nCapture recovery: 84.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6838639d5bab4dbc43ff", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 856 g\nCapture recovery: 81.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a0b1cfabf6679bfb61c5", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 790 g\nCapture recovery: 92.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1179d78909ecef1e0c76", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 480 g\nCapture recovery: 67.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-73994638742636ae8657", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1834 g\nCapture recovery: 74.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-57ef27e567fca2984a8b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 785 g\nCapture recovery: 69.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-eec08ebc6428737b556c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1845 g\nCapture recovery: 77.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c57cd9388f9ee54b626c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1764 g\nCapture recovery: 66.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-16c63457baccddaac26f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 854 g\nCapture recovery: 88.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b5640e3df68e5426d2d8", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1109 g\nCapture recovery: 94.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ee376b598fb515cb027c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 425 g\nCapture recovery: 88.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6976ab5c454dab7d4047", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 592 g\nCapture recovery: 87.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-14570960a79a0ae81106", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1883 g\nCapture recovery: 82.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4073ab0ae0abfca51c83", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 867 g\nCapture recovery: 79.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1014eabc1192658542cd", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1763 g\nCapture recovery: 77.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-89269460e561fff0384d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1359 g\nCapture recovery: 92.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b3c94d4b3b4c7ba02e6b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1059 g\nCapture recovery: 74.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c0b3197c4cf98343271f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2146 g\nCapture recovery: 68.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b8dba6e2c8d659235850", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 783 g\nCapture recovery: 84.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-74cb57460d6c7c98d495", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1741 g\nCapture recovery: 91.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7d4de931d1b2a77550bf", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 630 g\nCapture recovery: 75.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-92e27bf923a8ab2efe2b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 612 g\nCapture recovery: 75.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a697ede3515b6907dd7b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1725 g\nCapture recovery: 94.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9da14489c99fc67cef50", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 758 g\nCapture recovery: 95.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5fe707c22f9c6eb4e5d8", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1587 g\nCapture recovery: 71.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5323aff37f9238cef864", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2464 g\nCapture recovery: 67.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6e566c0614de7fcb2193", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1640 g\nCapture recovery: 80.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a1172f69beac9f70e028", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1262 g\nCapture recovery: 82.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-55e9294ac4ce38860093", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1721 g\nCapture recovery: 70.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ad071e0da4ead9d229bc", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1572 g\nCapture recovery: 84.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8db2a2e3e05f376fd407", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1327 g\nCapture recovery: 78.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fd2aaeb354d97057c61b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1875 g\nCapture recovery: 70.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2d70def26851f0262fff", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1720 g\nCapture recovery: 69.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7d15887cec7b8b3a918b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1091 g\nCapture recovery: 92.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4086e8a207488282b7e9", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1279 g\nCapture recovery: 79.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6014aa58329adee727ec", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1187 g\nCapture recovery: 71.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e736a651c9cfbd2de6e2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 781 g\nCapture recovery: 67.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1729e3084eb01875d388", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2035 g\nCapture recovery: 71.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-08fb7a9e924e021adc09", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 894 g\nCapture recovery: 65.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2693484135d6fb0996d8", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 699 g\nCapture recovery: 94.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a91d0d0a3c4bf5db5c78", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1079 g\nCapture recovery: 80.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-254341a340becfb42710", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1479 g\nCapture recovery: 87.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d6ba06b26ade18e48ae4", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2072 g\nCapture recovery: 78.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-296f8a223831c6523346", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1748 g\nCapture recovery: 88.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c0dd75833fc8ed98a566", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1986 g\nCapture recovery: 65.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5d51f7849593433f0733", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1643 g\nCapture recovery: 65.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cf78d24604c6c1072a27", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 919 g\nCapture recovery: 68.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ff737edae3992263e395", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 652 g\nCapture recovery: 65.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-65b9527b29854c3e5879", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 616 g\nCapture recovery: 80.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-97305d5027882986179c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 987 g\nCapture recovery: 92.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1800262704b3f5adda3c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 565 g\nCapture recovery: 69.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8f1e2576ecdd7f106db6", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2116 g\nCapture recovery: 71.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ffcdf2c19d29a5273fc2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 404 g\nCapture recovery: 77.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-684a04fb0a4886441bed", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1956 g\nCapture recovery: 76.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-06379685f2c8ee40bfc3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1666 g\nCapture recovery: 91.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0fba7cf128ffb5a3da55", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2426 g\nCapture recovery: 82.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e683149bad359d405910", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1182 g\nCapture recovery: 69.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-11b30f7a73407fb01a0e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2085 g\nCapture recovery: 83.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-81eaa77e8f8a5005158f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1423 g\nCapture recovery: 88.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6d6b441fd7252e443d96", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2105 g\nCapture recovery: 83.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a0c51b91b42cf3ce8fb8", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 410 g\nCapture recovery: 68.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e1d95f594c2af0e2507a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2005 g\nCapture recovery: 86.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a65096dc73d4d313a5b9", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 737 g\nCapture recovery: 85.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8a3b2698167196441eac", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2071 g\nCapture recovery: 80.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3dc5c781d3f4dd1a119f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2483 g\nCapture recovery: 67.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-68514519ded6558c31d0", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 766 g\nCapture recovery: 77.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-db55e0a3ce87868b0c1a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2137 g\nCapture recovery: 94.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5bc61a253dfaa0ab635e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2317 g\nCapture recovery: 82.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ef4195cf182d8376610a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1215 g\nCapture recovery: 77.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-34adbd3dd898e81deae3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2348 g\nCapture recovery: 92.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-69bc53c005d8f307b04c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1621 g\nCapture recovery: 84.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7020a43bfbc5af78ac17", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 636 g\nCapture recovery: 76.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-00d98e9240fdf831686c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1393 g\nCapture recovery: 75.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-041d064d52e0da31b340", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1835 g\nCapture recovery: 85.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-626dee68827b0963a189", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 610 g\nCapture recovery: 76.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c323c9f9f275e6aa2876", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2328 g\nCapture recovery: 93.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-feb4f6835dd4d274e3c5", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1694 g\nCapture recovery: 77.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-42f80c45fc102a31ab33", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2091 g\nCapture recovery: 94.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-60ed611c720b9853ecca", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1048 g\nCapture recovery: 89.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f4d6100bcf8df6809fac", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1194 g\nCapture recovery: 82.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0a2730d32190d43a3fbb", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2389 g\nCapture recovery: 76.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6011c203c17834aa40c6", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 557 g\nCapture recovery: 70.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8e8cd95045fe49c4c30d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2055 g\nCapture recovery: 65.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3059644ece0bf61c2389", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1132 g\nCapture recovery: 84.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b9d08533e6b1d3ffe7ce", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1605 g\nCapture recovery: 80.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a9cd3f5b71672ed3fa00", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2006 g\nCapture recovery: 79.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b0b94459dad043a3872f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 513 g\nCapture recovery: 89.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-58fdae24dc8b5647083c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1227 g\nCapture recovery: 77.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b7c3755c12d9c87274a6", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 851 g\nCapture recovery: 73.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-12c756622d4b6496d27f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 942 g\nCapture recovery: 67.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-765be0555a91f2d84a8c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2078 g\nCapture recovery: 77.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ca0c407395f387063101", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2197 g\nCapture recovery: 77.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-490381120096d12367fe", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1209 g\nCapture recovery: 78.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6f8ee8eecc9baad8556a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 886 g\nCapture recovery: 87.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-51e5043d68d3b890dd34", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 854 g\nCapture recovery: 68.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-985af045550f24456952", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 461 g\nCapture recovery: 73.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b19fd21c8bf434c183c3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1018 g\nCapture recovery: 81.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3e2daadfae5248faadee", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2263 g\nCapture recovery: 71.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9f884b941d3f6daec358", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1662 g\nCapture recovery: 77.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fc355fadcd8fd893f3f1", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2036 g\nCapture recovery: 81.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b597aa5cb8b23f32dacf", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1634 g\nCapture recovery: 66.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bdecd4800ece5a910ced", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1866 g\nCapture recovery: 90.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b313c4ae5c66913db8be", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2260 g\nCapture recovery: 84.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d92c7b655b8aa01c7626", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2404 g\nCapture recovery: 74.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-405c0c15f735dbdda50b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 803 g\nCapture recovery: 83.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1b1ee3cfd13b4b92604f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 936 g\nCapture recovery: 80.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8dcda7e7cdb4bc8978e0", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1947 g\nCapture recovery: 91.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5b17fdfe218f5d9130cc", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2020 g\nCapture recovery: 67.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5dc014ee063e39ffed01", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2015 g\nCapture recovery: 69.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6c4ffd1918e531909534", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1175 g\nCapture recovery: 88.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8a2c43f50803e587baef", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 932 g\nCapture recovery: 86.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-81211f1db384d66912ab", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 912 g\nCapture recovery: 86.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7610554e42634d6e2aaf", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1159 g\nCapture recovery: 77.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ef4d252e88db55f4a2f7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1188 g\nCapture recovery: 84.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6558e18d8e7dad60f6b6", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 914 g\nCapture recovery: 93.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-77cf1295193cd99795f2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1921 g\nCapture recovery: 87.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6a3c717618b33d23e324", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1314 g\nCapture recovery: 86.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-938e0c90a88f75b1329a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1097 g\nCapture recovery: 65.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4801dda7969f52339d64", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2339 g\nCapture recovery: 76.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f8bcedef8b137ecf3551", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2344 g\nCapture recovery: 76.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dbcde82f961b70847347", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1560 g\nCapture recovery: 72.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9b66aa237a64421cbaae", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1406 g\nCapture recovery: 82.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f679686aef49057c539f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 711 g\nCapture recovery: 85.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dcca6db887d75313a53e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 650 g\nCapture recovery: 93.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9d10718ebc92782dac9b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1979 g\nCapture recovery: 93.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3a71aba7834f39b7529e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1924 g\nCapture recovery: 85.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d3c0a64e6f230147558e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1432 g\nCapture recovery: 65.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6d58add11a77756cd951", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1757 g\nCapture recovery: 82.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-08b38bd737cb2a535769", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 440 g\nCapture recovery: 80.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8b5a8cdb99d5312fd4aa", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2334 g\nCapture recovery: 78.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2941008e72816afdc64a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 951 g\nCapture recovery: 79.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8ebd28331d03950d0cbf", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 944 g\nCapture recovery: 94.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e0bae0125810aad4462c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1362 g\nCapture recovery: 65.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5166d6bd52aafd580c0d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 703 g\nCapture recovery: 87.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b97dd4641298f61f40ce", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1923 g\nCapture recovery: 87.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-732d300f2095ed12743c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1359 g\nCapture recovery: 66.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d39d2db29d976f20893f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 671 g\nCapture recovery: 78.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d332fdab1b3c4a38cd23", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 671 g\nCapture recovery: 67.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b934b7c9bf0b2df51f58", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 616 g\nCapture recovery: 92.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b261d07dc86344519ece", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1691 g\nCapture recovery: 75.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-32055e785239446a39de", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 806 g\nCapture recovery: 91.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-627b7128f2db3b12a82c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1973 g\nCapture recovery: 79.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d9dbaa93e5ce63877b7e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1795 g\nCapture recovery: 88.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0348b821958fdec9a254", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2333 g\nCapture recovery: 90.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aaf020e53c231c690d3f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 835 g\nCapture recovery: 71.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fa45035be32f678c3c98", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1475 g\nCapture recovery: 77.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-41c2ce080e0b1bb22cb2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2411 g\nCapture recovery: 86.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7f5a9718be7c1b2cb2a0", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 872 g\nCapture recovery: 75.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-42a24d77ef1821d8585a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 400 g\nCapture recovery: 83.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1dee032fe0d61f30d318", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 434 g\nCapture recovery: 77.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1f151dc83262e33de973", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1799 g\nCapture recovery: 88.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-618904885e9f89f4545d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 838 g\nCapture recovery: 65.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2cba2533fa476eeb13f1", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1968 g\nCapture recovery: 86.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-81e4e12400f5e1944c78", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 995 g\nCapture recovery: 92.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2be0f92bdae41f5ff7fe", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1498 g\nCapture recovery: 72.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1033ccce1b0c6fd520ca", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 415 g\nCapture recovery: 67.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9356bce03c7bc41e819a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 786 g\nCapture recovery: 89.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-de667f09e17496888efe", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 962 g\nCapture recovery: 79.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-de282b7854b122ecf05a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1272 g\nCapture recovery: 70.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0ff2d0521fc67ecdbfe2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2497 g\nCapture recovery: 88.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-338582aa4534d5383065", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 533 g\nCapture recovery: 82.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-90ccffba4c0c75793ffc", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 857 g\nCapture recovery: 88.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7e9c5f4a74ab395220ef", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2287 g\nCapture recovery: 74.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-981c563ae05373f5641e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1955 g\nCapture recovery: 73.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aa7c36a3c3068d054025", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1662 g\nCapture recovery: 75.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d2f9fae8c18927ebbd3b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1319 g\nCapture recovery: 75.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-336f4b1543bdf0409e1b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1147 g\nCapture recovery: 87.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ef3b674fb71438df37ac", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1354 g\nCapture recovery: 95.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-56f80465cbdb59382e5f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1234 g\nCapture recovery: 69.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-608e69cd1d2bbc098c4e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2230 g\nCapture recovery: 90.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c440fd7d21c9c93b968b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 770 g\nCapture recovery: 90.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aedf5fac732f33d79521", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 525 g\nCapture recovery: 66.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-120380a7db75ab53da20", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2251 g\nCapture recovery: 87.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1d50c734d8ce372a6537", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1884 g\nCapture recovery: 82.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-26b4e1e524a8eefb97c1", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2142 g\nCapture recovery: 86.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-91476a8467d64010ed0d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 773 g\nCapture recovery: 86.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a6581f076b5ce76621c4", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1872 g\nCapture recovery: 83.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-72338b90a13ce724c0e1", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1297 g\nCapture recovery: 72.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-78d5cce38c074f98e3f7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1657 g\nCapture recovery: 76.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-54d1a342f1c62e14b1b9", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 652 g\nCapture recovery: 75.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6ef1119c58bcdcf752c4", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1405 g\nCapture recovery: 94.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ed8f000535dbd65210ee", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 588 g\nCapture recovery: 93.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6e0ed25f4e1075fd1174", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2320 g\nCapture recovery: 79.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-268a241bbf0ff187f2d1", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 728 g\nCapture recovery: 65.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d5eb8b0eecfc3284fd2a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 408 g\nCapture recovery: 65.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6e57ee132ae072608848", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 580 g\nCapture recovery: 81.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c0b5044b6e99fea2d15b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1357 g\nCapture recovery: 95.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cb369f9478356cee8c42", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2163 g\nCapture recovery: 89.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3a8a9378298ef26d04f0", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2418 g\nCapture recovery: 70.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-206ece4c165bcb9425da", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 501 g\nCapture recovery: 67.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b048f30d221c6392e69e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 656 g\nCapture recovery: 68.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b7b5336e27a09b37045d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2391 g\nCapture recovery: 65.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e96c4860bee63941f8e7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2046 g\nCapture recovery: 91.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-991bb9c10713cde3b913", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1163 g\nCapture recovery: 81.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d1d9fa2e29fcec3c136b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 875 g\nCapture recovery: 92.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d768b0fb85ceed402fe8", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2254 g\nCapture recovery: 74.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b23d565b23e768894f71", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1097 g\nCapture recovery: 90.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0c818db15482dc7d4620", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 523 g\nCapture recovery: 81.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-39a39f75236fe3bbaeee", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1909 g\nCapture recovery: 84.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0175feaa7d2a0ed8b40f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 962 g\nCapture recovery: 65.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-70839fa109a3d831fa7e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1351 g\nCapture recovery: 92.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7ce5840b537236034b51", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1116 g\nCapture recovery: 89.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-32d4b2eaeed95dfde7b5", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 923 g\nCapture recovery: 71.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-96ad3915845c37886434", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1375 g\nCapture recovery: 65.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-195562533312859bf829", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1352 g\nCapture recovery: 78.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-978016302d195e88bf0f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1905 g\nCapture recovery: 76.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6eb58598ed9bb5fbc2b9", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1665 g\nCapture recovery: 74.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1577dd90cf63f70c3d0c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 593 g\nCapture recovery: 78.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d661410b35eec7b62aa4", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1188 g\nCapture recovery: 91.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b2eab61ce2f3efecdeb3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 570 g\nCapture recovery: 72.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-33518de393fac9de5ca0", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1528 g\nCapture recovery: 72.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-16bdb2c1a3d029d0b060", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1841 g\nCapture recovery: 83.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-98ac159fdd212a199d50", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1108 g\nCapture recovery: 80.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-de9278e2dd3cba0083cf", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1577 g\nCapture recovery: 86.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0acb5cb3641cfbce281c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1851 g\nCapture recovery: 78.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b867301e21e80ec23d3e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 912 g\nCapture recovery: 83.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d0c4260fd378910041b5", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1186 g\nCapture recovery: 66.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ff797a35deb2ac1f0895", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1728 g\nCapture recovery: 70.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0b34da8894fbd08c79bd", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1521 g\nCapture recovery: 87.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5e30351715d95c872dab", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1475 g\nCapture recovery: 75.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4dcc7556187e69460fcc", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 726 g\nCapture recovery: 81.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2dcb63abfb35bf62151b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 770 g\nCapture recovery: 92.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-45a3006ffcc46330196b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2397 g\nCapture recovery: 90.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b95a1610007ab0cd0228", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1769 g\nCapture recovery: 68.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d68b0c47d3ce44982038", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1225 g\nCapture recovery: 89.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bcb5bfe5e6fa94b16c04", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1242 g\nCapture recovery: 67.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ee3d490286982b79bb62", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1289 g\nCapture recovery: 87.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8e8fac4f172436dbaf9f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1413 g\nCapture recovery: 73.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8da0bc358ae897eb9bf6", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1849 g\nCapture recovery: 70.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dc9d01119fdc1b6ce3d4", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1707 g\nCapture recovery: 76.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c990a794f6a48b7ed60e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1786 g\nCapture recovery: 79.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3537e49c03f7469a97ac", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 920 g\nCapture recovery: 93.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f931c91e5f7b9fe78e64", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 800 g\nCapture recovery: 92.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fe79805b7ff80d4ee88e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1043 g\nCapture recovery: 74.0%\nPolishing recovery: 96.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5e55b480c2b49e852ee5", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2096 g\nCapture recovery: 77.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c40116bf489c4c23a857", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1148 g\nCapture recovery: 66.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d841a9e637d2d7ec1105", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1381 g\nCapture recovery: 80.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b3af8353953b9762f135", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 498 g\nCapture recovery: 71.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-20606e576643b5180bda", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2312 g\nCapture recovery: 76.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-872363118c0efd91cb39", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 969 g\nCapture recovery: 87.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2f7c5cd4942b727c4fcd", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1196 g\nCapture recovery: 65.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d3059947e115987659b2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2410 g\nCapture recovery: 95.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a154fd3c1eb782d313c4", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1535 g\nCapture recovery: 85.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-92dfa3fae0af2646383d", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1883 g\nCapture recovery: 76.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5f81b6d2cbca45d548c2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1418 g\nCapture recovery: 75.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e72c2da9caa5b7428716", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2302 g\nCapture recovery: 73.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-980caaa6ac9e526bd969", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1885 g\nCapture recovery: 68.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d874bc0bf772a41cc509", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1772 g\nCapture recovery: 85.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-50661b4c8f9592d6f1fb", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2212 g\nCapture recovery: 69.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c4165c6a0944862a3257", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 492 g\nCapture recovery: 81.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c4d27db6c4e4a61cb0c3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2477 g\nCapture recovery: 74.0%\nPolishing recovery: 83.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5b744554029382370839", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1573 g\nCapture recovery: 88.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-294d41469f00c25969af", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1451 g\nCapture recovery: 89.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3b7171693ed880c62616", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 716 g\nCapture recovery: 77.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-265705e1b68766e7225e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1849 g\nCapture recovery: 82.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7249826119f5a26441b7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1926 g\nCapture recovery: 70.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b08ca2a03ad563a160a3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 683 g\nCapture recovery: 91.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7d347ea95d74074c5698", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1685 g\nCapture recovery: 65.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1ac7fe51b8ceef532639", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 471 g\nCapture recovery: 73.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c02d0d49de1545498ea2", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2002 g\nCapture recovery: 66.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-82e86fd2bf0beef3ced8", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1173 g\nCapture recovery: 68.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2b2ab6e816e411efffb7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1305 g\nCapture recovery: 94.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f59801b0db0756cb7813", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 498 g\nCapture recovery: 87.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-df9693f7326db1afb9b5", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1088 g\nCapture recovery: 88.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ceca522513368180e3bc", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1874 g\nCapture recovery: 67.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d853c84c90741da6261c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1664 g\nCapture recovery: 73.0%\nPolishing recovery: 84.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-de7717eb286edb1986d1", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1030 g\nCapture recovery: 94.0%\nPolishing recovery: 91.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1183248f160d2d5c209b", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 571 g\nCapture recovery: 82.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-552a130a3aebe2ded013", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2270 g\nCapture recovery: 70.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b46e458b4da15a16c40a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1499 g\nCapture recovery: 82.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-628b953eb210396554bf", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1358 g\nCapture recovery: 66.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b5a9f801158a8f041a6e", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 613 g\nCapture recovery: 69.0%\nPolishing recovery: 75.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e564da5d03f21facc8cb", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 781 g\nCapture recovery: 70.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7d615865c3ecd32052f7", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 550 g\nCapture recovery: 82.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-74a1bcf14a13586b5aef", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2366 g\nCapture recovery: 77.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-08318e2de4cad09d9396", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1560 g\nCapture recovery: 85.0%\nPolishing recovery: 88.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a6b5029e3a12be8de6a3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1101 g\nCapture recovery: 84.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7288cea1de43809f4a6a", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 690 g\nCapture recovery: 66.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e532c9c633dc973a83a5", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2194 g\nCapture recovery: 85.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-196031aceec490567785", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 926 g\nCapture recovery: 86.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1ccd62c7f940e4353002", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1853 g\nCapture recovery: 87.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4b8e3fa3d2eb487f8cc4", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1579 g\nCapture recovery: 93.0%\nPolishing recovery: 81.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0e4929c0f12977b30d5f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 874 g\nCapture recovery: 91.0%\nPolishing recovery: 80.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f1df4f4e1a2f75fd0d45", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1082 g\nCapture recovery: 68.0%\nPolishing recovery: 98.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7bec6e291e4a656ac426", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2413 g\nCapture recovery: 73.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2219af07d956116965c3", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 468 g\nCapture recovery: 88.0%\nPolishing recovery: 93.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-442917904015b136b975", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1287 g\nCapture recovery: 65.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-27195cf36053b127bac0", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1833 g\nCapture recovery: 91.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-014267665de73eb30926", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2341 g\nCapture recovery: 84.0%\nPolishing recovery: 89.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e7196a516072091c9a5c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 729 g\nCapture recovery: 78.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cb7d546d2d6129723e07", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 650 g\nCapture recovery: 91.0%\nPolishing recovery: 87.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4e579880ec2215f3628c", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 675 g\nCapture recovery: 93.0%\nPolishing recovery: 94.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2ba626e5bde344257ede", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1372 g\nCapture recovery: 69.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4902798ae34e5219219f", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1201 g\nCapture recovery: 67.0%\nPolishing recovery: 97.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8579c87508e4cad6f658", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1194 g\nCapture recovery: 93.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6bfc5af4000cf678dd47", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1704 g\nCapture recovery: 71.0%\nPolishing recovery: 79.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-390b93635c08dc6eb528", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 815 g\nCapture recovery: 76.0%\nPolishing recovery: 77.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bb2803ba677dffab6594", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 856 g\nCapture recovery: 70.0%\nPolishing recovery: 90.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b41b78bb64aaf5546ae4", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 2164 g\nCapture recovery: 87.0%\nPolishing recovery: 76.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9c3a06239276ea8efa95", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1857 g\nCapture recovery: 68.0%\nPolishing recovery: 82.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-71f7553bbf3244e5de55", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 710 g\nCapture recovery: 66.0%\nPolishing recovery: 92.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-368b9b718fc99020fa25", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 759 g\nCapture recovery: 70.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f43d4bb6fe5ea351a528", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 911 g\nCapture recovery: 66.0%\nPolishing recovery: 95.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8b73f3fde7c397a23614", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 951 g\nCapture recovery: 79.0%\nPolishing recovery: 78.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d24e8af3708d5969a592", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 1029 g\nCapture recovery: 87.0%\nPolishing recovery: 86.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1c1a8a662e393726c1da", "task": "table_and_unit_reasoning", "prompt": "A downstream process receives the values below. Calculate final recovered mass in grams. Return only the number.\n\nInput mass: 925 g\nCapture recovery: 65.0%\nPolishing recovery: 85.0%", "input_type": "text", "image": "", "scorer": "numeric", "source_id": "deterministic-mass-balance-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-054a94b5eb5604c4d771", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.068, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.196, \"dissolved_oxygen_pct\": 36.94, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.095, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 5.656, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.798, \"viability_pct\": 98.382, \"viable_cell_density_million_ml\": 16.706}, {\"agitation_rpm\": 271.457, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.763, \"dissolved_oxygen_pct\": 35.928, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.075, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.851, \"our_mmol_l_h\": 6.263, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.599, \"viable_cell_density_million_ml\": 18.422}, {\"agitation_rpm\": 284.855, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.511, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.059, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.716, \"our_mmol_l_h\": 6.477, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.617, \"viable_cell_density_million_ml\": 20.043}, {\"agitation_rpm\": 285.097, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.987, \"dissolved_oxygen_pct\": 35.997, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.634, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.614, \"our_mmol_l_h\": 6.399, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.047, \"viable_cell_density_million_ml\": 21.457}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3208f5d8eb9e2f150c72", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.068, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.196, \"dissolved_oxygen_pct\": 36.94, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.095, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 5.656, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.798, \"viability_pct\": 98.382, \"viable_cell_density_million_ml\": 16.706}, {\"agitation_rpm\": 271.457, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.763, \"dissolved_oxygen_pct\": 35.928, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.075, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.851, \"our_mmol_l_h\": 6.263, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.599, \"viable_cell_density_million_ml\": 18.422}, {\"agitation_rpm\": 284.855, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.511, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.059, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.716, \"our_mmol_l_h\": 6.477, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.617, \"viable_cell_density_million_ml\": 20.043}, {\"agitation_rpm\": 285.097, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.987, \"dissolved_oxygen_pct\": 35.997, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.634, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.614, \"our_mmol_l_h\": 6.399, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.047, \"viable_cell_density_million_ml\": 21.457}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-90135690003a31938c09", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.068, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.196, \"dissolved_oxygen_pct\": 36.94, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.095, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 5.656, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.798, \"viability_pct\": 98.382, \"viable_cell_density_million_ml\": 16.706}, {\"agitation_rpm\": 271.457, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.763, \"dissolved_oxygen_pct\": 35.928, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.075, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.851, \"our_mmol_l_h\": 6.263, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.599, \"viable_cell_density_million_ml\": 18.422}, {\"agitation_rpm\": 284.855, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.511, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.059, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.716, \"our_mmol_l_h\": 6.477, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.617, \"viable_cell_density_million_ml\": 20.043}, {\"agitation_rpm\": 285.097, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.987, \"dissolved_oxygen_pct\": 35.997, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.634, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.614, \"our_mmol_l_h\": 6.399, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.047, \"viable_cell_density_million_ml\": 21.457}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f7ddb652c2b95517c088", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.068, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.196, \"dissolved_oxygen_pct\": 36.94, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.095, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 5.656, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.798, \"viability_pct\": 98.382, \"viable_cell_density_million_ml\": 16.706}, {\"agitation_rpm\": 271.457, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.763, \"dissolved_oxygen_pct\": 35.928, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.075, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.851, \"our_mmol_l_h\": 6.263, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.599, \"viable_cell_density_million_ml\": 18.422}, {\"agitation_rpm\": 284.855, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.511, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.059, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.716, \"our_mmol_l_h\": 6.477, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.617, \"viable_cell_density_million_ml\": 20.043}, {\"agitation_rpm\": 285.097, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.987, \"dissolved_oxygen_pct\": 35.997, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.634, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.614, \"our_mmol_l_h\": 6.399, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.047, \"viable_cell_density_million_ml\": 21.457}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9459830294d95c3ec666", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.163, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.003, \"dissolved_oxygen_pct\": 35.752, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.027, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.813, \"our_mmol_l_h\": 6.519, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.52, \"viable_cell_density_million_ml\": 19.14}, {\"agitation_rpm\": 285.172, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.421, \"dissolved_oxygen_pct\": 36.988, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.943, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 7.012, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.514, \"viable_cell_density_million_ml\": 20.517}, {\"agitation_rpm\": 285.541, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.863, \"dissolved_oxygen_pct\": 28.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.858, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.624, \"our_mmol_l_h\": 7.486, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.643, \"viable_cell_density_million_ml\": 22.023}, {\"agitation_rpm\": 284.241, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.18, \"dissolved_oxygen_pct\": 18.787, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.79, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.487, \"our_mmol_l_h\": 7.843, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 96.59, \"viable_cell_density_million_ml\": 23.037}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ca39bf9177bc165c3370", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.163, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.003, \"dissolved_oxygen_pct\": 35.752, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.027, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.813, \"our_mmol_l_h\": 6.519, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.52, \"viable_cell_density_million_ml\": 19.14}, {\"agitation_rpm\": 285.172, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.421, \"dissolved_oxygen_pct\": 36.988, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.943, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 7.012, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.514, \"viable_cell_density_million_ml\": 20.517}, {\"agitation_rpm\": 285.541, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.863, \"dissolved_oxygen_pct\": 28.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.858, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.624, \"our_mmol_l_h\": 7.486, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.643, \"viable_cell_density_million_ml\": 22.023}, {\"agitation_rpm\": 284.241, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.18, \"dissolved_oxygen_pct\": 18.787, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.79, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.487, \"our_mmol_l_h\": 7.843, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 96.59, \"viable_cell_density_million_ml\": 23.037}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cdfad659920142f95339", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.163, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.003, \"dissolved_oxygen_pct\": 35.752, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.027, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.813, \"our_mmol_l_h\": 6.519, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.52, \"viable_cell_density_million_ml\": 19.14}, {\"agitation_rpm\": 285.172, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.421, \"dissolved_oxygen_pct\": 36.988, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.943, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 7.012, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.514, \"viable_cell_density_million_ml\": 20.517}, {\"agitation_rpm\": 285.541, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.863, \"dissolved_oxygen_pct\": 28.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.858, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.624, \"our_mmol_l_h\": 7.486, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.643, \"viable_cell_density_million_ml\": 22.023}, {\"agitation_rpm\": 284.241, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.18, \"dissolved_oxygen_pct\": 18.787, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.79, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.487, \"our_mmol_l_h\": 7.843, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 96.59, \"viable_cell_density_million_ml\": 23.037}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ebe670e62cedc5e589c2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.163, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.003, \"dissolved_oxygen_pct\": 35.752, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.027, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.813, \"our_mmol_l_h\": 6.519, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.52, \"viable_cell_density_million_ml\": 19.14}, {\"agitation_rpm\": 285.172, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.421, \"dissolved_oxygen_pct\": 36.988, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.943, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 7.012, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.514, \"viable_cell_density_million_ml\": 20.517}, {\"agitation_rpm\": 285.541, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.863, \"dissolved_oxygen_pct\": 28.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.858, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.624, \"our_mmol_l_h\": 7.486, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.643, \"viable_cell_density_million_ml\": 22.023}, {\"agitation_rpm\": 284.241, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.18, \"dissolved_oxygen_pct\": 18.787, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.79, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.487, \"our_mmol_l_h\": 7.843, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 96.59, \"viable_cell_density_million_ml\": 23.037}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1416a12ebc15e05cdc17", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.769, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.551, \"dissolved_oxygen_pct\": 35.894, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.358, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 7.125, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.92, \"viable_cell_density_million_ml\": 20.963}, {\"agitation_rpm\": 286.022, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.519, \"dissolved_oxygen_pct\": 36.505, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.323, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.16, \"our_mmol_l_h\": 8.196, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.693, \"viable_cell_density_million_ml\": 24.071}, {\"agitation_rpm\": 285.432, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.817, \"dissolved_oxygen_pct\": 36.636, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.496, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.124, \"our_mmol_l_h\": 9.111, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.864, \"viable_cell_density_million_ml\": 27.856}, {\"agitation_rpm\": 284.773, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 10.151, \"dissolved_oxygen_pct\": 36.199, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.41, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.023, \"our_mmol_l_h\": 9.797, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.928, \"viable_cell_density_million_ml\": 31.351}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9178631294ce3470b122", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.769, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.551, \"dissolved_oxygen_pct\": 35.894, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.358, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 7.125, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.92, \"viable_cell_density_million_ml\": 20.963}, {\"agitation_rpm\": 286.022, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.519, \"dissolved_oxygen_pct\": 36.505, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.323, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.16, \"our_mmol_l_h\": 8.196, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.693, \"viable_cell_density_million_ml\": 24.071}, {\"agitation_rpm\": 285.432, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.817, \"dissolved_oxygen_pct\": 36.636, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.496, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.124, \"our_mmol_l_h\": 9.111, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.864, \"viable_cell_density_million_ml\": 27.856}, {\"agitation_rpm\": 284.773, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 10.151, \"dissolved_oxygen_pct\": 36.199, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.41, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.023, \"our_mmol_l_h\": 9.797, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.928, \"viable_cell_density_million_ml\": 31.351}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1ffd28e62f0bd972e9cb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.769, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.551, \"dissolved_oxygen_pct\": 35.894, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.358, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 7.125, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.92, \"viable_cell_density_million_ml\": 20.963}, {\"agitation_rpm\": 286.022, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.519, \"dissolved_oxygen_pct\": 36.505, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.323, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.16, \"our_mmol_l_h\": 8.196, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.693, \"viable_cell_density_million_ml\": 24.071}, {\"agitation_rpm\": 285.432, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.817, \"dissolved_oxygen_pct\": 36.636, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.496, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.124, \"our_mmol_l_h\": 9.111, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.864, \"viable_cell_density_million_ml\": 27.856}, {\"agitation_rpm\": 284.773, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 10.151, \"dissolved_oxygen_pct\": 36.199, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.41, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.023, \"our_mmol_l_h\": 9.797, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.928, \"viable_cell_density_million_ml\": 31.351}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e32ad94e8af747bfac8c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.769, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.551, \"dissolved_oxygen_pct\": 35.894, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.358, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 7.125, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.92, \"viable_cell_density_million_ml\": 20.963}, {\"agitation_rpm\": 286.022, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.519, \"dissolved_oxygen_pct\": 36.505, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.323, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.16, \"our_mmol_l_h\": 8.196, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.693, \"viable_cell_density_million_ml\": 24.071}, {\"agitation_rpm\": 285.432, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.817, \"dissolved_oxygen_pct\": 36.636, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.496, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.124, \"our_mmol_l_h\": 9.111, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.864, \"viable_cell_density_million_ml\": 27.856}, {\"agitation_rpm\": 284.773, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 10.151, \"dissolved_oxygen_pct\": 36.199, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.41, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.023, \"our_mmol_l_h\": 9.797, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.928, \"viable_cell_density_million_ml\": 31.351}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e85d109b7281e9d0c153", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.125, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.994, \"dissolved_oxygen_pct\": 36.454, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.349, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.121, \"our_mmol_l_h\": 8.687, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.833, \"viable_cell_density_million_ml\": 25.558}, {\"agitation_rpm\": 284.608, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 8.844, \"dissolved_oxygen_pct\": 36.878, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.393, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.102, \"our_mmol_l_h\": 9.578, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.915, \"viable_cell_density_million_ml\": 28.273}, {\"agitation_rpm\": 285.475, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 9.851, \"dissolved_oxygen_pct\": 29.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.536, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.043, \"our_mmol_l_h\": 10.714, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 31.53}, {\"agitation_rpm\": 285.511, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.809, \"dissolved_oxygen_pct\": 18.481, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.729, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.01, \"our_mmol_l_h\": 11.774, \"ph\": 7.013, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.773, \"viable_cell_density_million_ml\": 34.513}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-040ee0d9d31e18ad9d88", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.125, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.994, \"dissolved_oxygen_pct\": 36.454, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.349, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.121, \"our_mmol_l_h\": 8.687, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.833, \"viable_cell_density_million_ml\": 25.558}, {\"agitation_rpm\": 284.608, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 8.844, \"dissolved_oxygen_pct\": 36.878, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.393, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.102, \"our_mmol_l_h\": 9.578, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.915, \"viable_cell_density_million_ml\": 28.273}, {\"agitation_rpm\": 285.475, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 9.851, \"dissolved_oxygen_pct\": 29.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.536, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.043, \"our_mmol_l_h\": 10.714, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 31.53}, {\"agitation_rpm\": 285.511, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.809, \"dissolved_oxygen_pct\": 18.481, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.729, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.01, \"our_mmol_l_h\": 11.774, \"ph\": 7.013, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.773, \"viable_cell_density_million_ml\": 34.513}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3bf83acf523743348735", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.125, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.994, \"dissolved_oxygen_pct\": 36.454, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.349, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.121, \"our_mmol_l_h\": 8.687, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.833, \"viable_cell_density_million_ml\": 25.558}, {\"agitation_rpm\": 284.608, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 8.844, \"dissolved_oxygen_pct\": 36.878, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.393, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.102, \"our_mmol_l_h\": 9.578, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.915, \"viable_cell_density_million_ml\": 28.273}, {\"agitation_rpm\": 285.475, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 9.851, \"dissolved_oxygen_pct\": 29.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.536, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.043, \"our_mmol_l_h\": 10.714, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 31.53}, {\"agitation_rpm\": 285.511, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.809, \"dissolved_oxygen_pct\": 18.481, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.729, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.01, \"our_mmol_l_h\": 11.774, \"ph\": 7.013, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.773, \"viable_cell_density_million_ml\": 34.513}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-342ff208c33a8cb7c700", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.125, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.994, \"dissolved_oxygen_pct\": 36.454, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.349, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.121, \"our_mmol_l_h\": 8.687, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.833, \"viable_cell_density_million_ml\": 25.558}, {\"agitation_rpm\": 284.608, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 8.844, \"dissolved_oxygen_pct\": 36.878, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.393, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.102, \"our_mmol_l_h\": 9.578, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.915, \"viable_cell_density_million_ml\": 28.273}, {\"agitation_rpm\": 285.475, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 9.851, \"dissolved_oxygen_pct\": 29.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.536, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.043, \"our_mmol_l_h\": 10.714, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 31.53}, {\"agitation_rpm\": 285.511, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.809, \"dissolved_oxygen_pct\": 18.481, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.729, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.01, \"our_mmol_l_h\": 11.774, \"ph\": 7.013, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.773, \"viable_cell_density_million_ml\": 34.513}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-10df0fb17144d4b3d03b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.733, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.209, \"dissolved_oxygen_pct\": 37.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.083, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.917, \"our_mmol_l_h\": 5.63, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.611, \"viable_cell_density_million_ml\": 16.693}, {\"agitation_rpm\": 271.326, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 36.191, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.237, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 18.349}, {\"agitation_rpm\": 285.924, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.45, \"dissolved_oxygen_pct\": 36.422, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.999, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.735, \"our_mmol_l_h\": 6.48, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.425, \"viable_cell_density_million_ml\": 20.052}, {\"agitation_rpm\": 285.615, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.98, \"dissolved_oxygen_pct\": 35.982, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.692, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.573, \"our_mmol_l_h\": 6.384, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.012, \"viable_cell_density_million_ml\": 21.437}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ca19810a9954f9a77b6e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.733, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.209, \"dissolved_oxygen_pct\": 37.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.083, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.917, \"our_mmol_l_h\": 5.63, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.611, \"viable_cell_density_million_ml\": 16.693}, {\"agitation_rpm\": 271.326, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 36.191, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.237, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 18.349}, {\"agitation_rpm\": 285.924, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.45, \"dissolved_oxygen_pct\": 36.422, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.999, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.735, \"our_mmol_l_h\": 6.48, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.425, \"viable_cell_density_million_ml\": 20.052}, {\"agitation_rpm\": 285.615, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.98, \"dissolved_oxygen_pct\": 35.982, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.692, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.573, \"our_mmol_l_h\": 6.384, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.012, \"viable_cell_density_million_ml\": 21.437}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ffc3e6ccb04aca6727af", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.733, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.209, \"dissolved_oxygen_pct\": 37.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.083, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.917, \"our_mmol_l_h\": 5.63, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.611, \"viable_cell_density_million_ml\": 16.693}, {\"agitation_rpm\": 271.326, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 36.191, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.237, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 18.349}, {\"agitation_rpm\": 285.924, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.45, \"dissolved_oxygen_pct\": 36.422, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.999, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.735, \"our_mmol_l_h\": 6.48, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.425, \"viable_cell_density_million_ml\": 20.052}, {\"agitation_rpm\": 285.615, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.98, \"dissolved_oxygen_pct\": 35.982, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.692, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.573, \"our_mmol_l_h\": 6.384, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.012, \"viable_cell_density_million_ml\": 21.437}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8fdb2ec08daec5bcf0d2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.733, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.209, \"dissolved_oxygen_pct\": 37.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.083, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.917, \"our_mmol_l_h\": 5.63, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.611, \"viable_cell_density_million_ml\": 16.693}, {\"agitation_rpm\": 271.326, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 36.191, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.237, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 18.349}, {\"agitation_rpm\": 285.924, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.45, \"dissolved_oxygen_pct\": 36.422, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.999, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.735, \"our_mmol_l_h\": 6.48, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.425, \"viable_cell_density_million_ml\": 20.052}, {\"agitation_rpm\": 285.615, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.98, \"dissolved_oxygen_pct\": 35.982, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.692, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.573, \"our_mmol_l_h\": 6.384, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.012, \"viable_cell_density_million_ml\": 21.437}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e3c79a1cebe02a5e723e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.614, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.013, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.99, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.791, \"our_mmol_l_h\": 6.54, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.511, \"viable_cell_density_million_ml\": 19.163}, {\"agitation_rpm\": 284.198, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.42, \"dissolved_oxygen_pct\": 36.467, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.944, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.749, \"our_mmol_l_h\": 6.958, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 20.579}, {\"agitation_rpm\": 284.042, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 6.905, \"dissolved_oxygen_pct\": 29.276, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.873, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.602, \"our_mmol_l_h\": 7.485, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.408, \"viable_cell_density_million_ml\": 21.954}, {\"agitation_rpm\": 284.268, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.204, \"dissolved_oxygen_pct\": 18.765, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.771, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.46, \"our_mmol_l_h\": 7.813, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 96.596, \"viable_cell_density_million_ml\": 22.997}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5821d7067bcf4f3fd290", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.614, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.013, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.99, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.791, \"our_mmol_l_h\": 6.54, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.511, \"viable_cell_density_million_ml\": 19.163}, {\"agitation_rpm\": 284.198, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.42, \"dissolved_oxygen_pct\": 36.467, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.944, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.749, \"our_mmol_l_h\": 6.958, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 20.579}, {\"agitation_rpm\": 284.042, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 6.905, \"dissolved_oxygen_pct\": 29.276, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.873, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.602, \"our_mmol_l_h\": 7.485, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.408, \"viable_cell_density_million_ml\": 21.954}, {\"agitation_rpm\": 284.268, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.204, \"dissolved_oxygen_pct\": 18.765, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.771, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.46, \"our_mmol_l_h\": 7.813, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 96.596, \"viable_cell_density_million_ml\": 22.997}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4715c40474575f7f1edd", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.614, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.013, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.99, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.791, \"our_mmol_l_h\": 6.54, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.511, \"viable_cell_density_million_ml\": 19.163}, {\"agitation_rpm\": 284.198, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.42, \"dissolved_oxygen_pct\": 36.467, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.944, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.749, \"our_mmol_l_h\": 6.958, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 20.579}, {\"agitation_rpm\": 284.042, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 6.905, \"dissolved_oxygen_pct\": 29.276, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.873, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.602, \"our_mmol_l_h\": 7.485, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.408, \"viable_cell_density_million_ml\": 21.954}, {\"agitation_rpm\": 284.268, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.204, \"dissolved_oxygen_pct\": 18.765, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.771, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.46, \"our_mmol_l_h\": 7.813, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 96.596, \"viable_cell_density_million_ml\": 22.997}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-49698235dfb0e8201497", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.614, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.013, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.99, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.791, \"our_mmol_l_h\": 6.54, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.511, \"viable_cell_density_million_ml\": 19.163}, {\"agitation_rpm\": 284.198, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.42, \"dissolved_oxygen_pct\": 36.467, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.944, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.749, \"our_mmol_l_h\": 6.958, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 20.579}, {\"agitation_rpm\": 284.042, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 6.905, \"dissolved_oxygen_pct\": 29.276, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.873, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.602, \"our_mmol_l_h\": 7.485, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.408, \"viable_cell_density_million_ml\": 21.954}, {\"agitation_rpm\": 284.268, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.204, \"dissolved_oxygen_pct\": 18.765, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.771, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.46, \"our_mmol_l_h\": 7.813, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 96.596, \"viable_cell_density_million_ml\": 22.997}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-070195b9e71727fb7123", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.7, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.714, \"dissolved_oxygen_pct\": 40.557, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.549, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 4.088, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.8, \"viability_pct\": 98.669, \"viable_cell_density_million_ml\": 11.987}, {\"agitation_rpm\": 237.676, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 4.656, \"dissolved_oxygen_pct\": 38.728, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.49, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.26, \"our_mmol_l_h\": 5.023, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.854, \"viable_cell_density_million_ml\": 14.84}, {\"agitation_rpm\": 279.743, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.106, \"dissolved_oxygen_pct\": 36.608, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.443, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 6.084, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.79, \"viability_pct\": 98.82, \"viable_cell_density_million_ml\": 19.066}, {\"agitation_rpm\": 285.168, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.022, \"dissolved_oxygen_pct\": 36.022, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.133, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.487, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.676, \"viable_cell_density_million_ml\": 24.572}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-777942705c3b36375aca", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.7, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.714, \"dissolved_oxygen_pct\": 40.557, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.549, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 4.088, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.8, \"viability_pct\": 98.669, \"viable_cell_density_million_ml\": 11.987}, {\"agitation_rpm\": 237.676, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 4.656, \"dissolved_oxygen_pct\": 38.728, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.49, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.26, \"our_mmol_l_h\": 5.023, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.854, \"viable_cell_density_million_ml\": 14.84}, {\"agitation_rpm\": 279.743, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.106, \"dissolved_oxygen_pct\": 36.608, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.443, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 6.084, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.79, \"viability_pct\": 98.82, \"viable_cell_density_million_ml\": 19.066}, {\"agitation_rpm\": 285.168, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.022, \"dissolved_oxygen_pct\": 36.022, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.133, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.487, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.676, \"viable_cell_density_million_ml\": 24.572}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-45ca65a42ce050cd3e80", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.7, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.714, \"dissolved_oxygen_pct\": 40.557, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.549, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 4.088, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.8, \"viability_pct\": 98.669, \"viable_cell_density_million_ml\": 11.987}, {\"agitation_rpm\": 237.676, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 4.656, \"dissolved_oxygen_pct\": 38.728, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.49, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.26, \"our_mmol_l_h\": 5.023, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.854, \"viable_cell_density_million_ml\": 14.84}, {\"agitation_rpm\": 279.743, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.106, \"dissolved_oxygen_pct\": 36.608, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.443, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 6.084, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.79, \"viability_pct\": 98.82, \"viable_cell_density_million_ml\": 19.066}, {\"agitation_rpm\": 285.168, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.022, \"dissolved_oxygen_pct\": 36.022, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.133, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.487, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.676, \"viable_cell_density_million_ml\": 24.572}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b4946c1d8e8ffec4da51", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.7, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.714, \"dissolved_oxygen_pct\": 40.557, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.549, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 4.088, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.8, \"viability_pct\": 98.669, \"viable_cell_density_million_ml\": 11.987}, {\"agitation_rpm\": 237.676, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 4.656, \"dissolved_oxygen_pct\": 38.728, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.49, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.26, \"our_mmol_l_h\": 5.023, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.854, \"viable_cell_density_million_ml\": 14.84}, {\"agitation_rpm\": 279.743, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.106, \"dissolved_oxygen_pct\": 36.608, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.443, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 6.084, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.79, \"viability_pct\": 98.82, \"viable_cell_density_million_ml\": 19.066}, {\"agitation_rpm\": 285.168, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.022, \"dissolved_oxygen_pct\": 36.022, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.133, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.487, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.676, \"viable_cell_density_million_ml\": 24.572}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-83d3ed15f0bee331ed7b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.725, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.013, \"dissolved_oxygen_pct\": 35.947, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.347, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 8.679, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.753, \"viable_cell_density_million_ml\": 25.544}, {\"agitation_rpm\": 285.036, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.801, \"dissolved_oxygen_pct\": 35.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.373, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.62, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.673, \"viable_cell_density_million_ml\": 28.277}, {\"agitation_rpm\": 285.994, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 9.842, \"dissolved_oxygen_pct\": 28.562, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.54, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.04, \"our_mmol_l_h\": 10.731, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.764, \"viable_cell_density_million_ml\": 31.561}, {\"agitation_rpm\": 285.031, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.779, \"dissolved_oxygen_pct\": 18.943, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.755, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.989, \"our_mmol_l_h\": 11.741, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.693, \"viable_cell_density_million_ml\": 34.523}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c7edef123b6f0e3cdf5f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.725, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.013, \"dissolved_oxygen_pct\": 35.947, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.347, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 8.679, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.753, \"viable_cell_density_million_ml\": 25.544}, {\"agitation_rpm\": 285.036, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.801, \"dissolved_oxygen_pct\": 35.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.373, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.62, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.673, \"viable_cell_density_million_ml\": 28.277}, {\"agitation_rpm\": 285.994, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 9.842, \"dissolved_oxygen_pct\": 28.562, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.54, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.04, \"our_mmol_l_h\": 10.731, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.764, \"viable_cell_density_million_ml\": 31.561}, {\"agitation_rpm\": 285.031, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.779, \"dissolved_oxygen_pct\": 18.943, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.755, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.989, \"our_mmol_l_h\": 11.741, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.693, \"viable_cell_density_million_ml\": 34.523}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c31f7c776872c03f58a0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.725, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.013, \"dissolved_oxygen_pct\": 35.947, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.347, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 8.679, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.753, \"viable_cell_density_million_ml\": 25.544}, {\"agitation_rpm\": 285.036, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.801, \"dissolved_oxygen_pct\": 35.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.373, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.62, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.673, \"viable_cell_density_million_ml\": 28.277}, {\"agitation_rpm\": 285.994, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 9.842, \"dissolved_oxygen_pct\": 28.562, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.54, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.04, \"our_mmol_l_h\": 10.731, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.764, \"viable_cell_density_million_ml\": 31.561}, {\"agitation_rpm\": 285.031, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.779, \"dissolved_oxygen_pct\": 18.943, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.755, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.989, \"our_mmol_l_h\": 11.741, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.693, \"viable_cell_density_million_ml\": 34.523}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-82cc5f7ed5fd30388511", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.725, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.013, \"dissolved_oxygen_pct\": 35.947, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.347, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 8.679, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.753, \"viable_cell_density_million_ml\": 25.544}, {\"agitation_rpm\": 285.036, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.801, \"dissolved_oxygen_pct\": 35.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.373, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.62, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.673, \"viable_cell_density_million_ml\": 28.277}, {\"agitation_rpm\": 285.994, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 9.842, \"dissolved_oxygen_pct\": 28.562, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.54, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.04, \"our_mmol_l_h\": 10.731, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.764, \"viable_cell_density_million_ml\": 31.561}, {\"agitation_rpm\": 285.031, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.779, \"dissolved_oxygen_pct\": 18.943, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.755, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.989, \"our_mmol_l_h\": 11.741, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.693, \"viable_cell_density_million_ml\": 34.523}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-177a64df7b567469f204", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.639, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 6.03, \"dissolved_oxygen_pct\": 36.783, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.01, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.801, \"our_mmol_l_h\": 6.53, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.408, \"viable_cell_density_million_ml\": 19.19}, {\"agitation_rpm\": 285.029, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.435, \"dissolved_oxygen_pct\": 36.261, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.955, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.733, \"our_mmol_l_h\": 6.993, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.417, \"viable_cell_density_million_ml\": 20.569}, {\"agitation_rpm\": 285.559, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.875, \"dissolved_oxygen_pct\": 36.463, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.971, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.61, \"our_mmol_l_h\": 6.956, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 21.509}, {\"agitation_rpm\": 284.924, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.178, \"dissolved_oxygen_pct\": 36.887, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.6, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.506, \"our_mmol_l_h\": 6.587, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 96.58, \"viable_cell_density_million_ml\": 21.941}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2f0a6f7aa7075a388f67", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.639, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 6.03, \"dissolved_oxygen_pct\": 36.783, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.01, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.801, \"our_mmol_l_h\": 6.53, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.408, \"viable_cell_density_million_ml\": 19.19}, {\"agitation_rpm\": 285.029, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.435, \"dissolved_oxygen_pct\": 36.261, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.955, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.733, \"our_mmol_l_h\": 6.993, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.417, \"viable_cell_density_million_ml\": 20.569}, {\"agitation_rpm\": 285.559, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.875, \"dissolved_oxygen_pct\": 36.463, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.971, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.61, \"our_mmol_l_h\": 6.956, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 21.509}, {\"agitation_rpm\": 284.924, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.178, \"dissolved_oxygen_pct\": 36.887, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.6, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.506, \"our_mmol_l_h\": 6.587, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 96.58, \"viable_cell_density_million_ml\": 21.941}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-57770ee034563e0c4e83", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.639, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 6.03, \"dissolved_oxygen_pct\": 36.783, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.01, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.801, \"our_mmol_l_h\": 6.53, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.408, \"viable_cell_density_million_ml\": 19.19}, {\"agitation_rpm\": 285.029, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.435, \"dissolved_oxygen_pct\": 36.261, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.955, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.733, \"our_mmol_l_h\": 6.993, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.417, \"viable_cell_density_million_ml\": 20.569}, {\"agitation_rpm\": 285.559, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.875, \"dissolved_oxygen_pct\": 36.463, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.971, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.61, \"our_mmol_l_h\": 6.956, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 21.509}, {\"agitation_rpm\": 284.924, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.178, \"dissolved_oxygen_pct\": 36.887, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.6, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.506, \"our_mmol_l_h\": 6.587, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 96.58, \"viable_cell_density_million_ml\": 21.941}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-192daf3fbc00981144e4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.639, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 6.03, \"dissolved_oxygen_pct\": 36.783, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.01, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.801, \"our_mmol_l_h\": 6.53, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.408, \"viable_cell_density_million_ml\": 19.19}, {\"agitation_rpm\": 285.029, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.435, \"dissolved_oxygen_pct\": 36.261, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.955, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.733, \"our_mmol_l_h\": 6.993, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.417, \"viable_cell_density_million_ml\": 20.569}, {\"agitation_rpm\": 285.559, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.875, \"dissolved_oxygen_pct\": 36.463, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.971, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.61, \"our_mmol_l_h\": 6.956, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 21.509}, {\"agitation_rpm\": 284.924, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.178, \"dissolved_oxygen_pct\": 36.887, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.6, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.506, \"our_mmol_l_h\": 6.587, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 96.58, \"viable_cell_density_million_ml\": 21.941}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bb8ac3e7ecdede1ccbb9", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.178, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.022, \"dissolved_oxygen_pct\": 36.135, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.027, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.819, \"our_mmol_l_h\": 6.554, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.619, \"viable_cell_density_million_ml\": 19.215}, {\"agitation_rpm\": 285.681, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.446, \"dissolved_oxygen_pct\": 36.092, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.929, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.743, \"our_mmol_l_h\": 6.981, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.508, \"viable_cell_density_million_ml\": 20.531}, {\"agitation_rpm\": 285.936, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 6.912, \"dissolved_oxygen_pct\": 28.598, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.881, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.617, \"our_mmol_l_h\": 7.477, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.353, \"viable_cell_density_million_ml\": 21.938}, {\"agitation_rpm\": 284.055, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.185, \"dissolved_oxygen_pct\": 17.623, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.762, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.829, \"ph\": 6.99, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 96.659, \"viable_cell_density_million_ml\": 22.968}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c7da137d155209611f47", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.178, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.022, \"dissolved_oxygen_pct\": 36.135, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.027, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.819, \"our_mmol_l_h\": 6.554, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.619, \"viable_cell_density_million_ml\": 19.215}, {\"agitation_rpm\": 285.681, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.446, \"dissolved_oxygen_pct\": 36.092, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.929, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.743, \"our_mmol_l_h\": 6.981, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.508, \"viable_cell_density_million_ml\": 20.531}, {\"agitation_rpm\": 285.936, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 6.912, \"dissolved_oxygen_pct\": 28.598, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.881, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.617, \"our_mmol_l_h\": 7.477, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.353, \"viable_cell_density_million_ml\": 21.938}, {\"agitation_rpm\": 284.055, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.185, \"dissolved_oxygen_pct\": 17.623, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.762, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.829, \"ph\": 6.99, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 96.659, \"viable_cell_density_million_ml\": 22.968}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e9397597d815f764fa8c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.178, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.022, \"dissolved_oxygen_pct\": 36.135, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.027, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.819, \"our_mmol_l_h\": 6.554, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.619, \"viable_cell_density_million_ml\": 19.215}, {\"agitation_rpm\": 285.681, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.446, \"dissolved_oxygen_pct\": 36.092, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.929, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.743, \"our_mmol_l_h\": 6.981, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.508, \"viable_cell_density_million_ml\": 20.531}, {\"agitation_rpm\": 285.936, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 6.912, \"dissolved_oxygen_pct\": 28.598, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.881, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.617, \"our_mmol_l_h\": 7.477, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.353, \"viable_cell_density_million_ml\": 21.938}, {\"agitation_rpm\": 284.055, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.185, \"dissolved_oxygen_pct\": 17.623, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.762, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.829, \"ph\": 6.99, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 96.659, \"viable_cell_density_million_ml\": 22.968}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6d3badcb2bdf10cd9a77", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.178, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.022, \"dissolved_oxygen_pct\": 36.135, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.027, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.819, \"our_mmol_l_h\": 6.554, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.619, \"viable_cell_density_million_ml\": 19.215}, {\"agitation_rpm\": 285.681, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.446, \"dissolved_oxygen_pct\": 36.092, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.929, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.743, \"our_mmol_l_h\": 6.981, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.508, \"viable_cell_density_million_ml\": 20.531}, {\"agitation_rpm\": 285.936, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 6.912, \"dissolved_oxygen_pct\": 28.598, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.881, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.617, \"our_mmol_l_h\": 7.477, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.353, \"viable_cell_density_million_ml\": 21.938}, {\"agitation_rpm\": 284.055, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.185, \"dissolved_oxygen_pct\": 17.623, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.762, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.829, \"ph\": 6.99, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 96.659, \"viable_cell_density_million_ml\": 22.968}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a6176b0f1b6d171cd252", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.253, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.549, \"dissolved_oxygen_pct\": 36.496, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.21, \"our_mmol_l_h\": 7.121, \"ph\": 6.991, \"phase\": \"exponential\", \"temperature_c\": 36.792, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 21.099}, {\"agitation_rpm\": 285.269, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.564, \"dissolved_oxygen_pct\": 36.225, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.32, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.149, \"our_mmol_l_h\": 8.206, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.711, \"viable_cell_density_million_ml\": 24.176}, {\"agitation_rpm\": 284.406, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.821, \"dissolved_oxygen_pct\": 36.614, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.486, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.092, \"our_mmol_l_h\": 9.085, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.826, \"viable_cell_density_million_ml\": 27.853}, {\"agitation_rpm\": 285.713, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.166, \"dissolved_oxygen_pct\": 36.462, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.411, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.065, \"our_mmol_l_h\": 9.846, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.784, \"viable_cell_density_million_ml\": 31.443}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8385aee5e455de3c48c8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.253, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.549, \"dissolved_oxygen_pct\": 36.496, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.21, \"our_mmol_l_h\": 7.121, \"ph\": 6.991, \"phase\": \"exponential\", \"temperature_c\": 36.792, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 21.099}, {\"agitation_rpm\": 285.269, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.564, \"dissolved_oxygen_pct\": 36.225, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.32, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.149, \"our_mmol_l_h\": 8.206, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.711, \"viable_cell_density_million_ml\": 24.176}, {\"agitation_rpm\": 284.406, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.821, \"dissolved_oxygen_pct\": 36.614, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.486, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.092, \"our_mmol_l_h\": 9.085, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.826, \"viable_cell_density_million_ml\": 27.853}, {\"agitation_rpm\": 285.713, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.166, \"dissolved_oxygen_pct\": 36.462, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.411, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.065, \"our_mmol_l_h\": 9.846, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.784, \"viable_cell_density_million_ml\": 31.443}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2934e50f1869fb33b84d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.253, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.549, \"dissolved_oxygen_pct\": 36.496, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.21, \"our_mmol_l_h\": 7.121, \"ph\": 6.991, \"phase\": \"exponential\", \"temperature_c\": 36.792, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 21.099}, {\"agitation_rpm\": 285.269, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.564, \"dissolved_oxygen_pct\": 36.225, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.32, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.149, \"our_mmol_l_h\": 8.206, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.711, \"viable_cell_density_million_ml\": 24.176}, {\"agitation_rpm\": 284.406, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.821, \"dissolved_oxygen_pct\": 36.614, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.486, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.092, \"our_mmol_l_h\": 9.085, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.826, \"viable_cell_density_million_ml\": 27.853}, {\"agitation_rpm\": 285.713, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.166, \"dissolved_oxygen_pct\": 36.462, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.411, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.065, \"our_mmol_l_h\": 9.846, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.784, \"viable_cell_density_million_ml\": 31.443}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0ecb45ec0a0db6fafb8c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.253, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.549, \"dissolved_oxygen_pct\": 36.496, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.21, \"our_mmol_l_h\": 7.121, \"ph\": 6.991, \"phase\": \"exponential\", \"temperature_c\": 36.792, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 21.099}, {\"agitation_rpm\": 285.269, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.564, \"dissolved_oxygen_pct\": 36.225, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.32, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.149, \"our_mmol_l_h\": 8.206, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.711, \"viable_cell_density_million_ml\": 24.176}, {\"agitation_rpm\": 284.406, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.821, \"dissolved_oxygen_pct\": 36.614, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.486, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.092, \"our_mmol_l_h\": 9.085, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.826, \"viable_cell_density_million_ml\": 27.853}, {\"agitation_rpm\": 285.713, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.166, \"dissolved_oxygen_pct\": 36.462, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.411, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.065, \"our_mmol_l_h\": 9.846, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.784, \"viable_cell_density_million_ml\": 31.443}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8e065039ba724ef4a173", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.013, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.097, \"dissolved_oxygen_pct\": 37.671, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.411, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.23, \"our_mmol_l_h\": 5.56, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.866, \"viable_cell_density_million_ml\": 16.352}, {\"agitation_rpm\": 280.839, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.087, \"dissolved_oxygen_pct\": 36.085, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.19, \"our_mmol_l_h\": 6.586, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.815, \"viable_cell_density_million_ml\": 19.48}, {\"agitation_rpm\": 285.161, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 7.547, \"dissolved_oxygen_pct\": 28.822, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.319, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.186, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.81, \"viable_cell_density_million_ml\": 24.137}, {\"agitation_rpm\": 284.999, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 9.188, \"dissolved_oxygen_pct\": 17.906, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.442, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.085, \"our_mmol_l_h\": 9.977, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.815, \"viable_cell_density_million_ml\": 29.484}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d7d987681d87b67068c6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.013, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.097, \"dissolved_oxygen_pct\": 37.671, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.411, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.23, \"our_mmol_l_h\": 5.56, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.866, \"viable_cell_density_million_ml\": 16.352}, {\"agitation_rpm\": 280.839, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.087, \"dissolved_oxygen_pct\": 36.085, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.19, \"our_mmol_l_h\": 6.586, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.815, \"viable_cell_density_million_ml\": 19.48}, {\"agitation_rpm\": 285.161, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 7.547, \"dissolved_oxygen_pct\": 28.822, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.319, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.186, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.81, \"viable_cell_density_million_ml\": 24.137}, {\"agitation_rpm\": 284.999, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 9.188, \"dissolved_oxygen_pct\": 17.906, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.442, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.085, \"our_mmol_l_h\": 9.977, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.815, \"viable_cell_density_million_ml\": 29.484}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e1f87f4a98fceab52a21", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.013, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.097, \"dissolved_oxygen_pct\": 37.671, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.411, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.23, \"our_mmol_l_h\": 5.56, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.866, \"viable_cell_density_million_ml\": 16.352}, {\"agitation_rpm\": 280.839, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.087, \"dissolved_oxygen_pct\": 36.085, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.19, \"our_mmol_l_h\": 6.586, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.815, \"viable_cell_density_million_ml\": 19.48}, {\"agitation_rpm\": 285.161, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 7.547, \"dissolved_oxygen_pct\": 28.822, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.319, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.186, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.81, \"viable_cell_density_million_ml\": 24.137}, {\"agitation_rpm\": 284.999, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 9.188, \"dissolved_oxygen_pct\": 17.906, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.442, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.085, \"our_mmol_l_h\": 9.977, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.815, \"viable_cell_density_million_ml\": 29.484}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4ab14935716558231793", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.013, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.097, \"dissolved_oxygen_pct\": 37.671, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.411, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.23, \"our_mmol_l_h\": 5.56, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.866, \"viable_cell_density_million_ml\": 16.352}, {\"agitation_rpm\": 280.839, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.087, \"dissolved_oxygen_pct\": 36.085, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.19, \"our_mmol_l_h\": 6.586, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.815, \"viable_cell_density_million_ml\": 19.48}, {\"agitation_rpm\": 285.161, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 7.547, \"dissolved_oxygen_pct\": 28.822, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.319, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.186, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.81, \"viable_cell_density_million_ml\": 24.137}, {\"agitation_rpm\": 284.999, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 9.188, \"dissolved_oxygen_pct\": 17.906, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.442, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.085, \"our_mmol_l_h\": 9.977, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.815, \"viable_cell_density_million_ml\": 29.484}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ed233f06ee04f64dda90", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.591, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.212, \"dissolved_oxygen_pct\": 37.546, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.109, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.931, \"our_mmol_l_h\": 5.639, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.442, \"viable_cell_density_million_ml\": 16.601}, {\"agitation_rpm\": 270.939, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.746, \"dissolved_oxygen_pct\": 36.586, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.06, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.863, \"our_mmol_l_h\": 6.237, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 18.36}, {\"agitation_rpm\": 283.968, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.09, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.055, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.711, \"our_mmol_l_h\": 6.46, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.645, \"viable_cell_density_million_ml\": 20.106}, {\"agitation_rpm\": 285.248, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.983, \"dissolved_oxygen_pct\": 36.924, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.685, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.58, \"our_mmol_l_h\": 6.389, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 97.882, \"viable_cell_density_million_ml\": 21.328}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-04872d6397e275c9a0ba", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.591, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.212, \"dissolved_oxygen_pct\": 37.546, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.109, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.931, \"our_mmol_l_h\": 5.639, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.442, \"viable_cell_density_million_ml\": 16.601}, {\"agitation_rpm\": 270.939, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.746, \"dissolved_oxygen_pct\": 36.586, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.06, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.863, \"our_mmol_l_h\": 6.237, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 18.36}, {\"agitation_rpm\": 283.968, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.09, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.055, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.711, \"our_mmol_l_h\": 6.46, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.645, \"viable_cell_density_million_ml\": 20.106}, {\"agitation_rpm\": 285.248, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.983, \"dissolved_oxygen_pct\": 36.924, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.685, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.58, \"our_mmol_l_h\": 6.389, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 97.882, \"viable_cell_density_million_ml\": 21.328}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-57a1eda99fd30815d247", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.591, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.212, \"dissolved_oxygen_pct\": 37.546, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.109, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.931, \"our_mmol_l_h\": 5.639, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.442, \"viable_cell_density_million_ml\": 16.601}, {\"agitation_rpm\": 270.939, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.746, \"dissolved_oxygen_pct\": 36.586, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.06, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.863, \"our_mmol_l_h\": 6.237, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 18.36}, {\"agitation_rpm\": 283.968, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.09, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.055, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.711, \"our_mmol_l_h\": 6.46, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.645, \"viable_cell_density_million_ml\": 20.106}, {\"agitation_rpm\": 285.248, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.983, \"dissolved_oxygen_pct\": 36.924, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.685, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.58, \"our_mmol_l_h\": 6.389, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 97.882, \"viable_cell_density_million_ml\": 21.328}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-07dc8925f46c2a2db371", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.591, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.212, \"dissolved_oxygen_pct\": 37.546, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.109, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.931, \"our_mmol_l_h\": 5.639, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.442, \"viable_cell_density_million_ml\": 16.601}, {\"agitation_rpm\": 270.939, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.746, \"dissolved_oxygen_pct\": 36.586, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.06, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.863, \"our_mmol_l_h\": 6.237, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 18.36}, {\"agitation_rpm\": 283.968, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.09, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.055, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.711, \"our_mmol_l_h\": 6.46, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.645, \"viable_cell_density_million_ml\": 20.106}, {\"agitation_rpm\": 285.248, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.983, \"dissolved_oxygen_pct\": 36.924, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.685, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.58, \"our_mmol_l_h\": 6.389, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 97.882, \"viable_cell_density_million_ml\": 21.328}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3220cea77b9e86687a9c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.75, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.218, \"dissolved_oxygen_pct\": 39.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.189, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.024, \"our_mmol_l_h\": 4.628, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.499, \"viable_cell_density_million_ml\": 13.612}, {\"agitation_rpm\": 246.471, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.868, \"dissolved_oxygen_pct\": 37.74, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.133, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.947, \"our_mmol_l_h\": 5.325, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.462, \"viable_cell_density_million_ml\": 15.689}, {\"agitation_rpm\": 269.906, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.747, \"dissolved_oxygen_pct\": 28.09, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.015, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.835, \"our_mmol_l_h\": 6.253, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 98.534, \"viable_cell_density_million_ml\": 18.448}, {\"agitation_rpm\": 285.981, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 6.584, \"dissolved_oxygen_pct\": 18.976, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.912, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.681, \"our_mmol_l_h\": 7.188, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.596, \"viable_cell_density_million_ml\": 21.096}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8bf2fc70a8c793e683ca", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.75, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.218, \"dissolved_oxygen_pct\": 39.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.189, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.024, \"our_mmol_l_h\": 4.628, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.499, \"viable_cell_density_million_ml\": 13.612}, {\"agitation_rpm\": 246.471, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.868, \"dissolved_oxygen_pct\": 37.74, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.133, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.947, \"our_mmol_l_h\": 5.325, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.462, \"viable_cell_density_million_ml\": 15.689}, {\"agitation_rpm\": 269.906, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.747, \"dissolved_oxygen_pct\": 28.09, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.015, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.835, \"our_mmol_l_h\": 6.253, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 98.534, \"viable_cell_density_million_ml\": 18.448}, {\"agitation_rpm\": 285.981, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 6.584, \"dissolved_oxygen_pct\": 18.976, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.912, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.681, \"our_mmol_l_h\": 7.188, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.596, \"viable_cell_density_million_ml\": 21.096}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ce19da6cb3c2cf62723c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.75, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.218, \"dissolved_oxygen_pct\": 39.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.189, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.024, \"our_mmol_l_h\": 4.628, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.499, \"viable_cell_density_million_ml\": 13.612}, {\"agitation_rpm\": 246.471, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.868, \"dissolved_oxygen_pct\": 37.74, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.133, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.947, \"our_mmol_l_h\": 5.325, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.462, \"viable_cell_density_million_ml\": 15.689}, {\"agitation_rpm\": 269.906, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.747, \"dissolved_oxygen_pct\": 28.09, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.015, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.835, \"our_mmol_l_h\": 6.253, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 98.534, \"viable_cell_density_million_ml\": 18.448}, {\"agitation_rpm\": 285.981, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 6.584, \"dissolved_oxygen_pct\": 18.976, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.912, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.681, \"our_mmol_l_h\": 7.188, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.596, \"viable_cell_density_million_ml\": 21.096}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5c9fc6d04e1979a77c25", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.75, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.218, \"dissolved_oxygen_pct\": 39.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.189, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.024, \"our_mmol_l_h\": 4.628, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.499, \"viable_cell_density_million_ml\": 13.612}, {\"agitation_rpm\": 246.471, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.868, \"dissolved_oxygen_pct\": 37.74, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.133, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.947, \"our_mmol_l_h\": 5.325, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.462, \"viable_cell_density_million_ml\": 15.689}, {\"agitation_rpm\": 269.906, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.747, \"dissolved_oxygen_pct\": 28.09, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.015, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.835, \"our_mmol_l_h\": 6.253, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 98.534, \"viable_cell_density_million_ml\": 18.448}, {\"agitation_rpm\": 285.981, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 6.584, \"dissolved_oxygen_pct\": 18.976, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.912, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.681, \"our_mmol_l_h\": 7.188, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.596, \"viable_cell_density_million_ml\": 21.096}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-590a5283e311c90a7262", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.065, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.771, \"dissolved_oxygen_pct\": 39.938, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.58, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.273, \"our_mmol_l_h\": 4.078, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.711, \"viable_cell_density_million_ml\": 11.927}, {\"agitation_rpm\": 238.331, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.606, \"dissolved_oxygen_pct\": 37.721, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.501, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.024, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.943, \"viable_cell_density_million_ml\": 14.787}, {\"agitation_rpm\": 279.015, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.064, \"dissolved_oxygen_pct\": 36.548, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.473, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.214, \"our_mmol_l_h\": 6.072, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.806, \"viable_cell_density_million_ml\": 19.097}, {\"agitation_rpm\": 285.743, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.978, \"dissolved_oxygen_pct\": 36.643, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.13, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.123, \"our_mmol_l_h\": 7.492, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.912, \"viable_cell_density_million_ml\": 24.6}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d7b6f1c4adbf6068abe5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.065, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.771, \"dissolved_oxygen_pct\": 39.938, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.58, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.273, \"our_mmol_l_h\": 4.078, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.711, \"viable_cell_density_million_ml\": 11.927}, {\"agitation_rpm\": 238.331, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.606, \"dissolved_oxygen_pct\": 37.721, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.501, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.024, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.943, \"viable_cell_density_million_ml\": 14.787}, {\"agitation_rpm\": 279.015, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.064, \"dissolved_oxygen_pct\": 36.548, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.473, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.214, \"our_mmol_l_h\": 6.072, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.806, \"viable_cell_density_million_ml\": 19.097}, {\"agitation_rpm\": 285.743, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.978, \"dissolved_oxygen_pct\": 36.643, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.13, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.123, \"our_mmol_l_h\": 7.492, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.912, \"viable_cell_density_million_ml\": 24.6}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1b461f8adfac76ab08cd", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.065, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.771, \"dissolved_oxygen_pct\": 39.938, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.58, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.273, \"our_mmol_l_h\": 4.078, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.711, \"viable_cell_density_million_ml\": 11.927}, {\"agitation_rpm\": 238.331, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.606, \"dissolved_oxygen_pct\": 37.721, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.501, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.024, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.943, \"viable_cell_density_million_ml\": 14.787}, {\"agitation_rpm\": 279.015, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.064, \"dissolved_oxygen_pct\": 36.548, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.473, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.214, \"our_mmol_l_h\": 6.072, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.806, \"viable_cell_density_million_ml\": 19.097}, {\"agitation_rpm\": 285.743, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.978, \"dissolved_oxygen_pct\": 36.643, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.13, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.123, \"our_mmol_l_h\": 7.492, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.912, \"viable_cell_density_million_ml\": 24.6}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e7f950388c9e3443dd7f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.065, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.771, \"dissolved_oxygen_pct\": 39.938, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.58, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.273, \"our_mmol_l_h\": 4.078, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.711, \"viable_cell_density_million_ml\": 11.927}, {\"agitation_rpm\": 238.331, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.606, \"dissolved_oxygen_pct\": 37.721, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.501, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.024, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.943, \"viable_cell_density_million_ml\": 14.787}, {\"agitation_rpm\": 279.015, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.064, \"dissolved_oxygen_pct\": 36.548, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.473, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.214, \"our_mmol_l_h\": 6.072, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.806, \"viable_cell_density_million_ml\": 19.097}, {\"agitation_rpm\": 285.743, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.978, \"dissolved_oxygen_pct\": 36.643, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.13, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.123, \"our_mmol_l_h\": 7.492, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.912, \"viable_cell_density_million_ml\": 24.6}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2e73196468daa776c13a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.989, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.605, \"dissolved_oxygen_pct\": 36.597, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.341, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.195, \"our_mmol_l_h\": 7.148, \"ph\": 6.99, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.855, \"viable_cell_density_million_ml\": 21.079}, {\"agitation_rpm\": 285.431, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.515, \"dissolved_oxygen_pct\": 36.469, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.348, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.203, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 24.17}, {\"agitation_rpm\": 285.955, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 8.859, \"dissolved_oxygen_pct\": 28.478, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.384, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.112, \"our_mmol_l_h\": 9.598, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.686, \"viable_cell_density_million_ml\": 28.219}, {\"agitation_rpm\": 285.031, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 10.112, \"dissolved_oxygen_pct\": 18.172, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.551, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.02, \"our_mmol_l_h\": 11.025, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 32.346}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ded525a0d9cf517ba384", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.989, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.605, \"dissolved_oxygen_pct\": 36.597, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.341, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.195, \"our_mmol_l_h\": 7.148, \"ph\": 6.99, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.855, \"viable_cell_density_million_ml\": 21.079}, {\"agitation_rpm\": 285.431, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.515, \"dissolved_oxygen_pct\": 36.469, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.348, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.203, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 24.17}, {\"agitation_rpm\": 285.955, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 8.859, \"dissolved_oxygen_pct\": 28.478, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.384, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.112, \"our_mmol_l_h\": 9.598, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.686, \"viable_cell_density_million_ml\": 28.219}, {\"agitation_rpm\": 285.031, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 10.112, \"dissolved_oxygen_pct\": 18.172, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.551, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.02, \"our_mmol_l_h\": 11.025, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 32.346}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cd91a802bd14107b6c88", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.989, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.605, \"dissolved_oxygen_pct\": 36.597, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.341, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.195, \"our_mmol_l_h\": 7.148, \"ph\": 6.99, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.855, \"viable_cell_density_million_ml\": 21.079}, {\"agitation_rpm\": 285.431, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.515, \"dissolved_oxygen_pct\": 36.469, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.348, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.203, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 24.17}, {\"agitation_rpm\": 285.955, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 8.859, \"dissolved_oxygen_pct\": 28.478, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.384, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.112, \"our_mmol_l_h\": 9.598, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.686, \"viable_cell_density_million_ml\": 28.219}, {\"agitation_rpm\": 285.031, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 10.112, \"dissolved_oxygen_pct\": 18.172, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.551, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.02, \"our_mmol_l_h\": 11.025, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 32.346}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e0b20bebf35624b12236", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.989, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.605, \"dissolved_oxygen_pct\": 36.597, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.341, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.195, \"our_mmol_l_h\": 7.148, \"ph\": 6.99, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.855, \"viable_cell_density_million_ml\": 21.079}, {\"agitation_rpm\": 285.431, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.515, \"dissolved_oxygen_pct\": 36.469, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.348, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.203, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 24.17}, {\"agitation_rpm\": 285.955, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 8.859, \"dissolved_oxygen_pct\": 28.478, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.384, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.112, \"our_mmol_l_h\": 9.598, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.686, \"viable_cell_density_million_ml\": 28.219}, {\"agitation_rpm\": 285.031, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 10.112, \"dissolved_oxygen_pct\": 18.172, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.551, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.02, \"our_mmol_l_h\": 11.025, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 32.346}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7f73ff6c6b18b1f54aff", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.371, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 6.022, \"dissolved_oxygen_pct\": 35.813, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.987, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.806, \"our_mmol_l_h\": 6.507, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.612, \"viable_cell_density_million_ml\": 19.198}, {\"agitation_rpm\": 284.53, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.428, \"dissolved_oxygen_pct\": 36.327, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.001, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.721, \"our_mmol_l_h\": 6.968, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 20.588}, {\"agitation_rpm\": 284.337, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.91, \"dissolved_oxygen_pct\": 36.993, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.982, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.62, \"our_mmol_l_h\": 6.967, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.593, \"viable_cell_density_million_ml\": 21.632}, {\"agitation_rpm\": 283.978, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.207, \"dissolved_oxygen_pct\": 36.689, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.59, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.492, \"our_mmol_l_h\": 6.597, \"ph\": 6.982, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 96.696, \"viable_cell_density_million_ml\": 22.022}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-53826f5bbba3283261b9", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.371, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 6.022, \"dissolved_oxygen_pct\": 35.813, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.987, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.806, \"our_mmol_l_h\": 6.507, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.612, \"viable_cell_density_million_ml\": 19.198}, {\"agitation_rpm\": 284.53, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.428, \"dissolved_oxygen_pct\": 36.327, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.001, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.721, \"our_mmol_l_h\": 6.968, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 20.588}, {\"agitation_rpm\": 284.337, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.91, \"dissolved_oxygen_pct\": 36.993, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.982, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.62, \"our_mmol_l_h\": 6.967, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.593, \"viable_cell_density_million_ml\": 21.632}, {\"agitation_rpm\": 283.978, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.207, \"dissolved_oxygen_pct\": 36.689, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.59, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.492, \"our_mmol_l_h\": 6.597, \"ph\": 6.982, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 96.696, \"viable_cell_density_million_ml\": 22.022}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-88ec160c4360dbe0735e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.371, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 6.022, \"dissolved_oxygen_pct\": 35.813, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.987, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.806, \"our_mmol_l_h\": 6.507, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.612, \"viable_cell_density_million_ml\": 19.198}, {\"agitation_rpm\": 284.53, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.428, \"dissolved_oxygen_pct\": 36.327, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.001, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.721, \"our_mmol_l_h\": 6.968, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 20.588}, {\"agitation_rpm\": 284.337, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.91, \"dissolved_oxygen_pct\": 36.993, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.982, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.62, \"our_mmol_l_h\": 6.967, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.593, \"viable_cell_density_million_ml\": 21.632}, {\"agitation_rpm\": 283.978, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.207, \"dissolved_oxygen_pct\": 36.689, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.59, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.492, \"our_mmol_l_h\": 6.597, \"ph\": 6.982, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 96.696, \"viable_cell_density_million_ml\": 22.022}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f86b1ab5abb0ea5449ff", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.371, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 6.022, \"dissolved_oxygen_pct\": 35.813, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.987, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.806, \"our_mmol_l_h\": 6.507, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.612, \"viable_cell_density_million_ml\": 19.198}, {\"agitation_rpm\": 284.53, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.428, \"dissolved_oxygen_pct\": 36.327, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.001, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.721, \"our_mmol_l_h\": 6.968, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 20.588}, {\"agitation_rpm\": 284.337, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.91, \"dissolved_oxygen_pct\": 36.993, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.982, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.62, \"our_mmol_l_h\": 6.967, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.593, \"viable_cell_density_million_ml\": 21.632}, {\"agitation_rpm\": 283.978, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.207, \"dissolved_oxygen_pct\": 36.689, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.59, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.492, \"our_mmol_l_h\": 6.597, \"ph\": 6.982, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 96.696, \"viable_cell_density_million_ml\": 22.022}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a54b1551525b081a0bb5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.897, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.023, \"dissolved_oxygen_pct\": 36.672, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.993, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.825, \"our_mmol_l_h\": 6.544, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.539, \"viable_cell_density_million_ml\": 19.137}, {\"agitation_rpm\": 285.802, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.961, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.99, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 7.003, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.494, \"viable_cell_density_million_ml\": 20.562}, {\"agitation_rpm\": 283.94, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 28.775, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.858, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.636, \"our_mmol_l_h\": 7.482, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.595, \"viable_cell_density_million_ml\": 21.946}, {\"agitation_rpm\": 285.929, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.163, \"dissolved_oxygen_pct\": 18.232, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.785, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.486, \"our_mmol_l_h\": 7.806, \"ph\": 6.987, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 96.467, \"viable_cell_density_million_ml\": 22.943}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8e72f41c93abf942c8a4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.897, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.023, \"dissolved_oxygen_pct\": 36.672, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.993, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.825, \"our_mmol_l_h\": 6.544, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.539, \"viable_cell_density_million_ml\": 19.137}, {\"agitation_rpm\": 285.802, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.961, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.99, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 7.003, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.494, \"viable_cell_density_million_ml\": 20.562}, {\"agitation_rpm\": 283.94, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 28.775, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.858, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.636, \"our_mmol_l_h\": 7.482, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.595, \"viable_cell_density_million_ml\": 21.946}, {\"agitation_rpm\": 285.929, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.163, \"dissolved_oxygen_pct\": 18.232, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.785, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.486, \"our_mmol_l_h\": 7.806, \"ph\": 6.987, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 96.467, \"viable_cell_density_million_ml\": 22.943}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a3084b2390faf5a42aa4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.897, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.023, \"dissolved_oxygen_pct\": 36.672, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.993, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.825, \"our_mmol_l_h\": 6.544, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.539, \"viable_cell_density_million_ml\": 19.137}, {\"agitation_rpm\": 285.802, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.961, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.99, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 7.003, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.494, \"viable_cell_density_million_ml\": 20.562}, {\"agitation_rpm\": 283.94, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 28.775, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.858, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.636, \"our_mmol_l_h\": 7.482, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.595, \"viable_cell_density_million_ml\": 21.946}, {\"agitation_rpm\": 285.929, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.163, \"dissolved_oxygen_pct\": 18.232, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.785, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.486, \"our_mmol_l_h\": 7.806, \"ph\": 6.987, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 96.467, \"viable_cell_density_million_ml\": 22.943}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-498ebbd3a31c7260e9cc", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.897, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.023, \"dissolved_oxygen_pct\": 36.672, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.993, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.825, \"our_mmol_l_h\": 6.544, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.539, \"viable_cell_density_million_ml\": 19.137}, {\"agitation_rpm\": 285.802, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.961, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.99, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 7.003, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.494, \"viable_cell_density_million_ml\": 20.562}, {\"agitation_rpm\": 283.94, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 28.775, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.858, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.636, \"our_mmol_l_h\": 7.482, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.595, \"viable_cell_density_million_ml\": 21.946}, {\"agitation_rpm\": 285.929, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.163, \"dissolved_oxygen_pct\": 18.232, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.785, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.486, \"our_mmol_l_h\": 7.806, \"ph\": 6.987, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 96.467, \"viable_cell_density_million_ml\": 22.943}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-52c1cabe942c001db3c4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.344, \"airflow_vvm\": 0.259, \"cer_mmol_l_h\": 5.113, \"dissolved_oxygen_pct\": 37.227, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.465, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.518, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.712, \"viable_cell_density_million_ml\": 16.261}, {\"agitation_rpm\": 280.498, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.101, \"dissolved_oxygen_pct\": 36.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.353, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.205, \"our_mmol_l_h\": 6.602, \"ph\": 7.002, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.826, \"viable_cell_density_million_ml\": 19.502}, {\"agitation_rpm\": 284.893, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.566, \"dissolved_oxygen_pct\": 36.348, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.395, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 7.681, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.818, \"viable_cell_density_million_ml\": 23.619}, {\"agitation_rpm\": 284.326, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.176, \"dissolved_oxygen_pct\": 36.831, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.214, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.093, \"our_mmol_l_h\": 8.828, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.905, \"viable_cell_density_million_ml\": 28.488}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-25f08c818d97b6ce47de", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.344, \"airflow_vvm\": 0.259, \"cer_mmol_l_h\": 5.113, \"dissolved_oxygen_pct\": 37.227, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.465, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.518, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.712, \"viable_cell_density_million_ml\": 16.261}, {\"agitation_rpm\": 280.498, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.101, \"dissolved_oxygen_pct\": 36.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.353, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.205, \"our_mmol_l_h\": 6.602, \"ph\": 7.002, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.826, \"viable_cell_density_million_ml\": 19.502}, {\"agitation_rpm\": 284.893, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.566, \"dissolved_oxygen_pct\": 36.348, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.395, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 7.681, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.818, \"viable_cell_density_million_ml\": 23.619}, {\"agitation_rpm\": 284.326, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.176, \"dissolved_oxygen_pct\": 36.831, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.214, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.093, \"our_mmol_l_h\": 8.828, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.905, \"viable_cell_density_million_ml\": 28.488}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d7ac73b2a5cd2e3a468d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.344, \"airflow_vvm\": 0.259, \"cer_mmol_l_h\": 5.113, \"dissolved_oxygen_pct\": 37.227, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.465, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.518, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.712, \"viable_cell_density_million_ml\": 16.261}, {\"agitation_rpm\": 280.498, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.101, \"dissolved_oxygen_pct\": 36.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.353, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.205, \"our_mmol_l_h\": 6.602, \"ph\": 7.002, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.826, \"viable_cell_density_million_ml\": 19.502}, {\"agitation_rpm\": 284.893, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.566, \"dissolved_oxygen_pct\": 36.348, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.395, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 7.681, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.818, \"viable_cell_density_million_ml\": 23.619}, {\"agitation_rpm\": 284.326, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.176, \"dissolved_oxygen_pct\": 36.831, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.214, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.093, \"our_mmol_l_h\": 8.828, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.905, \"viable_cell_density_million_ml\": 28.488}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bca50439de33f9724b6b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.344, \"airflow_vvm\": 0.259, \"cer_mmol_l_h\": 5.113, \"dissolved_oxygen_pct\": 37.227, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.465, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.518, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.712, \"viable_cell_density_million_ml\": 16.261}, {\"agitation_rpm\": 280.498, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.101, \"dissolved_oxygen_pct\": 36.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.353, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.205, \"our_mmol_l_h\": 6.602, \"ph\": 7.002, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.826, \"viable_cell_density_million_ml\": 19.502}, {\"agitation_rpm\": 284.893, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.566, \"dissolved_oxygen_pct\": 36.348, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.395, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 7.681, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.818, \"viable_cell_density_million_ml\": 23.619}, {\"agitation_rpm\": 284.326, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.176, \"dissolved_oxygen_pct\": 36.831, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.214, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.093, \"our_mmol_l_h\": 8.828, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.905, \"viable_cell_density_million_ml\": 28.488}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-31d006dffac561617939", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 286.191, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.988, \"dissolved_oxygen_pct\": 36.105, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.351, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.151, \"our_mmol_l_h\": 8.695, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 25.627}, {\"agitation_rpm\": 285.633, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.381, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.098, \"our_mmol_l_h\": 9.575, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.698, \"viable_cell_density_million_ml\": 28.29}, {\"agitation_rpm\": 284.302, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.858, \"dissolved_oxygen_pct\": 28.935, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.527, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.036, \"our_mmol_l_h\": 10.735, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.923, \"viable_cell_density_million_ml\": 31.47}, {\"agitation_rpm\": 283.892, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.806, \"dissolved_oxygen_pct\": 18.853, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.72, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.009, \"our_mmol_l_h\": 11.761, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.919, \"viable_cell_density_million_ml\": 34.584}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-06b964a7775d48d258e2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 286.191, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.988, \"dissolved_oxygen_pct\": 36.105, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.351, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.151, \"our_mmol_l_h\": 8.695, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 25.627}, {\"agitation_rpm\": 285.633, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.381, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.098, \"our_mmol_l_h\": 9.575, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.698, \"viable_cell_density_million_ml\": 28.29}, {\"agitation_rpm\": 284.302, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.858, \"dissolved_oxygen_pct\": 28.935, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.527, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.036, \"our_mmol_l_h\": 10.735, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.923, \"viable_cell_density_million_ml\": 31.47}, {\"agitation_rpm\": 283.892, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.806, \"dissolved_oxygen_pct\": 18.853, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.72, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.009, \"our_mmol_l_h\": 11.761, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.919, \"viable_cell_density_million_ml\": 34.584}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d37d40bf6fe214d17210", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 286.191, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.988, \"dissolved_oxygen_pct\": 36.105, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.351, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.151, \"our_mmol_l_h\": 8.695, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 25.627}, {\"agitation_rpm\": 285.633, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.381, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.098, \"our_mmol_l_h\": 9.575, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.698, \"viable_cell_density_million_ml\": 28.29}, {\"agitation_rpm\": 284.302, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.858, \"dissolved_oxygen_pct\": 28.935, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.527, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.036, \"our_mmol_l_h\": 10.735, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.923, \"viable_cell_density_million_ml\": 31.47}, {\"agitation_rpm\": 283.892, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.806, \"dissolved_oxygen_pct\": 18.853, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.72, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.009, \"our_mmol_l_h\": 11.761, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.919, \"viable_cell_density_million_ml\": 34.584}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-02c9a21b9e0783e45036", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 286.191, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.988, \"dissolved_oxygen_pct\": 36.105, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.351, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.151, \"our_mmol_l_h\": 8.695, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 25.627}, {\"agitation_rpm\": 285.633, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.381, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.098, \"our_mmol_l_h\": 9.575, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.698, \"viable_cell_density_million_ml\": 28.29}, {\"agitation_rpm\": 284.302, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.858, \"dissolved_oxygen_pct\": 28.935, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.527, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.036, \"our_mmol_l_h\": 10.735, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.923, \"viable_cell_density_million_ml\": 31.47}, {\"agitation_rpm\": 283.892, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.806, \"dissolved_oxygen_pct\": 18.853, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.72, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.009, \"our_mmol_l_h\": 11.761, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.919, \"viable_cell_density_million_ml\": 34.584}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1f7f5d4d1e4a7519c07f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.251, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 36.395, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.006, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.797, \"our_mmol_l_h\": 6.511, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.425, \"viable_cell_density_million_ml\": 19.249}, {\"agitation_rpm\": 285.717, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.411, \"dissolved_oxygen_pct\": 36.028, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.933, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.711, \"our_mmol_l_h\": 6.98, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.588, \"viable_cell_density_million_ml\": 20.615}, {\"agitation_rpm\": 284.389, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.883, \"dissolved_oxygen_pct\": 35.897, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.964, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.637, \"our_mmol_l_h\": 6.996, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.409, \"viable_cell_density_million_ml\": 21.532}, {\"agitation_rpm\": 285.939, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.168, \"dissolved_oxygen_pct\": 36.993, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.605, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.475, \"our_mmol_l_h\": 6.646, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 96.715, \"viable_cell_density_million_ml\": 21.988}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3b2c8907a8b913872545", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.251, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 36.395, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.006, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.797, \"our_mmol_l_h\": 6.511, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.425, \"viable_cell_density_million_ml\": 19.249}, {\"agitation_rpm\": 285.717, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.411, \"dissolved_oxygen_pct\": 36.028, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.933, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.711, \"our_mmol_l_h\": 6.98, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.588, \"viable_cell_density_million_ml\": 20.615}, {\"agitation_rpm\": 284.389, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.883, \"dissolved_oxygen_pct\": 35.897, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.964, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.637, \"our_mmol_l_h\": 6.996, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.409, \"viable_cell_density_million_ml\": 21.532}, {\"agitation_rpm\": 285.939, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.168, \"dissolved_oxygen_pct\": 36.993, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.605, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.475, \"our_mmol_l_h\": 6.646, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 96.715, \"viable_cell_density_million_ml\": 21.988}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0f493caeaae03d610e55", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.251, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 36.395, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.006, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.797, \"our_mmol_l_h\": 6.511, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.425, \"viable_cell_density_million_ml\": 19.249}, {\"agitation_rpm\": 285.717, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.411, \"dissolved_oxygen_pct\": 36.028, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.933, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.711, \"our_mmol_l_h\": 6.98, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.588, \"viable_cell_density_million_ml\": 20.615}, {\"agitation_rpm\": 284.389, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.883, \"dissolved_oxygen_pct\": 35.897, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.964, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.637, \"our_mmol_l_h\": 6.996, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.409, \"viable_cell_density_million_ml\": 21.532}, {\"agitation_rpm\": 285.939, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.168, \"dissolved_oxygen_pct\": 36.993, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.605, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.475, \"our_mmol_l_h\": 6.646, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 96.715, \"viable_cell_density_million_ml\": 21.988}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ce4169fa215c076361e1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.251, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 36.395, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.006, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.797, \"our_mmol_l_h\": 6.511, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.425, \"viable_cell_density_million_ml\": 19.249}, {\"agitation_rpm\": 285.717, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.411, \"dissolved_oxygen_pct\": 36.028, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.933, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.711, \"our_mmol_l_h\": 6.98, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.588, \"viable_cell_density_million_ml\": 20.615}, {\"agitation_rpm\": 284.389, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.883, \"dissolved_oxygen_pct\": 35.897, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.964, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.637, \"our_mmol_l_h\": 6.996, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.409, \"viable_cell_density_million_ml\": 21.532}, {\"agitation_rpm\": 285.939, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.168, \"dissolved_oxygen_pct\": 36.993, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.605, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.475, \"our_mmol_l_h\": 6.646, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 96.715, \"viable_cell_density_million_ml\": 21.988}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e154d2c1813cd243899f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.641, \"airflow_vvm\": 0.229, \"cer_mmol_l_h\": 4.256, \"dissolved_oxygen_pct\": 39.387, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.124, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.026, \"our_mmol_l_h\": 4.592, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 13.573}, {\"agitation_rpm\": 246.972, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.913, \"dissolved_oxygen_pct\": 38.302, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.14, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.95, \"our_mmol_l_h\": 5.336, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.456, \"viable_cell_density_million_ml\": 15.604}, {\"agitation_rpm\": 269.616, \"airflow_vvm\": 0.23, \"cer_mmol_l_h\": 5.784, \"dissolved_oxygen_pct\": 28.991, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.996, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.843, \"our_mmol_l_h\": 6.269, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 18.368}, {\"agitation_rpm\": 284.577, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 6.572, \"dissolved_oxygen_pct\": 18.919, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.956, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.685, \"our_mmol_l_h\": 7.183, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 21.111}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dec2ce8a3248a5d587f2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.641, \"airflow_vvm\": 0.229, \"cer_mmol_l_h\": 4.256, \"dissolved_oxygen_pct\": 39.387, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.124, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.026, \"our_mmol_l_h\": 4.592, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 13.573}, {\"agitation_rpm\": 246.972, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.913, \"dissolved_oxygen_pct\": 38.302, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.14, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.95, \"our_mmol_l_h\": 5.336, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.456, \"viable_cell_density_million_ml\": 15.604}, {\"agitation_rpm\": 269.616, \"airflow_vvm\": 0.23, \"cer_mmol_l_h\": 5.784, \"dissolved_oxygen_pct\": 28.991, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.996, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.843, \"our_mmol_l_h\": 6.269, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 18.368}, {\"agitation_rpm\": 284.577, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 6.572, \"dissolved_oxygen_pct\": 18.919, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.956, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.685, \"our_mmol_l_h\": 7.183, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 21.111}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cb5e43f1d127ac7a3d17", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.641, \"airflow_vvm\": 0.229, \"cer_mmol_l_h\": 4.256, \"dissolved_oxygen_pct\": 39.387, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.124, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.026, \"our_mmol_l_h\": 4.592, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 13.573}, {\"agitation_rpm\": 246.972, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.913, \"dissolved_oxygen_pct\": 38.302, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.14, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.95, \"our_mmol_l_h\": 5.336, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.456, \"viable_cell_density_million_ml\": 15.604}, {\"agitation_rpm\": 269.616, \"airflow_vvm\": 0.23, \"cer_mmol_l_h\": 5.784, \"dissolved_oxygen_pct\": 28.991, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.996, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.843, \"our_mmol_l_h\": 6.269, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 18.368}, {\"agitation_rpm\": 284.577, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 6.572, \"dissolved_oxygen_pct\": 18.919, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.956, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.685, \"our_mmol_l_h\": 7.183, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 21.111}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-812a9169dee4fe68a1c6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.641, \"airflow_vvm\": 0.229, \"cer_mmol_l_h\": 4.256, \"dissolved_oxygen_pct\": 39.387, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.124, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.026, \"our_mmol_l_h\": 4.592, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 13.573}, {\"agitation_rpm\": 246.972, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.913, \"dissolved_oxygen_pct\": 38.302, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.14, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.95, \"our_mmol_l_h\": 5.336, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.456, \"viable_cell_density_million_ml\": 15.604}, {\"agitation_rpm\": 269.616, \"airflow_vvm\": 0.23, \"cer_mmol_l_h\": 5.784, \"dissolved_oxygen_pct\": 28.991, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.996, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.843, \"our_mmol_l_h\": 6.269, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 18.368}, {\"agitation_rpm\": 284.577, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 6.572, \"dissolved_oxygen_pct\": 18.919, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.956, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.685, \"our_mmol_l_h\": 7.183, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 21.111}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6cbc916ea6f48019b054", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.721, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.998, \"dissolved_oxygen_pct\": 36.138, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.339, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.153, \"our_mmol_l_h\": 8.663, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.725, \"viable_cell_density_million_ml\": 25.615}, {\"agitation_rpm\": 284.05, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.589, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.444, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.099, \"our_mmol_l_h\": 9.574, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.917, \"viable_cell_density_million_ml\": 28.169}, {\"agitation_rpm\": 285.62, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.868, \"dissolved_oxygen_pct\": 35.672, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.599, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.044, \"our_mmol_l_h\": 10.198, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 31.124}, {\"agitation_rpm\": 284.364, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 10.794, \"dissolved_oxygen_pct\": 36.157, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.53, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.017, \"our_mmol_l_h\": 10.523, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 33.57}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4ceef2c9ca3673451c5e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.721, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.998, \"dissolved_oxygen_pct\": 36.138, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.339, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.153, \"our_mmol_l_h\": 8.663, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.725, \"viable_cell_density_million_ml\": 25.615}, {\"agitation_rpm\": 284.05, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.589, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.444, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.099, \"our_mmol_l_h\": 9.574, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.917, \"viable_cell_density_million_ml\": 28.169}, {\"agitation_rpm\": 285.62, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.868, \"dissolved_oxygen_pct\": 35.672, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.599, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.044, \"our_mmol_l_h\": 10.198, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 31.124}, {\"agitation_rpm\": 284.364, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 10.794, \"dissolved_oxygen_pct\": 36.157, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.53, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.017, \"our_mmol_l_h\": 10.523, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 33.57}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ee81b536d0b70bd35878", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.721, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.998, \"dissolved_oxygen_pct\": 36.138, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.339, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.153, \"our_mmol_l_h\": 8.663, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.725, \"viable_cell_density_million_ml\": 25.615}, {\"agitation_rpm\": 284.05, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.589, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.444, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.099, \"our_mmol_l_h\": 9.574, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.917, \"viable_cell_density_million_ml\": 28.169}, {\"agitation_rpm\": 285.62, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.868, \"dissolved_oxygen_pct\": 35.672, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.599, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.044, \"our_mmol_l_h\": 10.198, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 31.124}, {\"agitation_rpm\": 284.364, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 10.794, \"dissolved_oxygen_pct\": 36.157, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.53, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.017, \"our_mmol_l_h\": 10.523, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 33.57}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-eb32ed507d5a1d6aa3c1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.721, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.998, \"dissolved_oxygen_pct\": 36.138, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.339, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.153, \"our_mmol_l_h\": 8.663, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.725, \"viable_cell_density_million_ml\": 25.615}, {\"agitation_rpm\": 284.05, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.589, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.444, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.099, \"our_mmol_l_h\": 9.574, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.917, \"viable_cell_density_million_ml\": 28.169}, {\"agitation_rpm\": 285.62, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.868, \"dissolved_oxygen_pct\": 35.672, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.599, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.044, \"our_mmol_l_h\": 10.198, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 31.124}, {\"agitation_rpm\": 284.364, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 10.794, \"dissolved_oxygen_pct\": 36.157, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.53, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.017, \"our_mmol_l_h\": 10.523, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 33.57}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-07db663ad0596720444a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 250.623, \"airflow_vvm\": 0.257, \"cer_mmol_l_h\": 5.127, \"dissolved_oxygen_pct\": 36.955, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.487, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.547, \"ph\": 6.99, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.77, \"viable_cell_density_million_ml\": 16.294}, {\"agitation_rpm\": 279.48, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.055, \"dissolved_oxygen_pct\": 35.728, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.39, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.181, \"our_mmol_l_h\": 6.616, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.823, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 19.516}, {\"agitation_rpm\": 283.919, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 28.321, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.352, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.171, \"our_mmol_l_h\": 8.223, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.761, \"viable_cell_density_million_ml\": 24.167}, {\"agitation_rpm\": 285.196, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 9.195, \"dissolved_oxygen_pct\": 18.139, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.407, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.107, \"our_mmol_l_h\": 10.007, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 29.371}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-70f57de9fc89545b0dc1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 250.623, \"airflow_vvm\": 0.257, \"cer_mmol_l_h\": 5.127, \"dissolved_oxygen_pct\": 36.955, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.487, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.547, \"ph\": 6.99, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.77, \"viable_cell_density_million_ml\": 16.294}, {\"agitation_rpm\": 279.48, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.055, \"dissolved_oxygen_pct\": 35.728, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.39, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.181, \"our_mmol_l_h\": 6.616, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.823, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 19.516}, {\"agitation_rpm\": 283.919, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 28.321, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.352, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.171, \"our_mmol_l_h\": 8.223, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.761, \"viable_cell_density_million_ml\": 24.167}, {\"agitation_rpm\": 285.196, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 9.195, \"dissolved_oxygen_pct\": 18.139, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.407, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.107, \"our_mmol_l_h\": 10.007, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 29.371}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-98e42cb61e38a898da0b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 250.623, \"airflow_vvm\": 0.257, \"cer_mmol_l_h\": 5.127, \"dissolved_oxygen_pct\": 36.955, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.487, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.547, \"ph\": 6.99, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.77, \"viable_cell_density_million_ml\": 16.294}, {\"agitation_rpm\": 279.48, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.055, \"dissolved_oxygen_pct\": 35.728, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.39, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.181, \"our_mmol_l_h\": 6.616, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.823, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 19.516}, {\"agitation_rpm\": 283.919, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 28.321, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.352, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.171, \"our_mmol_l_h\": 8.223, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.761, \"viable_cell_density_million_ml\": 24.167}, {\"agitation_rpm\": 285.196, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 9.195, \"dissolved_oxygen_pct\": 18.139, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.407, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.107, \"our_mmol_l_h\": 10.007, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 29.371}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-07b34e5ca6197657fa95", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 250.623, \"airflow_vvm\": 0.257, \"cer_mmol_l_h\": 5.127, \"dissolved_oxygen_pct\": 36.955, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.487, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.547, \"ph\": 6.99, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.77, \"viable_cell_density_million_ml\": 16.294}, {\"agitation_rpm\": 279.48, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.055, \"dissolved_oxygen_pct\": 35.728, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.39, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.181, \"our_mmol_l_h\": 6.616, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.823, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 19.516}, {\"agitation_rpm\": 283.919, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 28.321, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.352, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.171, \"our_mmol_l_h\": 8.223, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.761, \"viable_cell_density_million_ml\": 24.167}, {\"agitation_rpm\": 285.196, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 9.195, \"dissolved_oxygen_pct\": 18.139, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.407, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.107, \"our_mmol_l_h\": 10.007, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 29.371}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5446aae4017e87ae2582", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.65, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.017, \"dissolved_oxygen_pct\": 36.154, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.982, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.531, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.613, \"viable_cell_density_million_ml\": 19.232}, {\"agitation_rpm\": 286.053, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 36.432, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.93, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.734, \"our_mmol_l_h\": 6.957, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.536, \"viable_cell_density_million_ml\": 20.604}, {\"agitation_rpm\": 284.069, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.895, \"dissolved_oxygen_pct\": 36.679, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.951, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.623, \"our_mmol_l_h\": 6.958, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.645, \"viable_cell_density_million_ml\": 21.662}, {\"agitation_rpm\": 285.521, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.217, \"dissolved_oxygen_pct\": 36.272, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.577, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.504, \"our_mmol_l_h\": 6.595, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 96.706, \"viable_cell_density_million_ml\": 22.007}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b0b9954400df962222fb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.65, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.017, \"dissolved_oxygen_pct\": 36.154, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.982, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.531, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.613, \"viable_cell_density_million_ml\": 19.232}, {\"agitation_rpm\": 286.053, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 36.432, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.93, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.734, \"our_mmol_l_h\": 6.957, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.536, \"viable_cell_density_million_ml\": 20.604}, {\"agitation_rpm\": 284.069, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.895, \"dissolved_oxygen_pct\": 36.679, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.951, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.623, \"our_mmol_l_h\": 6.958, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.645, \"viable_cell_density_million_ml\": 21.662}, {\"agitation_rpm\": 285.521, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.217, \"dissolved_oxygen_pct\": 36.272, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.577, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.504, \"our_mmol_l_h\": 6.595, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 96.706, \"viable_cell_density_million_ml\": 22.007}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a6525b542620efae795b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.65, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.017, \"dissolved_oxygen_pct\": 36.154, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.982, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.531, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.613, \"viable_cell_density_million_ml\": 19.232}, {\"agitation_rpm\": 286.053, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 36.432, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.93, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.734, \"our_mmol_l_h\": 6.957, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.536, \"viable_cell_density_million_ml\": 20.604}, {\"agitation_rpm\": 284.069, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.895, \"dissolved_oxygen_pct\": 36.679, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.951, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.623, \"our_mmol_l_h\": 6.958, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.645, \"viable_cell_density_million_ml\": 21.662}, {\"agitation_rpm\": 285.521, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.217, \"dissolved_oxygen_pct\": 36.272, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.577, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.504, \"our_mmol_l_h\": 6.595, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 96.706, \"viable_cell_density_million_ml\": 22.007}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b5717a516c4d6b293ba2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.65, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.017, \"dissolved_oxygen_pct\": 36.154, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.982, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.531, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.613, \"viable_cell_density_million_ml\": 19.232}, {\"agitation_rpm\": 286.053, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 36.432, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.93, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.734, \"our_mmol_l_h\": 6.957, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.536, \"viable_cell_density_million_ml\": 20.604}, {\"agitation_rpm\": 284.069, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.895, \"dissolved_oxygen_pct\": 36.679, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.951, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.623, \"our_mmol_l_h\": 6.958, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.645, \"viable_cell_density_million_ml\": 21.662}, {\"agitation_rpm\": 285.521, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.217, \"dissolved_oxygen_pct\": 36.272, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.577, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.504, \"our_mmol_l_h\": 6.595, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 96.706, \"viable_cell_density_million_ml\": 22.007}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b93f5c82207ab011c95a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.391, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.0, \"dissolved_oxygen_pct\": 36.383, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.039, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.817, \"our_mmol_l_h\": 6.535, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 19.266}, {\"agitation_rpm\": 284.204, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.437, \"dissolved_oxygen_pct\": 36.144, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.962, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.758, \"our_mmol_l_h\": 6.984, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.535, \"viable_cell_density_million_ml\": 20.589}, {\"agitation_rpm\": 284.957, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.891, \"dissolved_oxygen_pct\": 28.86, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.885, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.619, \"our_mmol_l_h\": 7.511, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.585, \"viable_cell_density_million_ml\": 22.036}, {\"agitation_rpm\": 283.93, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 7.179, \"dissolved_oxygen_pct\": 18.393, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.757, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.808, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 96.582, \"viable_cell_density_million_ml\": 22.961}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e6107a7bcb519b626a98", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.391, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.0, \"dissolved_oxygen_pct\": 36.383, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.039, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.817, \"our_mmol_l_h\": 6.535, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 19.266}, {\"agitation_rpm\": 284.204, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.437, \"dissolved_oxygen_pct\": 36.144, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.962, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.758, \"our_mmol_l_h\": 6.984, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.535, \"viable_cell_density_million_ml\": 20.589}, {\"agitation_rpm\": 284.957, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.891, \"dissolved_oxygen_pct\": 28.86, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.885, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.619, \"our_mmol_l_h\": 7.511, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.585, \"viable_cell_density_million_ml\": 22.036}, {\"agitation_rpm\": 283.93, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 7.179, \"dissolved_oxygen_pct\": 18.393, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.757, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.808, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 96.582, \"viable_cell_density_million_ml\": 22.961}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4920e329d908a863f072", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.391, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.0, \"dissolved_oxygen_pct\": 36.383, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.039, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.817, \"our_mmol_l_h\": 6.535, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 19.266}, {\"agitation_rpm\": 284.204, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.437, \"dissolved_oxygen_pct\": 36.144, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.962, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.758, \"our_mmol_l_h\": 6.984, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.535, \"viable_cell_density_million_ml\": 20.589}, {\"agitation_rpm\": 284.957, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.891, \"dissolved_oxygen_pct\": 28.86, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.885, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.619, \"our_mmol_l_h\": 7.511, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.585, \"viable_cell_density_million_ml\": 22.036}, {\"agitation_rpm\": 283.93, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 7.179, \"dissolved_oxygen_pct\": 18.393, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.757, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.808, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 96.582, \"viable_cell_density_million_ml\": 22.961}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-97a7c9b2568dc031e00a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.391, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.0, \"dissolved_oxygen_pct\": 36.383, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.039, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.817, \"our_mmol_l_h\": 6.535, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 19.266}, {\"agitation_rpm\": 284.204, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.437, \"dissolved_oxygen_pct\": 36.144, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.962, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.758, \"our_mmol_l_h\": 6.984, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.535, \"viable_cell_density_million_ml\": 20.589}, {\"agitation_rpm\": 284.957, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.891, \"dissolved_oxygen_pct\": 28.86, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.885, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.619, \"our_mmol_l_h\": 7.511, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.585, \"viable_cell_density_million_ml\": 22.036}, {\"agitation_rpm\": 283.93, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 7.179, \"dissolved_oxygen_pct\": 18.393, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.757, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.808, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 96.582, \"viable_cell_density_million_ml\": 22.961}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-887c3d2a69f57acb300f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.812, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.018, \"dissolved_oxygen_pct\": 36.842, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.386, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.114, \"our_mmol_l_h\": 8.678, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.898, \"viable_cell_density_million_ml\": 25.577}, {\"agitation_rpm\": 284.716, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.822, \"dissolved_oxygen_pct\": 36.047, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.424, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.094, \"our_mmol_l_h\": 9.618, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.946, \"viable_cell_density_million_ml\": 28.221}, {\"agitation_rpm\": 284.38, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 9.83, \"dissolved_oxygen_pct\": 35.611, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.611, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.055, \"our_mmol_l_h\": 10.177, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.729, \"viable_cell_density_million_ml\": 31.075}, {\"agitation_rpm\": 284.619, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 10.817, \"dissolved_oxygen_pct\": 36.52, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.51, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 10.569, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.89, \"viable_cell_density_million_ml\": 33.56}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8c60fef888355c2c88ec", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.812, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.018, \"dissolved_oxygen_pct\": 36.842, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.386, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.114, \"our_mmol_l_h\": 8.678, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.898, \"viable_cell_density_million_ml\": 25.577}, {\"agitation_rpm\": 284.716, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.822, \"dissolved_oxygen_pct\": 36.047, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.424, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.094, \"our_mmol_l_h\": 9.618, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.946, \"viable_cell_density_million_ml\": 28.221}, {\"agitation_rpm\": 284.38, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 9.83, \"dissolved_oxygen_pct\": 35.611, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.611, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.055, \"our_mmol_l_h\": 10.177, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.729, \"viable_cell_density_million_ml\": 31.075}, {\"agitation_rpm\": 284.619, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 10.817, \"dissolved_oxygen_pct\": 36.52, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.51, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 10.569, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.89, \"viable_cell_density_million_ml\": 33.56}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-444b2a3114e6e1a2d58f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.812, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.018, \"dissolved_oxygen_pct\": 36.842, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.386, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.114, \"our_mmol_l_h\": 8.678, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.898, \"viable_cell_density_million_ml\": 25.577}, {\"agitation_rpm\": 284.716, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.822, \"dissolved_oxygen_pct\": 36.047, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.424, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.094, \"our_mmol_l_h\": 9.618, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.946, \"viable_cell_density_million_ml\": 28.221}, {\"agitation_rpm\": 284.38, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 9.83, \"dissolved_oxygen_pct\": 35.611, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.611, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.055, \"our_mmol_l_h\": 10.177, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.729, \"viable_cell_density_million_ml\": 31.075}, {\"agitation_rpm\": 284.619, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 10.817, \"dissolved_oxygen_pct\": 36.52, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.51, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 10.569, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.89, \"viable_cell_density_million_ml\": 33.56}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b50e98319bfbe7565c9a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.812, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.018, \"dissolved_oxygen_pct\": 36.842, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.386, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.114, \"our_mmol_l_h\": 8.678, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.898, \"viable_cell_density_million_ml\": 25.577}, {\"agitation_rpm\": 284.716, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.822, \"dissolved_oxygen_pct\": 36.047, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.424, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.094, \"our_mmol_l_h\": 9.618, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.946, \"viable_cell_density_million_ml\": 28.221}, {\"agitation_rpm\": 284.38, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 9.83, \"dissolved_oxygen_pct\": 35.611, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.611, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.055, \"our_mmol_l_h\": 10.177, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.729, \"viable_cell_density_million_ml\": 31.075}, {\"agitation_rpm\": 284.619, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 10.817, \"dissolved_oxygen_pct\": 36.52, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.51, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 10.569, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.89, \"viable_cell_density_million_ml\": 33.56}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4fafdc8af1614d4c8ba4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 283.826, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.548, \"dissolved_oxygen_pct\": 36.692, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.371, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.186, \"our_mmol_l_h\": 7.176, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.932, \"viable_cell_density_million_ml\": 21.011}, {\"agitation_rpm\": 284.869, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.513, \"dissolved_oxygen_pct\": 36.164, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.339, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.142, \"our_mmol_l_h\": 8.199, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 24.029}, {\"agitation_rpm\": 285.771, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 8.847, \"dissolved_oxygen_pct\": 28.602, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.099, \"our_mmol_l_h\": 9.595, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.75, \"viable_cell_density_million_ml\": 28.305}, {\"agitation_rpm\": 285.155, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 10.131, \"dissolved_oxygen_pct\": 17.741, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.547, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.023, \"our_mmol_l_h\": 11.025, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.771, \"viable_cell_density_million_ml\": 32.38}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7c3fe57f109d91f42144", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 283.826, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.548, \"dissolved_oxygen_pct\": 36.692, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.371, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.186, \"our_mmol_l_h\": 7.176, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.932, \"viable_cell_density_million_ml\": 21.011}, {\"agitation_rpm\": 284.869, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.513, \"dissolved_oxygen_pct\": 36.164, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.339, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.142, \"our_mmol_l_h\": 8.199, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 24.029}, {\"agitation_rpm\": 285.771, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 8.847, \"dissolved_oxygen_pct\": 28.602, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.099, \"our_mmol_l_h\": 9.595, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.75, \"viable_cell_density_million_ml\": 28.305}, {\"agitation_rpm\": 285.155, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 10.131, \"dissolved_oxygen_pct\": 17.741, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.547, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.023, \"our_mmol_l_h\": 11.025, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.771, \"viable_cell_density_million_ml\": 32.38}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9d729fab5d6fec69bfb0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 283.826, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.548, \"dissolved_oxygen_pct\": 36.692, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.371, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.186, \"our_mmol_l_h\": 7.176, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.932, \"viable_cell_density_million_ml\": 21.011}, {\"agitation_rpm\": 284.869, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.513, \"dissolved_oxygen_pct\": 36.164, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.339, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.142, \"our_mmol_l_h\": 8.199, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 24.029}, {\"agitation_rpm\": 285.771, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 8.847, \"dissolved_oxygen_pct\": 28.602, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.099, \"our_mmol_l_h\": 9.595, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.75, \"viable_cell_density_million_ml\": 28.305}, {\"agitation_rpm\": 285.155, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 10.131, \"dissolved_oxygen_pct\": 17.741, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.547, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.023, \"our_mmol_l_h\": 11.025, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.771, \"viable_cell_density_million_ml\": 32.38}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c66fb7640d0b688ef583", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 283.826, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.548, \"dissolved_oxygen_pct\": 36.692, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.371, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.186, \"our_mmol_l_h\": 7.176, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.932, \"viable_cell_density_million_ml\": 21.011}, {\"agitation_rpm\": 284.869, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.513, \"dissolved_oxygen_pct\": 36.164, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.339, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.142, \"our_mmol_l_h\": 8.199, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 24.029}, {\"agitation_rpm\": 285.771, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 8.847, \"dissolved_oxygen_pct\": 28.602, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.099, \"our_mmol_l_h\": 9.595, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.75, \"viable_cell_density_million_ml\": 28.305}, {\"agitation_rpm\": 285.155, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 10.131, \"dissolved_oxygen_pct\": 17.741, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.547, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.023, \"our_mmol_l_h\": 11.025, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.771, \"viable_cell_density_million_ml\": 32.38}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d81e56d16f9f5fb3ebd1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.152, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.188, \"dissolved_oxygen_pct\": 37.602, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.112, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.905, \"our_mmol_l_h\": 5.661, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.378, \"viable_cell_density_million_ml\": 16.554}, {\"agitation_rpm\": 270.251, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.734, \"dissolved_oxygen_pct\": 36.971, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.042, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 6.247, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.614, \"viable_cell_density_million_ml\": 18.432}, {\"agitation_rpm\": 285.675, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 36.313, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.987, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.729, \"our_mmol_l_h\": 6.456, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 20.043}, {\"agitation_rpm\": 284.872, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.996, \"dissolved_oxygen_pct\": 35.688, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.637, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.613, \"our_mmol_l_h\": 6.431, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.141, \"viable_cell_density_million_ml\": 21.335}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f8424bf045df99f6ede1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.152, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.188, \"dissolved_oxygen_pct\": 37.602, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.112, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.905, \"our_mmol_l_h\": 5.661, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.378, \"viable_cell_density_million_ml\": 16.554}, {\"agitation_rpm\": 270.251, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.734, \"dissolved_oxygen_pct\": 36.971, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.042, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 6.247, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.614, \"viable_cell_density_million_ml\": 18.432}, {\"agitation_rpm\": 285.675, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 36.313, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.987, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.729, \"our_mmol_l_h\": 6.456, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 20.043}, {\"agitation_rpm\": 284.872, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.996, \"dissolved_oxygen_pct\": 35.688, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.637, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.613, \"our_mmol_l_h\": 6.431, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.141, \"viable_cell_density_million_ml\": 21.335}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-57ee20fec83b624529f7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.152, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.188, \"dissolved_oxygen_pct\": 37.602, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.112, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.905, \"our_mmol_l_h\": 5.661, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.378, \"viable_cell_density_million_ml\": 16.554}, {\"agitation_rpm\": 270.251, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.734, \"dissolved_oxygen_pct\": 36.971, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.042, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 6.247, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.614, \"viable_cell_density_million_ml\": 18.432}, {\"agitation_rpm\": 285.675, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 36.313, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.987, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.729, \"our_mmol_l_h\": 6.456, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 20.043}, {\"agitation_rpm\": 284.872, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.996, \"dissolved_oxygen_pct\": 35.688, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.637, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.613, \"our_mmol_l_h\": 6.431, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.141, \"viable_cell_density_million_ml\": 21.335}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-95544d8f82062102fa9d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.152, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.188, \"dissolved_oxygen_pct\": 37.602, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.112, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.905, \"our_mmol_l_h\": 5.661, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.378, \"viable_cell_density_million_ml\": 16.554}, {\"agitation_rpm\": 270.251, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.734, \"dissolved_oxygen_pct\": 36.971, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.042, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 6.247, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.614, \"viable_cell_density_million_ml\": 18.432}, {\"agitation_rpm\": 285.675, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 36.313, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.987, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.729, \"our_mmol_l_h\": 6.456, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.796, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 20.043}, {\"agitation_rpm\": 284.872, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.996, \"dissolved_oxygen_pct\": 35.688, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.637, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.613, \"our_mmol_l_h\": 6.431, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.141, \"viable_cell_density_million_ml\": 21.335}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2cc0bc3f475f681c1f56", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.289, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 4.241, \"dissolved_oxygen_pct\": 39.784, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.162, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.034, \"our_mmol_l_h\": 4.611, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.605, \"viable_cell_density_million_ml\": 13.501}, {\"agitation_rpm\": 245.306, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.872, \"dissolved_oxygen_pct\": 38.278, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.117, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.975, \"our_mmol_l_h\": 5.299, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.589, \"viable_cell_density_million_ml\": 15.65}, {\"agitation_rpm\": 271.75, \"airflow_vvm\": 0.23, \"cer_mmol_l_h\": 5.736, \"dissolved_oxygen_pct\": 28.269, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.074, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.827, \"our_mmol_l_h\": 6.272, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.463, \"viable_cell_density_million_ml\": 18.47}, {\"agitation_rpm\": 285.581, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.57, \"dissolved_oxygen_pct\": 18.832, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.967, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.692, \"our_mmol_l_h\": 7.173, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.488, \"viable_cell_density_million_ml\": 21.17}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-734d92f3d66958492a25", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.289, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 4.241, \"dissolved_oxygen_pct\": 39.784, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.162, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.034, \"our_mmol_l_h\": 4.611, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.605, \"viable_cell_density_million_ml\": 13.501}, {\"agitation_rpm\": 245.306, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.872, \"dissolved_oxygen_pct\": 38.278, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.117, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.975, \"our_mmol_l_h\": 5.299, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.589, \"viable_cell_density_million_ml\": 15.65}, {\"agitation_rpm\": 271.75, \"airflow_vvm\": 0.23, \"cer_mmol_l_h\": 5.736, \"dissolved_oxygen_pct\": 28.269, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.074, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.827, \"our_mmol_l_h\": 6.272, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.463, \"viable_cell_density_million_ml\": 18.47}, {\"agitation_rpm\": 285.581, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.57, \"dissolved_oxygen_pct\": 18.832, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.967, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.692, \"our_mmol_l_h\": 7.173, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.488, \"viable_cell_density_million_ml\": 21.17}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d564f4f4e190caacb2b0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.289, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 4.241, \"dissolved_oxygen_pct\": 39.784, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.162, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.034, \"our_mmol_l_h\": 4.611, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.605, \"viable_cell_density_million_ml\": 13.501}, {\"agitation_rpm\": 245.306, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.872, \"dissolved_oxygen_pct\": 38.278, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.117, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.975, \"our_mmol_l_h\": 5.299, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.589, \"viable_cell_density_million_ml\": 15.65}, {\"agitation_rpm\": 271.75, \"airflow_vvm\": 0.23, \"cer_mmol_l_h\": 5.736, \"dissolved_oxygen_pct\": 28.269, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.074, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.827, \"our_mmol_l_h\": 6.272, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.463, \"viable_cell_density_million_ml\": 18.47}, {\"agitation_rpm\": 285.581, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.57, \"dissolved_oxygen_pct\": 18.832, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.967, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.692, \"our_mmol_l_h\": 7.173, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.488, \"viable_cell_density_million_ml\": 21.17}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-16167e560163f17f8e99", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.289, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 4.241, \"dissolved_oxygen_pct\": 39.784, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.162, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.034, \"our_mmol_l_h\": 4.611, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.605, \"viable_cell_density_million_ml\": 13.501}, {\"agitation_rpm\": 245.306, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.872, \"dissolved_oxygen_pct\": 38.278, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.117, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.975, \"our_mmol_l_h\": 5.299, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.589, \"viable_cell_density_million_ml\": 15.65}, {\"agitation_rpm\": 271.75, \"airflow_vvm\": 0.23, \"cer_mmol_l_h\": 5.736, \"dissolved_oxygen_pct\": 28.269, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.074, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.827, \"our_mmol_l_h\": 6.272, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.463, \"viable_cell_density_million_ml\": 18.47}, {\"agitation_rpm\": 285.581, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.57, \"dissolved_oxygen_pct\": 18.832, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.967, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.692, \"our_mmol_l_h\": 7.173, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.488, \"viable_cell_density_million_ml\": 21.17}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6754c24d20cf20635efc", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.815, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.97, \"dissolved_oxygen_pct\": 35.832, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.348, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.13, \"our_mmol_l_h\": 8.682, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 25.564}, {\"agitation_rpm\": 285.893, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.86, \"dissolved_oxygen_pct\": 36.984, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.418, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.091, \"our_mmol_l_h\": 9.622, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.879, \"viable_cell_density_million_ml\": 28.252}, {\"agitation_rpm\": 285.981, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 9.844, \"dissolved_oxygen_pct\": 35.611, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.567, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.047, \"our_mmol_l_h\": 10.205, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.801, \"viable_cell_density_million_ml\": 31.058}, {\"agitation_rpm\": 286.142, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.788, \"dissolved_oxygen_pct\": 36.478, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.505, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.973, \"our_mmol_l_h\": 10.546, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 33.512}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-23a9eadd936dd55a0ce5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.815, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.97, \"dissolved_oxygen_pct\": 35.832, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.348, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.13, \"our_mmol_l_h\": 8.682, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 25.564}, {\"agitation_rpm\": 285.893, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.86, \"dissolved_oxygen_pct\": 36.984, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.418, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.091, \"our_mmol_l_h\": 9.622, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.879, \"viable_cell_density_million_ml\": 28.252}, {\"agitation_rpm\": 285.981, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 9.844, \"dissolved_oxygen_pct\": 35.611, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.567, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.047, \"our_mmol_l_h\": 10.205, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.801, \"viable_cell_density_million_ml\": 31.058}, {\"agitation_rpm\": 286.142, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.788, \"dissolved_oxygen_pct\": 36.478, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.505, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.973, \"our_mmol_l_h\": 10.546, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 33.512}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fbcdc5712ce37ac6d6c3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.815, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.97, \"dissolved_oxygen_pct\": 35.832, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.348, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.13, \"our_mmol_l_h\": 8.682, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 25.564}, {\"agitation_rpm\": 285.893, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.86, \"dissolved_oxygen_pct\": 36.984, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.418, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.091, \"our_mmol_l_h\": 9.622, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.879, \"viable_cell_density_million_ml\": 28.252}, {\"agitation_rpm\": 285.981, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 9.844, \"dissolved_oxygen_pct\": 35.611, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.567, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.047, \"our_mmol_l_h\": 10.205, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.801, \"viable_cell_density_million_ml\": 31.058}, {\"agitation_rpm\": 286.142, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.788, \"dissolved_oxygen_pct\": 36.478, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.505, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.973, \"our_mmol_l_h\": 10.546, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 33.512}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-10da6cfc11a385e8aa07", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.815, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.97, \"dissolved_oxygen_pct\": 35.832, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.348, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.13, \"our_mmol_l_h\": 8.682, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 25.564}, {\"agitation_rpm\": 285.893, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.86, \"dissolved_oxygen_pct\": 36.984, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.418, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.091, \"our_mmol_l_h\": 9.622, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.879, \"viable_cell_density_million_ml\": 28.252}, {\"agitation_rpm\": 285.981, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 9.844, \"dissolved_oxygen_pct\": 35.611, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.567, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.047, \"our_mmol_l_h\": 10.205, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.801, \"viable_cell_density_million_ml\": 31.058}, {\"agitation_rpm\": 286.142, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.788, \"dissolved_oxygen_pct\": 36.478, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.505, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.973, \"our_mmol_l_h\": 10.546, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 33.512}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b6c54da43f89a60c9d39", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.73, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.979, \"dissolved_oxygen_pct\": 36.872, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.396, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.134, \"our_mmol_l_h\": 8.691, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.943, \"viable_cell_density_million_ml\": 25.598}, {\"agitation_rpm\": 285.484, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.132, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.417, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 9.602, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.716, \"viable_cell_density_million_ml\": 28.217}, {\"agitation_rpm\": 284.269, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 9.859, \"dissolved_oxygen_pct\": 29.251, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.519, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 10.73, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.926, \"viable_cell_density_million_ml\": 31.583}, {\"agitation_rpm\": 285.323, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 10.816, \"dissolved_oxygen_pct\": 17.684, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.706, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 11.762, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.897, \"viable_cell_density_million_ml\": 34.609}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-06defda251640adb7259", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.73, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.979, \"dissolved_oxygen_pct\": 36.872, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.396, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.134, \"our_mmol_l_h\": 8.691, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.943, \"viable_cell_density_million_ml\": 25.598}, {\"agitation_rpm\": 285.484, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.132, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.417, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 9.602, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.716, \"viable_cell_density_million_ml\": 28.217}, {\"agitation_rpm\": 284.269, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 9.859, \"dissolved_oxygen_pct\": 29.251, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.519, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 10.73, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.926, \"viable_cell_density_million_ml\": 31.583}, {\"agitation_rpm\": 285.323, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 10.816, \"dissolved_oxygen_pct\": 17.684, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.706, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 11.762, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.897, \"viable_cell_density_million_ml\": 34.609}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8e29b3ac0d61835f90b1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.73, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.979, \"dissolved_oxygen_pct\": 36.872, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.396, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.134, \"our_mmol_l_h\": 8.691, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.943, \"viable_cell_density_million_ml\": 25.598}, {\"agitation_rpm\": 285.484, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.132, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.417, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 9.602, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.716, \"viable_cell_density_million_ml\": 28.217}, {\"agitation_rpm\": 284.269, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 9.859, \"dissolved_oxygen_pct\": 29.251, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.519, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 10.73, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.926, \"viable_cell_density_million_ml\": 31.583}, {\"agitation_rpm\": 285.323, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 10.816, \"dissolved_oxygen_pct\": 17.684, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.706, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 11.762, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.897, \"viable_cell_density_million_ml\": 34.609}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b7fa53ee0346f5e076ca", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.73, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.979, \"dissolved_oxygen_pct\": 36.872, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.396, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.134, \"our_mmol_l_h\": 8.691, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.943, \"viable_cell_density_million_ml\": 25.598}, {\"agitation_rpm\": 285.484, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.833, \"dissolved_oxygen_pct\": 36.132, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.417, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 9.602, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.716, \"viable_cell_density_million_ml\": 28.217}, {\"agitation_rpm\": 284.269, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 9.859, \"dissolved_oxygen_pct\": 29.251, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.519, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 10.73, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.926, \"viable_cell_density_million_ml\": 31.583}, {\"agitation_rpm\": 285.323, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 10.816, \"dissolved_oxygen_pct\": 17.684, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.706, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 11.762, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.897, \"viable_cell_density_million_ml\": 34.609}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1492ba5f3d807d610904", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.803, \"airflow_vvm\": 0.195, \"cer_mmol_l_h\": 3.198, \"dissolved_oxygen_pct\": 41.056, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.465, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 3.479, \"ph\": 6.977, \"phase\": \"exponential\", \"temperature_c\": 36.81, \"viability_pct\": 98.479, \"viable_cell_density_million_ml\": 10.367}, {\"agitation_rpm\": 217.144, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.873, \"dissolved_oxygen_pct\": 39.847, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.157, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.917, \"our_mmol_l_h\": 4.252, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.55, \"viable_cell_density_million_ml\": 12.384}, {\"agitation_rpm\": 244.886, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 4.918, \"dissolved_oxygen_pct\": 37.72, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.138, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.791, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.79, \"viability_pct\": 98.457, \"viable_cell_density_million_ml\": 15.202}, {\"agitation_rpm\": 277.089, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.014, \"dissolved_oxygen_pct\": 36.066, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.841, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.808, \"our_mmol_l_h\": 5.305, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.556, \"viable_cell_density_million_ml\": 18.274}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-249e5a7f56600f870466", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.803, \"airflow_vvm\": 0.195, \"cer_mmol_l_h\": 3.198, \"dissolved_oxygen_pct\": 41.056, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.465, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 3.479, \"ph\": 6.977, \"phase\": \"exponential\", \"temperature_c\": 36.81, \"viability_pct\": 98.479, \"viable_cell_density_million_ml\": 10.367}, {\"agitation_rpm\": 217.144, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.873, \"dissolved_oxygen_pct\": 39.847, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.157, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.917, \"our_mmol_l_h\": 4.252, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.55, \"viable_cell_density_million_ml\": 12.384}, {\"agitation_rpm\": 244.886, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 4.918, \"dissolved_oxygen_pct\": 37.72, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.138, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.791, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.79, \"viability_pct\": 98.457, \"viable_cell_density_million_ml\": 15.202}, {\"agitation_rpm\": 277.089, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.014, \"dissolved_oxygen_pct\": 36.066, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.841, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.808, \"our_mmol_l_h\": 5.305, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.556, \"viable_cell_density_million_ml\": 18.274}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d4e33eb265e5412551eb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.803, \"airflow_vvm\": 0.195, \"cer_mmol_l_h\": 3.198, \"dissolved_oxygen_pct\": 41.056, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.465, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 3.479, \"ph\": 6.977, \"phase\": \"exponential\", \"temperature_c\": 36.81, \"viability_pct\": 98.479, \"viable_cell_density_million_ml\": 10.367}, {\"agitation_rpm\": 217.144, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.873, \"dissolved_oxygen_pct\": 39.847, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.157, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.917, \"our_mmol_l_h\": 4.252, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.55, \"viable_cell_density_million_ml\": 12.384}, {\"agitation_rpm\": 244.886, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 4.918, \"dissolved_oxygen_pct\": 37.72, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.138, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.791, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.79, \"viability_pct\": 98.457, \"viable_cell_density_million_ml\": 15.202}, {\"agitation_rpm\": 277.089, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.014, \"dissolved_oxygen_pct\": 36.066, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.841, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.808, \"our_mmol_l_h\": 5.305, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.556, \"viable_cell_density_million_ml\": 18.274}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-178cda3308d4ea5556e1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.803, \"airflow_vvm\": 0.195, \"cer_mmol_l_h\": 3.198, \"dissolved_oxygen_pct\": 41.056, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.465, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 3.479, \"ph\": 6.977, \"phase\": \"exponential\", \"temperature_c\": 36.81, \"viability_pct\": 98.479, \"viable_cell_density_million_ml\": 10.367}, {\"agitation_rpm\": 217.144, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.873, \"dissolved_oxygen_pct\": 39.847, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.157, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.917, \"our_mmol_l_h\": 4.252, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.55, \"viable_cell_density_million_ml\": 12.384}, {\"agitation_rpm\": 244.886, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 4.918, \"dissolved_oxygen_pct\": 37.72, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.138, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.791, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.79, \"viability_pct\": 98.457, \"viable_cell_density_million_ml\": 15.202}, {\"agitation_rpm\": 277.089, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.014, \"dissolved_oxygen_pct\": 36.066, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.841, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.808, \"our_mmol_l_h\": 5.305, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.556, \"viable_cell_density_million_ml\": 18.274}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9f8a9e96ed5cfa5b3b80", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.719, \"airflow_vvm\": 0.226, \"cer_mmol_l_h\": 4.232, \"dissolved_oxygen_pct\": 38.781, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.127, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.011, \"our_mmol_l_h\": 4.628, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.793, \"viability_pct\": 98.364, \"viable_cell_density_million_ml\": 13.617}, {\"agitation_rpm\": 245.813, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.893, \"dissolved_oxygen_pct\": 37.623, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.125, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.952, \"our_mmol_l_h\": 5.29, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.624, \"viable_cell_density_million_ml\": 15.643}, {\"agitation_rpm\": 271.784, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 5.778, \"dissolved_oxygen_pct\": 27.904, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.264, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.607, \"viable_cell_density_million_ml\": 18.424}, {\"agitation_rpm\": 284.543, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 6.582, \"dissolved_oxygen_pct\": 18.961, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.902, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.678, \"our_mmol_l_h\": 7.199, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.388, \"viable_cell_density_million_ml\": 21.027}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0b2b2d3a9ab76e031597", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.719, \"airflow_vvm\": 0.226, \"cer_mmol_l_h\": 4.232, \"dissolved_oxygen_pct\": 38.781, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.127, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.011, \"our_mmol_l_h\": 4.628, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.793, \"viability_pct\": 98.364, \"viable_cell_density_million_ml\": 13.617}, {\"agitation_rpm\": 245.813, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.893, \"dissolved_oxygen_pct\": 37.623, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.125, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.952, \"our_mmol_l_h\": 5.29, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.624, \"viable_cell_density_million_ml\": 15.643}, {\"agitation_rpm\": 271.784, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 5.778, \"dissolved_oxygen_pct\": 27.904, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.264, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.607, \"viable_cell_density_million_ml\": 18.424}, {\"agitation_rpm\": 284.543, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 6.582, \"dissolved_oxygen_pct\": 18.961, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.902, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.678, \"our_mmol_l_h\": 7.199, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.388, \"viable_cell_density_million_ml\": 21.027}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-af713110fdfdbd660869", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.719, \"airflow_vvm\": 0.226, \"cer_mmol_l_h\": 4.232, \"dissolved_oxygen_pct\": 38.781, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.127, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.011, \"our_mmol_l_h\": 4.628, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.793, \"viability_pct\": 98.364, \"viable_cell_density_million_ml\": 13.617}, {\"agitation_rpm\": 245.813, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.893, \"dissolved_oxygen_pct\": 37.623, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.125, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.952, \"our_mmol_l_h\": 5.29, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.624, \"viable_cell_density_million_ml\": 15.643}, {\"agitation_rpm\": 271.784, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 5.778, \"dissolved_oxygen_pct\": 27.904, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.264, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.607, \"viable_cell_density_million_ml\": 18.424}, {\"agitation_rpm\": 284.543, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 6.582, \"dissolved_oxygen_pct\": 18.961, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.902, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.678, \"our_mmol_l_h\": 7.199, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.388, \"viable_cell_density_million_ml\": 21.027}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-358728406a686a38635a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.719, \"airflow_vvm\": 0.226, \"cer_mmol_l_h\": 4.232, \"dissolved_oxygen_pct\": 38.781, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.127, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.011, \"our_mmol_l_h\": 4.628, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.793, \"viability_pct\": 98.364, \"viable_cell_density_million_ml\": 13.617}, {\"agitation_rpm\": 245.813, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.893, \"dissolved_oxygen_pct\": 37.623, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.125, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.952, \"our_mmol_l_h\": 5.29, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.624, \"viable_cell_density_million_ml\": 15.643}, {\"agitation_rpm\": 271.784, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 5.778, \"dissolved_oxygen_pct\": 27.904, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.264, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.607, \"viable_cell_density_million_ml\": 18.424}, {\"agitation_rpm\": 284.543, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 6.582, \"dissolved_oxygen_pct\": 18.961, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.902, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.678, \"our_mmol_l_h\": 7.199, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.388, \"viable_cell_density_million_ml\": 21.027}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c21d023749b8ff1b9a94", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.39, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.722, \"dissolved_oxygen_pct\": 39.555, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.607, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.237, \"our_mmol_l_h\": 4.092, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 11.949}, {\"agitation_rpm\": 237.999, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 4.616, \"dissolved_oxygen_pct\": 38.869, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.486, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.239, \"our_mmol_l_h\": 5.028, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.85, \"viable_cell_density_million_ml\": 14.792}, {\"agitation_rpm\": 278.931, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.06, \"dissolved_oxygen_pct\": 36.999, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.453, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.183, \"our_mmol_l_h\": 6.073, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.794, \"viability_pct\": 98.681, \"viable_cell_density_million_ml\": 19.034}, {\"agitation_rpm\": 284.889, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 36.528, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.151, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.13, \"our_mmol_l_h\": 7.518, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.725, \"viable_cell_density_million_ml\": 24.633}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-40d56b2e92a3c424f982", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.39, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.722, \"dissolved_oxygen_pct\": 39.555, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.607, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.237, \"our_mmol_l_h\": 4.092, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 11.949}, {\"agitation_rpm\": 237.999, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 4.616, \"dissolved_oxygen_pct\": 38.869, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.486, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.239, \"our_mmol_l_h\": 5.028, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.85, \"viable_cell_density_million_ml\": 14.792}, {\"agitation_rpm\": 278.931, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.06, \"dissolved_oxygen_pct\": 36.999, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.453, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.183, \"our_mmol_l_h\": 6.073, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.794, \"viability_pct\": 98.681, \"viable_cell_density_million_ml\": 19.034}, {\"agitation_rpm\": 284.889, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 36.528, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.151, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.13, \"our_mmol_l_h\": 7.518, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.725, \"viable_cell_density_million_ml\": 24.633}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ffc1fc827c6f41648ee6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.39, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.722, \"dissolved_oxygen_pct\": 39.555, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.607, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.237, \"our_mmol_l_h\": 4.092, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 11.949}, {\"agitation_rpm\": 237.999, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 4.616, \"dissolved_oxygen_pct\": 38.869, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.486, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.239, \"our_mmol_l_h\": 5.028, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.85, \"viable_cell_density_million_ml\": 14.792}, {\"agitation_rpm\": 278.931, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.06, \"dissolved_oxygen_pct\": 36.999, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.453, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.183, \"our_mmol_l_h\": 6.073, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.794, \"viability_pct\": 98.681, \"viable_cell_density_million_ml\": 19.034}, {\"agitation_rpm\": 284.889, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 36.528, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.151, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.13, \"our_mmol_l_h\": 7.518, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.725, \"viable_cell_density_million_ml\": 24.633}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-419a8ee11de9e2b5d9c8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.39, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.722, \"dissolved_oxygen_pct\": 39.555, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.607, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.237, \"our_mmol_l_h\": 4.092, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 11.949}, {\"agitation_rpm\": 237.999, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 4.616, \"dissolved_oxygen_pct\": 38.869, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.486, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.239, \"our_mmol_l_h\": 5.028, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.85, \"viable_cell_density_million_ml\": 14.792}, {\"agitation_rpm\": 278.931, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.06, \"dissolved_oxygen_pct\": 36.999, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.453, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.183, \"our_mmol_l_h\": 6.073, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.794, \"viability_pct\": 98.681, \"viable_cell_density_million_ml\": 19.034}, {\"agitation_rpm\": 284.889, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 36.528, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.151, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.13, \"our_mmol_l_h\": 7.518, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.725, \"viable_cell_density_million_ml\": 24.633}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a0014d48c5e819f39b6e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.157, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.735, \"dissolved_oxygen_pct\": 40.143, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.556, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.226, \"our_mmol_l_h\": 4.053, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.844, \"viable_cell_density_million_ml\": 11.898}, {\"agitation_rpm\": 238.454, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.651, \"dissolved_oxygen_pct\": 38.735, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.462, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.235, \"our_mmol_l_h\": 5.015, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.811, \"viable_cell_density_million_ml\": 14.719}, {\"agitation_rpm\": 280.437, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.075, \"dissolved_oxygen_pct\": 28.921, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.401, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 6.593, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.814, \"viable_cell_density_million_ml\": 19.39}, {\"agitation_rpm\": 284.019, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 17.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.333, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.706, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 25.528}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0d84cdf47d24603d850d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.157, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.735, \"dissolved_oxygen_pct\": 40.143, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.556, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.226, \"our_mmol_l_h\": 4.053, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.844, \"viable_cell_density_million_ml\": 11.898}, {\"agitation_rpm\": 238.454, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.651, \"dissolved_oxygen_pct\": 38.735, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.462, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.235, \"our_mmol_l_h\": 5.015, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.811, \"viable_cell_density_million_ml\": 14.719}, {\"agitation_rpm\": 280.437, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.075, \"dissolved_oxygen_pct\": 28.921, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.401, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 6.593, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.814, \"viable_cell_density_million_ml\": 19.39}, {\"agitation_rpm\": 284.019, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 17.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.333, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.706, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 25.528}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-14270b286d2ae2c3b20c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.157, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.735, \"dissolved_oxygen_pct\": 40.143, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.556, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.226, \"our_mmol_l_h\": 4.053, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.844, \"viable_cell_density_million_ml\": 11.898}, {\"agitation_rpm\": 238.454, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.651, \"dissolved_oxygen_pct\": 38.735, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.462, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.235, \"our_mmol_l_h\": 5.015, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.811, \"viable_cell_density_million_ml\": 14.719}, {\"agitation_rpm\": 280.437, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.075, \"dissolved_oxygen_pct\": 28.921, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.401, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 6.593, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.814, \"viable_cell_density_million_ml\": 19.39}, {\"agitation_rpm\": 284.019, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 17.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.333, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.706, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 25.528}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-389180e4d5d0d7f0120c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.157, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.735, \"dissolved_oxygen_pct\": 40.143, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.556, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.226, \"our_mmol_l_h\": 4.053, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.844, \"viable_cell_density_million_ml\": 11.898}, {\"agitation_rpm\": 238.454, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.651, \"dissolved_oxygen_pct\": 38.735, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.462, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.235, \"our_mmol_l_h\": 5.015, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.811, \"viable_cell_density_million_ml\": 14.719}, {\"agitation_rpm\": 280.437, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.075, \"dissolved_oxygen_pct\": 28.921, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.401, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 6.593, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.814, \"viable_cell_density_million_ml\": 19.39}, {\"agitation_rpm\": 284.019, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 17.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.333, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.706, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 25.528}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d00fbdcadae8cf05b743", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.113, \"airflow_vvm\": 0.288, \"cer_mmol_l_h\": 5.982, \"dissolved_oxygen_pct\": 36.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.998, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.814, \"our_mmol_l_h\": 6.546, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.386, \"viable_cell_density_million_ml\": 19.263}, {\"agitation_rpm\": 283.876, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.423, \"dissolved_oxygen_pct\": 36.06, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.998, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.726, \"our_mmol_l_h\": 6.986, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.554, \"viable_cell_density_million_ml\": 20.523}, {\"agitation_rpm\": 284.681, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.887, \"dissolved_oxygen_pct\": 36.907, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.93, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.612, \"our_mmol_l_h\": 6.993, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.482, \"viable_cell_density_million_ml\": 21.553}, {\"agitation_rpm\": 285.216, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.184, \"dissolved_oxygen_pct\": 36.692, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.595, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.471, \"our_mmol_l_h\": 6.604, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 96.436, \"viable_cell_density_million_ml\": 22.007}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f8e6fef40cfb63a96ab9", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.113, \"airflow_vvm\": 0.288, \"cer_mmol_l_h\": 5.982, \"dissolved_oxygen_pct\": 36.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.998, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.814, \"our_mmol_l_h\": 6.546, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.386, \"viable_cell_density_million_ml\": 19.263}, {\"agitation_rpm\": 283.876, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.423, \"dissolved_oxygen_pct\": 36.06, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.998, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.726, \"our_mmol_l_h\": 6.986, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.554, \"viable_cell_density_million_ml\": 20.523}, {\"agitation_rpm\": 284.681, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.887, \"dissolved_oxygen_pct\": 36.907, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.93, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.612, \"our_mmol_l_h\": 6.993, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.482, \"viable_cell_density_million_ml\": 21.553}, {\"agitation_rpm\": 285.216, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.184, \"dissolved_oxygen_pct\": 36.692, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.595, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.471, \"our_mmol_l_h\": 6.604, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 96.436, \"viable_cell_density_million_ml\": 22.007}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9e2fe4dcddcb54092dae", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.113, \"airflow_vvm\": 0.288, \"cer_mmol_l_h\": 5.982, \"dissolved_oxygen_pct\": 36.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.998, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.814, \"our_mmol_l_h\": 6.546, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.386, \"viable_cell_density_million_ml\": 19.263}, {\"agitation_rpm\": 283.876, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.423, \"dissolved_oxygen_pct\": 36.06, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.998, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.726, \"our_mmol_l_h\": 6.986, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.554, \"viable_cell_density_million_ml\": 20.523}, {\"agitation_rpm\": 284.681, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.887, \"dissolved_oxygen_pct\": 36.907, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.93, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.612, \"our_mmol_l_h\": 6.993, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.482, \"viable_cell_density_million_ml\": 21.553}, {\"agitation_rpm\": 285.216, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.184, \"dissolved_oxygen_pct\": 36.692, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.595, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.471, \"our_mmol_l_h\": 6.604, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 96.436, \"viable_cell_density_million_ml\": 22.007}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b06ef7629f2222652ce8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.113, \"airflow_vvm\": 0.288, \"cer_mmol_l_h\": 5.982, \"dissolved_oxygen_pct\": 36.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.998, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.814, \"our_mmol_l_h\": 6.546, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.386, \"viable_cell_density_million_ml\": 19.263}, {\"agitation_rpm\": 283.876, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.423, \"dissolved_oxygen_pct\": 36.06, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.998, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.726, \"our_mmol_l_h\": 6.986, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.554, \"viable_cell_density_million_ml\": 20.523}, {\"agitation_rpm\": 284.681, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.887, \"dissolved_oxygen_pct\": 36.907, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.93, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.612, \"our_mmol_l_h\": 6.993, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.482, \"viable_cell_density_million_ml\": 21.553}, {\"agitation_rpm\": 285.216, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.184, \"dissolved_oxygen_pct\": 36.692, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.595, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.471, \"our_mmol_l_h\": 6.604, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 96.436, \"viable_cell_density_million_ml\": 22.007}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b0d8657e9294a12780f7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.667, \"airflow_vvm\": 0.194, \"cer_mmol_l_h\": 3.214, \"dissolved_oxygen_pct\": 41.073, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.45, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.75, \"our_mmol_l_h\": 3.48, \"ph\": 6.974, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 10.254}, {\"agitation_rpm\": 217.771, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.876, \"dissolved_oxygen_pct\": 39.572, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.143, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.95, \"our_mmol_l_h\": 4.224, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.472, \"viable_cell_density_million_ml\": 12.479}, {\"agitation_rpm\": 245.62, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.903, \"dissolved_oxygen_pct\": 30.566, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.148, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 5.315, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.555, \"viable_cell_density_million_ml\": 15.607}, {\"agitation_rpm\": 277.401, \"airflow_vvm\": 0.171, \"cer_mmol_l_h\": 5.976, \"dissolved_oxygen_pct\": 18.809, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.0, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 6.543, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 19.195}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c22583255e52987e7283", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.667, \"airflow_vvm\": 0.194, \"cer_mmol_l_h\": 3.214, \"dissolved_oxygen_pct\": 41.073, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.45, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.75, \"our_mmol_l_h\": 3.48, \"ph\": 6.974, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 10.254}, {\"agitation_rpm\": 217.771, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.876, \"dissolved_oxygen_pct\": 39.572, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.143, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.95, \"our_mmol_l_h\": 4.224, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.472, \"viable_cell_density_million_ml\": 12.479}, {\"agitation_rpm\": 245.62, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.903, \"dissolved_oxygen_pct\": 30.566, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.148, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 5.315, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.555, \"viable_cell_density_million_ml\": 15.607}, {\"agitation_rpm\": 277.401, \"airflow_vvm\": 0.171, \"cer_mmol_l_h\": 5.976, \"dissolved_oxygen_pct\": 18.809, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.0, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 6.543, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 19.195}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9cf7c65bef8371683261", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.667, \"airflow_vvm\": 0.194, \"cer_mmol_l_h\": 3.214, \"dissolved_oxygen_pct\": 41.073, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.45, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.75, \"our_mmol_l_h\": 3.48, \"ph\": 6.974, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 10.254}, {\"agitation_rpm\": 217.771, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.876, \"dissolved_oxygen_pct\": 39.572, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.143, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.95, \"our_mmol_l_h\": 4.224, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.472, \"viable_cell_density_million_ml\": 12.479}, {\"agitation_rpm\": 245.62, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.903, \"dissolved_oxygen_pct\": 30.566, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.148, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 5.315, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.555, \"viable_cell_density_million_ml\": 15.607}, {\"agitation_rpm\": 277.401, \"airflow_vvm\": 0.171, \"cer_mmol_l_h\": 5.976, \"dissolved_oxygen_pct\": 18.809, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.0, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 6.543, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 19.195}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-999cb4e5749d73e98584", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.667, \"airflow_vvm\": 0.194, \"cer_mmol_l_h\": 3.214, \"dissolved_oxygen_pct\": 41.073, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.45, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.75, \"our_mmol_l_h\": 3.48, \"ph\": 6.974, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 10.254}, {\"agitation_rpm\": 217.771, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.876, \"dissolved_oxygen_pct\": 39.572, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.143, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.95, \"our_mmol_l_h\": 4.224, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.472, \"viable_cell_density_million_ml\": 12.479}, {\"agitation_rpm\": 245.62, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.903, \"dissolved_oxygen_pct\": 30.566, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.148, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 5.315, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.555, \"viable_cell_density_million_ml\": 15.607}, {\"agitation_rpm\": 277.401, \"airflow_vvm\": 0.171, \"cer_mmol_l_h\": 5.976, \"dissolved_oxygen_pct\": 18.809, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.0, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 6.543, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 19.195}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-714f6352afd7ecb31a6f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.278, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.585, \"dissolved_oxygen_pct\": 36.271, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.338, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 7.173, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.802, \"viability_pct\": 98.658, \"viable_cell_density_million_ml\": 21.109}, {\"agitation_rpm\": 285.068, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.512, \"dissolved_oxygen_pct\": 36.276, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.331, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 8.204, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 98.828, \"viable_cell_density_million_ml\": 24.045}, {\"agitation_rpm\": 285.713, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.835, \"dissolved_oxygen_pct\": 35.979, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.429, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.084, \"our_mmol_l_h\": 9.089, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.71, \"viable_cell_density_million_ml\": 27.822}, {\"agitation_rpm\": 283.866, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.136, \"dissolved_oxygen_pct\": 36.021, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.4, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.056, \"our_mmol_l_h\": 9.826, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.672, \"viable_cell_density_million_ml\": 31.472}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-23a179a8f8240f8fc2da", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.278, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.585, \"dissolved_oxygen_pct\": 36.271, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.338, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 7.173, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.802, \"viability_pct\": 98.658, \"viable_cell_density_million_ml\": 21.109}, {\"agitation_rpm\": 285.068, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.512, \"dissolved_oxygen_pct\": 36.276, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.331, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 8.204, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 98.828, \"viable_cell_density_million_ml\": 24.045}, {\"agitation_rpm\": 285.713, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.835, \"dissolved_oxygen_pct\": 35.979, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.429, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.084, \"our_mmol_l_h\": 9.089, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.71, \"viable_cell_density_million_ml\": 27.822}, {\"agitation_rpm\": 283.866, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.136, \"dissolved_oxygen_pct\": 36.021, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.4, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.056, \"our_mmol_l_h\": 9.826, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.672, \"viable_cell_density_million_ml\": 31.472}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5b77f5833e518918e623", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.278, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.585, \"dissolved_oxygen_pct\": 36.271, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.338, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 7.173, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.802, \"viability_pct\": 98.658, \"viable_cell_density_million_ml\": 21.109}, {\"agitation_rpm\": 285.068, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.512, \"dissolved_oxygen_pct\": 36.276, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.331, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 8.204, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 98.828, \"viable_cell_density_million_ml\": 24.045}, {\"agitation_rpm\": 285.713, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.835, \"dissolved_oxygen_pct\": 35.979, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.429, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.084, \"our_mmol_l_h\": 9.089, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.71, \"viable_cell_density_million_ml\": 27.822}, {\"agitation_rpm\": 283.866, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.136, \"dissolved_oxygen_pct\": 36.021, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.4, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.056, \"our_mmol_l_h\": 9.826, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.672, \"viable_cell_density_million_ml\": 31.472}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8fd170d94bdf0610d236", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.278, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.585, \"dissolved_oxygen_pct\": 36.271, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.338, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 7.173, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.802, \"viability_pct\": 98.658, \"viable_cell_density_million_ml\": 21.109}, {\"agitation_rpm\": 285.068, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.512, \"dissolved_oxygen_pct\": 36.276, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.331, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 8.204, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 98.828, \"viable_cell_density_million_ml\": 24.045}, {\"agitation_rpm\": 285.713, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.835, \"dissolved_oxygen_pct\": 35.979, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.429, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.084, \"our_mmol_l_h\": 9.089, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.71, \"viable_cell_density_million_ml\": 27.822}, {\"agitation_rpm\": 283.866, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.136, \"dissolved_oxygen_pct\": 36.021, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.4, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.056, \"our_mmol_l_h\": 9.826, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.672, \"viable_cell_density_million_ml\": 31.472}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4a25114c410257665d3f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.559, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 8.01, \"dissolved_oxygen_pct\": 36.819, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.392, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.682, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.914, \"viable_cell_density_million_ml\": 25.568}, {\"agitation_rpm\": 284.877, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.809, \"dissolved_oxygen_pct\": 35.652, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.1, \"our_mmol_l_h\": 9.585, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.727, \"viable_cell_density_million_ml\": 28.219}, {\"agitation_rpm\": 285.273, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.84, \"dissolved_oxygen_pct\": 28.413, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.529, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 10.687, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 31.563}, {\"agitation_rpm\": 283.842, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.813, \"dissolved_oxygen_pct\": 18.11, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.758, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.008, \"our_mmol_l_h\": 11.721, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.833, \"viable_cell_density_million_ml\": 34.523}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ca36cefe2a4dd808141f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.559, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 8.01, \"dissolved_oxygen_pct\": 36.819, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.392, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.682, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.914, \"viable_cell_density_million_ml\": 25.568}, {\"agitation_rpm\": 284.877, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.809, \"dissolved_oxygen_pct\": 35.652, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.1, \"our_mmol_l_h\": 9.585, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.727, \"viable_cell_density_million_ml\": 28.219}, {\"agitation_rpm\": 285.273, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.84, \"dissolved_oxygen_pct\": 28.413, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.529, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 10.687, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 31.563}, {\"agitation_rpm\": 283.842, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.813, \"dissolved_oxygen_pct\": 18.11, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.758, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.008, \"our_mmol_l_h\": 11.721, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.833, \"viable_cell_density_million_ml\": 34.523}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-324f2a023ad2fa1d1dca", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.559, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 8.01, \"dissolved_oxygen_pct\": 36.819, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.392, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.682, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.914, \"viable_cell_density_million_ml\": 25.568}, {\"agitation_rpm\": 284.877, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.809, \"dissolved_oxygen_pct\": 35.652, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.1, \"our_mmol_l_h\": 9.585, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.727, \"viable_cell_density_million_ml\": 28.219}, {\"agitation_rpm\": 285.273, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.84, \"dissolved_oxygen_pct\": 28.413, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.529, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 10.687, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 31.563}, {\"agitation_rpm\": 283.842, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.813, \"dissolved_oxygen_pct\": 18.11, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.758, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.008, \"our_mmol_l_h\": 11.721, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.833, \"viable_cell_density_million_ml\": 34.523}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-287bf5c2b235d70e1f79", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.559, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 8.01, \"dissolved_oxygen_pct\": 36.819, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.392, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.682, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.914, \"viable_cell_density_million_ml\": 25.568}, {\"agitation_rpm\": 284.877, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.809, \"dissolved_oxygen_pct\": 35.652, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.1, \"our_mmol_l_h\": 9.585, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.727, \"viable_cell_density_million_ml\": 28.219}, {\"agitation_rpm\": 285.273, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.84, \"dissolved_oxygen_pct\": 28.413, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.529, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 10.687, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 31.563}, {\"agitation_rpm\": 283.842, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.813, \"dissolved_oxygen_pct\": 18.11, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.758, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.008, \"our_mmol_l_h\": 11.721, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.833, \"viable_cell_density_million_ml\": 34.523}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5d6840acef495316a2f2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.551, \"airflow_vvm\": 0.227, \"cer_mmol_l_h\": 4.206, \"dissolved_oxygen_pct\": 38.953, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.143, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.021, \"our_mmol_l_h\": 4.603, \"ph\": 6.954, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.559, \"viable_cell_density_million_ml\": 13.523}, {\"agitation_rpm\": 245.45, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.922, \"dissolved_oxygen_pct\": 38.346, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.085, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.975, \"our_mmol_l_h\": 5.334, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 15.576}, {\"agitation_rpm\": 269.828, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.788, \"dissolved_oxygen_pct\": 35.89, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.109, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.852, \"our_mmol_l_h\": 5.722, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.588, \"viable_cell_density_million_ml\": 17.91}, {\"agitation_rpm\": 285.282, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.57, \"dissolved_oxygen_pct\": 36.681, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.769, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.706, \"our_mmol_l_h\": 5.972, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.465, \"viable_cell_density_million_ml\": 20.082}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-92b6a0f31a022dca0fed", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.551, \"airflow_vvm\": 0.227, \"cer_mmol_l_h\": 4.206, \"dissolved_oxygen_pct\": 38.953, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.143, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.021, \"our_mmol_l_h\": 4.603, \"ph\": 6.954, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.559, \"viable_cell_density_million_ml\": 13.523}, {\"agitation_rpm\": 245.45, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.922, \"dissolved_oxygen_pct\": 38.346, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.085, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.975, \"our_mmol_l_h\": 5.334, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 15.576}, {\"agitation_rpm\": 269.828, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.788, \"dissolved_oxygen_pct\": 35.89, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.109, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.852, \"our_mmol_l_h\": 5.722, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.588, \"viable_cell_density_million_ml\": 17.91}, {\"agitation_rpm\": 285.282, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.57, \"dissolved_oxygen_pct\": 36.681, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.769, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.706, \"our_mmol_l_h\": 5.972, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.465, \"viable_cell_density_million_ml\": 20.082}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-600d842adfc8677d2af3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.551, \"airflow_vvm\": 0.227, \"cer_mmol_l_h\": 4.206, \"dissolved_oxygen_pct\": 38.953, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.143, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.021, \"our_mmol_l_h\": 4.603, \"ph\": 6.954, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.559, \"viable_cell_density_million_ml\": 13.523}, {\"agitation_rpm\": 245.45, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.922, \"dissolved_oxygen_pct\": 38.346, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.085, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.975, \"our_mmol_l_h\": 5.334, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 15.576}, {\"agitation_rpm\": 269.828, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.788, \"dissolved_oxygen_pct\": 35.89, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.109, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.852, \"our_mmol_l_h\": 5.722, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.588, \"viable_cell_density_million_ml\": 17.91}, {\"agitation_rpm\": 285.282, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.57, \"dissolved_oxygen_pct\": 36.681, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.769, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.706, \"our_mmol_l_h\": 5.972, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.465, \"viable_cell_density_million_ml\": 20.082}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f4f7cb814093ba06a975", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.551, \"airflow_vvm\": 0.227, \"cer_mmol_l_h\": 4.206, \"dissolved_oxygen_pct\": 38.953, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.143, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.021, \"our_mmol_l_h\": 4.603, \"ph\": 6.954, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.559, \"viable_cell_density_million_ml\": 13.523}, {\"agitation_rpm\": 245.45, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.922, \"dissolved_oxygen_pct\": 38.346, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.085, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.975, \"our_mmol_l_h\": 5.334, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 15.576}, {\"agitation_rpm\": 269.828, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.788, \"dissolved_oxygen_pct\": 35.89, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.109, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.852, \"our_mmol_l_h\": 5.722, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.588, \"viable_cell_density_million_ml\": 17.91}, {\"agitation_rpm\": 285.282, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.57, \"dissolved_oxygen_pct\": 36.681, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.769, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.706, \"our_mmol_l_h\": 5.972, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.465, \"viable_cell_density_million_ml\": 20.082}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5b3f74c96a7bf60234fe", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.375, \"airflow_vvm\": 0.264, \"cer_mmol_l_h\": 5.192, \"dissolved_oxygen_pct\": 37.081, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.064, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.938, \"our_mmol_l_h\": 5.634, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.449, \"viable_cell_density_million_ml\": 16.613}, {\"agitation_rpm\": 271.583, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.731, \"dissolved_oxygen_pct\": 36.455, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.01, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.262, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.585, \"viable_cell_density_million_ml\": 18.468}, {\"agitation_rpm\": 285.214, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 28.738, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.997, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.71, \"our_mmol_l_h\": 6.983, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 20.596}, {\"agitation_rpm\": 283.863, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.007, \"dissolved_oxygen_pct\": 18.535, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.903, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.591, \"our_mmol_l_h\": 7.584, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.006, \"viable_cell_density_million_ml\": 22.361}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-340d07213633ae627fe7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.375, \"airflow_vvm\": 0.264, \"cer_mmol_l_h\": 5.192, \"dissolved_oxygen_pct\": 37.081, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.064, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.938, \"our_mmol_l_h\": 5.634, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.449, \"viable_cell_density_million_ml\": 16.613}, {\"agitation_rpm\": 271.583, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.731, \"dissolved_oxygen_pct\": 36.455, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.01, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.262, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.585, \"viable_cell_density_million_ml\": 18.468}, {\"agitation_rpm\": 285.214, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 28.738, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.997, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.71, \"our_mmol_l_h\": 6.983, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 20.596}, {\"agitation_rpm\": 283.863, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.007, \"dissolved_oxygen_pct\": 18.535, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.903, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.591, \"our_mmol_l_h\": 7.584, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.006, \"viable_cell_density_million_ml\": 22.361}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-99fc9af977cea83dede3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.375, \"airflow_vvm\": 0.264, \"cer_mmol_l_h\": 5.192, \"dissolved_oxygen_pct\": 37.081, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.064, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.938, \"our_mmol_l_h\": 5.634, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.449, \"viable_cell_density_million_ml\": 16.613}, {\"agitation_rpm\": 271.583, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.731, \"dissolved_oxygen_pct\": 36.455, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.01, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.262, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.585, \"viable_cell_density_million_ml\": 18.468}, {\"agitation_rpm\": 285.214, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 28.738, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.997, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.71, \"our_mmol_l_h\": 6.983, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 20.596}, {\"agitation_rpm\": 283.863, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.007, \"dissolved_oxygen_pct\": 18.535, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.903, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.591, \"our_mmol_l_h\": 7.584, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.006, \"viable_cell_density_million_ml\": 22.361}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9751477cd44c8f42653c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.375, \"airflow_vvm\": 0.264, \"cer_mmol_l_h\": 5.192, \"dissolved_oxygen_pct\": 37.081, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.064, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.938, \"our_mmol_l_h\": 5.634, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.449, \"viable_cell_density_million_ml\": 16.613}, {\"agitation_rpm\": 271.583, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.731, \"dissolved_oxygen_pct\": 36.455, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.01, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.262, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.585, \"viable_cell_density_million_ml\": 18.468}, {\"agitation_rpm\": 285.214, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 28.738, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.997, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.71, \"our_mmol_l_h\": 6.983, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 20.596}, {\"agitation_rpm\": 283.863, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.007, \"dissolved_oxygen_pct\": 18.535, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.903, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.591, \"our_mmol_l_h\": 7.584, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.006, \"viable_cell_density_million_ml\": 22.361}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8067feda6d40f21d438b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.568, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.096, \"dissolved_oxygen_pct\": 36.726, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.472, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.238, \"our_mmol_l_h\": 5.522, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 16.345}, {\"agitation_rpm\": 280.245, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.385, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 6.618, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.817, \"viability_pct\": 98.855, \"viable_cell_density_million_ml\": 19.393}, {\"agitation_rpm\": 285.42, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.53, \"dissolved_oxygen_pct\": 36.114, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.44, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.143, \"our_mmol_l_h\": 7.676, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.854, \"viable_cell_density_million_ml\": 23.711}, {\"agitation_rpm\": 284.822, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 9.203, \"dissolved_oxygen_pct\": 36.7, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.264, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.065, \"our_mmol_l_h\": 8.794, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.695, \"viable_cell_density_million_ml\": 28.442}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-45325334cea98f048040", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.568, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.096, \"dissolved_oxygen_pct\": 36.726, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.472, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.238, \"our_mmol_l_h\": 5.522, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 16.345}, {\"agitation_rpm\": 280.245, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.385, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 6.618, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.817, \"viability_pct\": 98.855, \"viable_cell_density_million_ml\": 19.393}, {\"agitation_rpm\": 285.42, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.53, \"dissolved_oxygen_pct\": 36.114, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.44, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.143, \"our_mmol_l_h\": 7.676, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.854, \"viable_cell_density_million_ml\": 23.711}, {\"agitation_rpm\": 284.822, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 9.203, \"dissolved_oxygen_pct\": 36.7, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.264, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.065, \"our_mmol_l_h\": 8.794, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.695, \"viable_cell_density_million_ml\": 28.442}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-341d224bcd5374dd9e12", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.568, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.096, \"dissolved_oxygen_pct\": 36.726, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.472, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.238, \"our_mmol_l_h\": 5.522, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 16.345}, {\"agitation_rpm\": 280.245, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.385, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 6.618, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.817, \"viability_pct\": 98.855, \"viable_cell_density_million_ml\": 19.393}, {\"agitation_rpm\": 285.42, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.53, \"dissolved_oxygen_pct\": 36.114, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.44, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.143, \"our_mmol_l_h\": 7.676, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.854, \"viable_cell_density_million_ml\": 23.711}, {\"agitation_rpm\": 284.822, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 9.203, \"dissolved_oxygen_pct\": 36.7, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.264, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.065, \"our_mmol_l_h\": 8.794, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.695, \"viable_cell_density_million_ml\": 28.442}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d3fd45ae9ce08e486656", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.568, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.096, \"dissolved_oxygen_pct\": 36.726, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.472, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.238, \"our_mmol_l_h\": 5.522, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 16.345}, {\"agitation_rpm\": 280.245, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.385, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 6.618, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.817, \"viability_pct\": 98.855, \"viable_cell_density_million_ml\": 19.393}, {\"agitation_rpm\": 285.42, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.53, \"dissolved_oxygen_pct\": 36.114, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.44, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.143, \"our_mmol_l_h\": 7.676, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.854, \"viable_cell_density_million_ml\": 23.711}, {\"agitation_rpm\": 284.822, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 9.203, \"dissolved_oxygen_pct\": 36.7, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.264, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.065, \"our_mmol_l_h\": 8.794, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.695, \"viable_cell_density_million_ml\": 28.442}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f958bab020edaf5de477", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 211.471, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.759, \"dissolved_oxygen_pct\": 39.62, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.607, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.245, \"our_mmol_l_h\": 4.068, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.737, \"viable_cell_density_million_ml\": 12.041}, {\"agitation_rpm\": 238.554, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.65, \"dissolved_oxygen_pct\": 38.346, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.5, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.268, \"our_mmol_l_h\": 5.053, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.792, \"viability_pct\": 98.669, \"viable_cell_density_million_ml\": 14.814}, {\"agitation_rpm\": 279.766, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.091, \"dissolved_oxygen_pct\": 28.333, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.401, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.179, \"our_mmol_l_h\": 6.616, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.794, \"viability_pct\": 98.803, \"viable_cell_density_million_ml\": 19.471}, {\"agitation_rpm\": 284.674, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 8.016, \"dissolved_oxygen_pct\": 17.889, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.34, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.689, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 25.612}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1bcb0b9f1bcb4aede972", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 211.471, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.759, \"dissolved_oxygen_pct\": 39.62, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.607, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.245, \"our_mmol_l_h\": 4.068, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.737, \"viable_cell_density_million_ml\": 12.041}, {\"agitation_rpm\": 238.554, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.65, \"dissolved_oxygen_pct\": 38.346, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.5, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.268, \"our_mmol_l_h\": 5.053, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.792, \"viability_pct\": 98.669, \"viable_cell_density_million_ml\": 14.814}, {\"agitation_rpm\": 279.766, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.091, \"dissolved_oxygen_pct\": 28.333, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.401, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.179, \"our_mmol_l_h\": 6.616, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.794, \"viability_pct\": 98.803, \"viable_cell_density_million_ml\": 19.471}, {\"agitation_rpm\": 284.674, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 8.016, \"dissolved_oxygen_pct\": 17.889, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.34, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.689, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 25.612}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2eab1d1a7ec14ba51a79", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 211.471, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.759, \"dissolved_oxygen_pct\": 39.62, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.607, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.245, \"our_mmol_l_h\": 4.068, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.737, \"viable_cell_density_million_ml\": 12.041}, {\"agitation_rpm\": 238.554, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.65, \"dissolved_oxygen_pct\": 38.346, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.5, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.268, \"our_mmol_l_h\": 5.053, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.792, \"viability_pct\": 98.669, \"viable_cell_density_million_ml\": 14.814}, {\"agitation_rpm\": 279.766, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.091, \"dissolved_oxygen_pct\": 28.333, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.401, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.179, \"our_mmol_l_h\": 6.616, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.794, \"viability_pct\": 98.803, \"viable_cell_density_million_ml\": 19.471}, {\"agitation_rpm\": 284.674, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 8.016, \"dissolved_oxygen_pct\": 17.889, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.34, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.689, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 25.612}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9e6fbfabc706fb1806e0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 211.471, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.759, \"dissolved_oxygen_pct\": 39.62, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.607, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.245, \"our_mmol_l_h\": 4.068, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.737, \"viable_cell_density_million_ml\": 12.041}, {\"agitation_rpm\": 238.554, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.65, \"dissolved_oxygen_pct\": 38.346, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.5, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.268, \"our_mmol_l_h\": 5.053, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.792, \"viability_pct\": 98.669, \"viable_cell_density_million_ml\": 14.814}, {\"agitation_rpm\": 279.766, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.091, \"dissolved_oxygen_pct\": 28.333, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.401, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.179, \"our_mmol_l_h\": 6.616, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.794, \"viability_pct\": 98.803, \"viable_cell_density_million_ml\": 19.471}, {\"agitation_rpm\": 284.674, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 8.016, \"dissolved_oxygen_pct\": 17.889, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.34, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.689, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 25.612}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-86612b590425091a7594", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.051, \"airflow_vvm\": 0.263, \"cer_mmol_l_h\": 5.2, \"dissolved_oxygen_pct\": 37.473, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.068, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.937, \"our_mmol_l_h\": 5.652, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.389, \"viable_cell_density_million_ml\": 16.687}, {\"agitation_rpm\": 271.19, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.738, \"dissolved_oxygen_pct\": 36.27, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.019, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.82, \"our_mmol_l_h\": 6.232, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.499, \"viable_cell_density_million_ml\": 18.36}, {\"agitation_rpm\": 285.569, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.451, \"dissolved_oxygen_pct\": 36.975, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.992, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 6.449, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.401, \"viable_cell_density_million_ml\": 20.18}, {\"agitation_rpm\": 286.021, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.976, \"dissolved_oxygen_pct\": 35.752, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.662, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.607, \"our_mmol_l_h\": 6.433, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.15, \"viable_cell_density_million_ml\": 21.434}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-14c0956d0b928e111674", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.051, \"airflow_vvm\": 0.263, \"cer_mmol_l_h\": 5.2, \"dissolved_oxygen_pct\": 37.473, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.068, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.937, \"our_mmol_l_h\": 5.652, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.389, \"viable_cell_density_million_ml\": 16.687}, {\"agitation_rpm\": 271.19, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.738, \"dissolved_oxygen_pct\": 36.27, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.019, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.82, \"our_mmol_l_h\": 6.232, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.499, \"viable_cell_density_million_ml\": 18.36}, {\"agitation_rpm\": 285.569, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.451, \"dissolved_oxygen_pct\": 36.975, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.992, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 6.449, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.401, \"viable_cell_density_million_ml\": 20.18}, {\"agitation_rpm\": 286.021, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.976, \"dissolved_oxygen_pct\": 35.752, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.662, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.607, \"our_mmol_l_h\": 6.433, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.15, \"viable_cell_density_million_ml\": 21.434}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a56b9e18e48a4f183182", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.051, \"airflow_vvm\": 0.263, \"cer_mmol_l_h\": 5.2, \"dissolved_oxygen_pct\": 37.473, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.068, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.937, \"our_mmol_l_h\": 5.652, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.389, \"viable_cell_density_million_ml\": 16.687}, {\"agitation_rpm\": 271.19, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.738, \"dissolved_oxygen_pct\": 36.27, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.019, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.82, \"our_mmol_l_h\": 6.232, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.499, \"viable_cell_density_million_ml\": 18.36}, {\"agitation_rpm\": 285.569, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.451, \"dissolved_oxygen_pct\": 36.975, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.992, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 6.449, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.401, \"viable_cell_density_million_ml\": 20.18}, {\"agitation_rpm\": 286.021, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.976, \"dissolved_oxygen_pct\": 35.752, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.662, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.607, \"our_mmol_l_h\": 6.433, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.15, \"viable_cell_density_million_ml\": 21.434}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cf035506e63d95728d47", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.051, \"airflow_vvm\": 0.263, \"cer_mmol_l_h\": 5.2, \"dissolved_oxygen_pct\": 37.473, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.068, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.937, \"our_mmol_l_h\": 5.652, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.389, \"viable_cell_density_million_ml\": 16.687}, {\"agitation_rpm\": 271.19, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.738, \"dissolved_oxygen_pct\": 36.27, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.019, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.82, \"our_mmol_l_h\": 6.232, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.499, \"viable_cell_density_million_ml\": 18.36}, {\"agitation_rpm\": 285.569, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.451, \"dissolved_oxygen_pct\": 36.975, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.992, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 6.449, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.401, \"viable_cell_density_million_ml\": 20.18}, {\"agitation_rpm\": 286.021, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.976, \"dissolved_oxygen_pct\": 35.752, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.662, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.607, \"our_mmol_l_h\": 6.433, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.15, \"viable_cell_density_million_ml\": 21.434}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a69219e44a89ce28bab8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.152, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 4.238, \"dissolved_oxygen_pct\": 39.422, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.164, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.026, \"our_mmol_l_h\": 4.577, \"ph\": 6.966, \"phase\": \"exponential\", \"temperature_c\": 36.815, \"viability_pct\": 98.464, \"viable_cell_density_million_ml\": 13.461}, {\"agitation_rpm\": 246.722, \"airflow_vvm\": 0.253, \"cer_mmol_l_h\": 4.893, \"dissolved_oxygen_pct\": 37.401, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.103, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.935, \"our_mmol_l_h\": 5.319, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.385, \"viable_cell_density_million_ml\": 15.593}, {\"agitation_rpm\": 271.378, \"airflow_vvm\": 0.234, \"cer_mmol_l_h\": 5.752, \"dissolved_oxygen_pct\": 28.121, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.006, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.862, \"our_mmol_l_h\": 6.281, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.59, \"viable_cell_density_million_ml\": 18.383}, {\"agitation_rpm\": 284.296, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.628, \"dissolved_oxygen_pct\": 17.98, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.932, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.694, \"our_mmol_l_h\": 7.191, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 21.143}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e3ddb9ae5ce0facaaa66", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.152, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 4.238, \"dissolved_oxygen_pct\": 39.422, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.164, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.026, \"our_mmol_l_h\": 4.577, \"ph\": 6.966, \"phase\": \"exponential\", \"temperature_c\": 36.815, \"viability_pct\": 98.464, \"viable_cell_density_million_ml\": 13.461}, {\"agitation_rpm\": 246.722, \"airflow_vvm\": 0.253, \"cer_mmol_l_h\": 4.893, \"dissolved_oxygen_pct\": 37.401, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.103, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.935, \"our_mmol_l_h\": 5.319, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.385, \"viable_cell_density_million_ml\": 15.593}, {\"agitation_rpm\": 271.378, \"airflow_vvm\": 0.234, \"cer_mmol_l_h\": 5.752, \"dissolved_oxygen_pct\": 28.121, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.006, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.862, \"our_mmol_l_h\": 6.281, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.59, \"viable_cell_density_million_ml\": 18.383}, {\"agitation_rpm\": 284.296, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.628, \"dissolved_oxygen_pct\": 17.98, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.932, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.694, \"our_mmol_l_h\": 7.191, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 21.143}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6f1828d0b924f4fae94e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.152, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 4.238, \"dissolved_oxygen_pct\": 39.422, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.164, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.026, \"our_mmol_l_h\": 4.577, \"ph\": 6.966, \"phase\": \"exponential\", \"temperature_c\": 36.815, \"viability_pct\": 98.464, \"viable_cell_density_million_ml\": 13.461}, {\"agitation_rpm\": 246.722, \"airflow_vvm\": 0.253, \"cer_mmol_l_h\": 4.893, \"dissolved_oxygen_pct\": 37.401, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.103, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.935, \"our_mmol_l_h\": 5.319, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.385, \"viable_cell_density_million_ml\": 15.593}, {\"agitation_rpm\": 271.378, \"airflow_vvm\": 0.234, \"cer_mmol_l_h\": 5.752, \"dissolved_oxygen_pct\": 28.121, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.006, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.862, \"our_mmol_l_h\": 6.281, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.59, \"viable_cell_density_million_ml\": 18.383}, {\"agitation_rpm\": 284.296, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.628, \"dissolved_oxygen_pct\": 17.98, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.932, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.694, \"our_mmol_l_h\": 7.191, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 21.143}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-98de92971e8f309c0665", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.152, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 4.238, \"dissolved_oxygen_pct\": 39.422, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.164, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.026, \"our_mmol_l_h\": 4.577, \"ph\": 6.966, \"phase\": \"exponential\", \"temperature_c\": 36.815, \"viability_pct\": 98.464, \"viable_cell_density_million_ml\": 13.461}, {\"agitation_rpm\": 246.722, \"airflow_vvm\": 0.253, \"cer_mmol_l_h\": 4.893, \"dissolved_oxygen_pct\": 37.401, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.103, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.935, \"our_mmol_l_h\": 5.319, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.385, \"viable_cell_density_million_ml\": 15.593}, {\"agitation_rpm\": 271.378, \"airflow_vvm\": 0.234, \"cer_mmol_l_h\": 5.752, \"dissolved_oxygen_pct\": 28.121, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.006, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.862, \"our_mmol_l_h\": 6.281, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.59, \"viable_cell_density_million_ml\": 18.383}, {\"agitation_rpm\": 284.296, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.628, \"dissolved_oxygen_pct\": 17.98, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.932, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.694, \"our_mmol_l_h\": 7.191, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 21.143}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aed0281ec90a869c2ac4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.987, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.116, \"dissolved_oxygen_pct\": 37.15, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.427, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.212, \"our_mmol_l_h\": 5.517, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.806, \"viable_cell_density_million_ml\": 16.346}, {\"agitation_rpm\": 280.092, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.099, \"dissolved_oxygen_pct\": 36.891, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.344, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.214, \"our_mmol_l_h\": 6.63, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.695, \"viable_cell_density_million_ml\": 19.458}, {\"agitation_rpm\": 285.88, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.322, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.381, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.686, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.769, \"viable_cell_density_million_ml\": 23.662}, {\"agitation_rpm\": 285.317, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.222, \"dissolved_oxygen_pct\": 36.609, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.211, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.086, \"our_mmol_l_h\": 8.819, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.848, \"viable_cell_density_million_ml\": 28.47}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d217cc9958f909231ac1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.987, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.116, \"dissolved_oxygen_pct\": 37.15, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.427, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.212, \"our_mmol_l_h\": 5.517, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.806, \"viable_cell_density_million_ml\": 16.346}, {\"agitation_rpm\": 280.092, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.099, \"dissolved_oxygen_pct\": 36.891, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.344, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.214, \"our_mmol_l_h\": 6.63, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.695, \"viable_cell_density_million_ml\": 19.458}, {\"agitation_rpm\": 285.88, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.322, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.381, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.686, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.769, \"viable_cell_density_million_ml\": 23.662}, {\"agitation_rpm\": 285.317, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.222, \"dissolved_oxygen_pct\": 36.609, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.211, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.086, \"our_mmol_l_h\": 8.819, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.848, \"viable_cell_density_million_ml\": 28.47}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cfccf0f5420cb5e62bb8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.987, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.116, \"dissolved_oxygen_pct\": 37.15, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.427, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.212, \"our_mmol_l_h\": 5.517, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.806, \"viable_cell_density_million_ml\": 16.346}, {\"agitation_rpm\": 280.092, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.099, \"dissolved_oxygen_pct\": 36.891, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.344, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.214, \"our_mmol_l_h\": 6.63, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.695, \"viable_cell_density_million_ml\": 19.458}, {\"agitation_rpm\": 285.88, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.322, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.381, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.686, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.769, \"viable_cell_density_million_ml\": 23.662}, {\"agitation_rpm\": 285.317, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.222, \"dissolved_oxygen_pct\": 36.609, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.211, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.086, \"our_mmol_l_h\": 8.819, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.848, \"viable_cell_density_million_ml\": 28.47}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-421d473aefaf0a4e85d6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.987, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.116, \"dissolved_oxygen_pct\": 37.15, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.427, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.212, \"our_mmol_l_h\": 5.517, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.806, \"viable_cell_density_million_ml\": 16.346}, {\"agitation_rpm\": 280.092, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 6.099, \"dissolved_oxygen_pct\": 36.891, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.344, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.214, \"our_mmol_l_h\": 6.63, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.695, \"viable_cell_density_million_ml\": 19.458}, {\"agitation_rpm\": 285.88, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.322, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.381, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.686, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.769, \"viable_cell_density_million_ml\": 23.662}, {\"agitation_rpm\": 285.317, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.222, \"dissolved_oxygen_pct\": 36.609, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.211, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.086, \"our_mmol_l_h\": 8.819, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.848, \"viable_cell_density_million_ml\": 28.47}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-133da00adac519b80c9e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.819, \"airflow_vvm\": 0.213, \"cer_mmol_l_h\": 3.758, \"dissolved_oxygen_pct\": 40.696, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.583, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.245, \"our_mmol_l_h\": 4.073, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.686, \"viable_cell_density_million_ml\": 12.034}, {\"agitation_rpm\": 239.036, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.607, \"dissolved_oxygen_pct\": 38.007, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.508, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.233, \"our_mmol_l_h\": 5.05, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.802, \"viability_pct\": 98.762, \"viable_cell_density_million_ml\": 14.876}, {\"agitation_rpm\": 279.268, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 6.11, \"dissolved_oxygen_pct\": 28.991, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.408, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.18, \"our_mmol_l_h\": 6.594, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.936, \"viable_cell_density_million_ml\": 19.38}, {\"agitation_rpm\": 284.227, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.022, \"dissolved_oxygen_pct\": 18.196, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.364, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.154, \"our_mmol_l_h\": 8.68, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.911, \"viable_cell_density_million_ml\": 25.513}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-34a01847b6a77311fdde", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.819, \"airflow_vvm\": 0.213, \"cer_mmol_l_h\": 3.758, \"dissolved_oxygen_pct\": 40.696, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.583, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.245, \"our_mmol_l_h\": 4.073, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.686, \"viable_cell_density_million_ml\": 12.034}, {\"agitation_rpm\": 239.036, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.607, \"dissolved_oxygen_pct\": 38.007, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.508, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.233, \"our_mmol_l_h\": 5.05, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.802, \"viability_pct\": 98.762, \"viable_cell_density_million_ml\": 14.876}, {\"agitation_rpm\": 279.268, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 6.11, \"dissolved_oxygen_pct\": 28.991, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.408, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.18, \"our_mmol_l_h\": 6.594, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.936, \"viable_cell_density_million_ml\": 19.38}, {\"agitation_rpm\": 284.227, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.022, \"dissolved_oxygen_pct\": 18.196, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.364, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.154, \"our_mmol_l_h\": 8.68, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.911, \"viable_cell_density_million_ml\": 25.513}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b54e8a2c4971c0cd6de7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.819, \"airflow_vvm\": 0.213, \"cer_mmol_l_h\": 3.758, \"dissolved_oxygen_pct\": 40.696, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.583, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.245, \"our_mmol_l_h\": 4.073, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.686, \"viable_cell_density_million_ml\": 12.034}, {\"agitation_rpm\": 239.036, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.607, \"dissolved_oxygen_pct\": 38.007, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.508, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.233, \"our_mmol_l_h\": 5.05, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.802, \"viability_pct\": 98.762, \"viable_cell_density_million_ml\": 14.876}, {\"agitation_rpm\": 279.268, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 6.11, \"dissolved_oxygen_pct\": 28.991, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.408, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.18, \"our_mmol_l_h\": 6.594, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.936, \"viable_cell_density_million_ml\": 19.38}, {\"agitation_rpm\": 284.227, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.022, \"dissolved_oxygen_pct\": 18.196, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.364, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.154, \"our_mmol_l_h\": 8.68, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.911, \"viable_cell_density_million_ml\": 25.513}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aa45687a4dbb11232256", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.819, \"airflow_vvm\": 0.213, \"cer_mmol_l_h\": 3.758, \"dissolved_oxygen_pct\": 40.696, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.583, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.245, \"our_mmol_l_h\": 4.073, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.686, \"viable_cell_density_million_ml\": 12.034}, {\"agitation_rpm\": 239.036, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.607, \"dissolved_oxygen_pct\": 38.007, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.508, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.233, \"our_mmol_l_h\": 5.05, \"ph\": 7.001, \"phase\": \"exponential\", \"temperature_c\": 36.802, \"viability_pct\": 98.762, \"viable_cell_density_million_ml\": 14.876}, {\"agitation_rpm\": 279.268, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 6.11, \"dissolved_oxygen_pct\": 28.991, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.408, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.18, \"our_mmol_l_h\": 6.594, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.936, \"viable_cell_density_million_ml\": 19.38}, {\"agitation_rpm\": 284.227, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.022, \"dissolved_oxygen_pct\": 18.196, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.364, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.154, \"our_mmol_l_h\": 8.68, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.911, \"viable_cell_density_million_ml\": 25.513}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b2bc6f39b6d461e82cb2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.923, \"airflow_vvm\": 0.266, \"cer_mmol_l_h\": 5.193, \"dissolved_oxygen_pct\": 37.539, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.093, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.902, \"our_mmol_l_h\": 5.664, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 16.562}, {\"agitation_rpm\": 271.196, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.783, \"dissolved_oxygen_pct\": 36.084, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.062, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.847, \"our_mmol_l_h\": 6.246, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.51, \"viable_cell_density_million_ml\": 18.485}, {\"agitation_rpm\": 285.682, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.529, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.048, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.715, \"our_mmol_l_h\": 6.481, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.41, \"viable_cell_density_million_ml\": 20.122}, {\"agitation_rpm\": 286.017, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.022, \"dissolved_oxygen_pct\": 36.666, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.708, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.576, \"our_mmol_l_h\": 6.388, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 97.994, \"viable_cell_density_million_ml\": 21.426}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6a8c70994e11a73f41e6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.923, \"airflow_vvm\": 0.266, \"cer_mmol_l_h\": 5.193, \"dissolved_oxygen_pct\": 37.539, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.093, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.902, \"our_mmol_l_h\": 5.664, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 16.562}, {\"agitation_rpm\": 271.196, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.783, \"dissolved_oxygen_pct\": 36.084, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.062, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.847, \"our_mmol_l_h\": 6.246, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.51, \"viable_cell_density_million_ml\": 18.485}, {\"agitation_rpm\": 285.682, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.529, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.048, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.715, \"our_mmol_l_h\": 6.481, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.41, \"viable_cell_density_million_ml\": 20.122}, {\"agitation_rpm\": 286.017, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.022, \"dissolved_oxygen_pct\": 36.666, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.708, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.576, \"our_mmol_l_h\": 6.388, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 97.994, \"viable_cell_density_million_ml\": 21.426}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6c8ffce552505e61d331", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.923, \"airflow_vvm\": 0.266, \"cer_mmol_l_h\": 5.193, \"dissolved_oxygen_pct\": 37.539, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.093, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.902, \"our_mmol_l_h\": 5.664, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 16.562}, {\"agitation_rpm\": 271.196, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.783, \"dissolved_oxygen_pct\": 36.084, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.062, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.847, \"our_mmol_l_h\": 6.246, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.51, \"viable_cell_density_million_ml\": 18.485}, {\"agitation_rpm\": 285.682, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.529, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.048, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.715, \"our_mmol_l_h\": 6.481, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.41, \"viable_cell_density_million_ml\": 20.122}, {\"agitation_rpm\": 286.017, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.022, \"dissolved_oxygen_pct\": 36.666, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.708, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.576, \"our_mmol_l_h\": 6.388, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 97.994, \"viable_cell_density_million_ml\": 21.426}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-03d7567e70c554636131", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.923, \"airflow_vvm\": 0.266, \"cer_mmol_l_h\": 5.193, \"dissolved_oxygen_pct\": 37.539, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.093, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.902, \"our_mmol_l_h\": 5.664, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 16.562}, {\"agitation_rpm\": 271.196, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.783, \"dissolved_oxygen_pct\": 36.084, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.062, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.847, \"our_mmol_l_h\": 6.246, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.51, \"viable_cell_density_million_ml\": 18.485}, {\"agitation_rpm\": 285.682, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.445, \"dissolved_oxygen_pct\": 36.529, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.048, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.715, \"our_mmol_l_h\": 6.481, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.41, \"viable_cell_density_million_ml\": 20.122}, {\"agitation_rpm\": 286.017, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.022, \"dissolved_oxygen_pct\": 36.666, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.708, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.576, \"our_mmol_l_h\": 6.388, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 97.994, \"viable_cell_density_million_ml\": 21.426}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-72938786df0b29e5781a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.02, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.23, \"dissolved_oxygen_pct\": 39.415, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.119, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.999, \"our_mmol_l_h\": 4.601, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.784, \"viability_pct\": 98.379, \"viable_cell_density_million_ml\": 13.534}, {\"agitation_rpm\": 246.132, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.915, \"dissolved_oxygen_pct\": 37.183, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.136, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.966, \"our_mmol_l_h\": 5.32, \"ph\": 6.955, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.361, \"viable_cell_density_million_ml\": 15.564}, {\"agitation_rpm\": 271.808, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.744, \"dissolved_oxygen_pct\": 28.074, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.05, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.827, \"our_mmol_l_h\": 6.289, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.434, \"viable_cell_density_million_ml\": 18.358}, {\"agitation_rpm\": 285.023, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 6.584, \"dissolved_oxygen_pct\": 17.984, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.972, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.723, \"our_mmol_l_h\": 7.158, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.609, \"viable_cell_density_million_ml\": 21.138}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3c92418d8c0fe927a5f5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.02, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.23, \"dissolved_oxygen_pct\": 39.415, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.119, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.999, \"our_mmol_l_h\": 4.601, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.784, \"viability_pct\": 98.379, \"viable_cell_density_million_ml\": 13.534}, {\"agitation_rpm\": 246.132, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.915, \"dissolved_oxygen_pct\": 37.183, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.136, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.966, \"our_mmol_l_h\": 5.32, \"ph\": 6.955, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.361, \"viable_cell_density_million_ml\": 15.564}, {\"agitation_rpm\": 271.808, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.744, \"dissolved_oxygen_pct\": 28.074, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.05, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.827, \"our_mmol_l_h\": 6.289, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.434, \"viable_cell_density_million_ml\": 18.358}, {\"agitation_rpm\": 285.023, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 6.584, \"dissolved_oxygen_pct\": 17.984, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.972, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.723, \"our_mmol_l_h\": 7.158, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.609, \"viable_cell_density_million_ml\": 21.138}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ddc0d9e1886b2e12771a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.02, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.23, \"dissolved_oxygen_pct\": 39.415, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.119, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.999, \"our_mmol_l_h\": 4.601, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.784, \"viability_pct\": 98.379, \"viable_cell_density_million_ml\": 13.534}, {\"agitation_rpm\": 246.132, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.915, \"dissolved_oxygen_pct\": 37.183, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.136, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.966, \"our_mmol_l_h\": 5.32, \"ph\": 6.955, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.361, \"viable_cell_density_million_ml\": 15.564}, {\"agitation_rpm\": 271.808, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.744, \"dissolved_oxygen_pct\": 28.074, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.05, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.827, \"our_mmol_l_h\": 6.289, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.434, \"viable_cell_density_million_ml\": 18.358}, {\"agitation_rpm\": 285.023, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 6.584, \"dissolved_oxygen_pct\": 17.984, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.972, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.723, \"our_mmol_l_h\": 7.158, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.609, \"viable_cell_density_million_ml\": 21.138}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9b0d1c6c767476cfd7a6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.02, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.23, \"dissolved_oxygen_pct\": 39.415, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.119, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.999, \"our_mmol_l_h\": 4.601, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.784, \"viability_pct\": 98.379, \"viable_cell_density_million_ml\": 13.534}, {\"agitation_rpm\": 246.132, \"airflow_vvm\": 0.255, \"cer_mmol_l_h\": 4.915, \"dissolved_oxygen_pct\": 37.183, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.136, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.966, \"our_mmol_l_h\": 5.32, \"ph\": 6.955, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.361, \"viable_cell_density_million_ml\": 15.564}, {\"agitation_rpm\": 271.808, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.744, \"dissolved_oxygen_pct\": 28.074, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.05, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.827, \"our_mmol_l_h\": 6.289, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.434, \"viable_cell_density_million_ml\": 18.358}, {\"agitation_rpm\": 285.023, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 6.584, \"dissolved_oxygen_pct\": 17.984, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.972, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.723, \"our_mmol_l_h\": 7.158, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.609, \"viable_cell_density_million_ml\": 21.138}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cbfc86a5b3c1e25cdfcb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.229, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.569, \"dissolved_oxygen_pct\": 35.714, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.346, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.201, \"our_mmol_l_h\": 7.13, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 21.019}, {\"agitation_rpm\": 284.374, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.517, \"dissolved_oxygen_pct\": 36.831, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 8.194, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.805, \"viable_cell_density_million_ml\": 24.154}, {\"agitation_rpm\": 284.044, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.805, \"dissolved_oxygen_pct\": 36.766, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.43, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.1, \"our_mmol_l_h\": 9.082, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.932, \"viable_cell_density_million_ml\": 27.797}, {\"agitation_rpm\": 285.59, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.14, \"dissolved_oxygen_pct\": 36.276, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.359, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.058, \"our_mmol_l_h\": 9.837, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.654, \"viable_cell_density_million_ml\": 31.409}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-26dc77aa11baf6115048", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.229, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.569, \"dissolved_oxygen_pct\": 35.714, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.346, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.201, \"our_mmol_l_h\": 7.13, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 21.019}, {\"agitation_rpm\": 284.374, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.517, \"dissolved_oxygen_pct\": 36.831, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 8.194, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.805, \"viable_cell_density_million_ml\": 24.154}, {\"agitation_rpm\": 284.044, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.805, \"dissolved_oxygen_pct\": 36.766, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.43, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.1, \"our_mmol_l_h\": 9.082, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.932, \"viable_cell_density_million_ml\": 27.797}, {\"agitation_rpm\": 285.59, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.14, \"dissolved_oxygen_pct\": 36.276, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.359, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.058, \"our_mmol_l_h\": 9.837, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.654, \"viable_cell_density_million_ml\": 31.409}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-48b424eb29253757e56a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.229, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.569, \"dissolved_oxygen_pct\": 35.714, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.346, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.201, \"our_mmol_l_h\": 7.13, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 21.019}, {\"agitation_rpm\": 284.374, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.517, \"dissolved_oxygen_pct\": 36.831, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 8.194, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.805, \"viable_cell_density_million_ml\": 24.154}, {\"agitation_rpm\": 284.044, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.805, \"dissolved_oxygen_pct\": 36.766, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.43, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.1, \"our_mmol_l_h\": 9.082, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.932, \"viable_cell_density_million_ml\": 27.797}, {\"agitation_rpm\": 285.59, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.14, \"dissolved_oxygen_pct\": 36.276, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.359, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.058, \"our_mmol_l_h\": 9.837, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.654, \"viable_cell_density_million_ml\": 31.409}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fc9a706a486d6cfc320a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.229, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.569, \"dissolved_oxygen_pct\": 35.714, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.346, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.201, \"our_mmol_l_h\": 7.13, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 21.019}, {\"agitation_rpm\": 284.374, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.517, \"dissolved_oxygen_pct\": 36.831, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.372, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 8.194, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.805, \"viable_cell_density_million_ml\": 24.154}, {\"agitation_rpm\": 284.044, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.805, \"dissolved_oxygen_pct\": 36.766, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.43, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.1, \"our_mmol_l_h\": 9.082, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.932, \"viable_cell_density_million_ml\": 27.797}, {\"agitation_rpm\": 285.59, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.14, \"dissolved_oxygen_pct\": 36.276, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.359, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.058, \"our_mmol_l_h\": 9.837, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.791, \"viability_pct\": 98.654, \"viable_cell_density_million_ml\": 31.409}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-245ff4be4c1c6dc01d3d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.435, \"airflow_vvm\": 0.212, \"cer_mmol_l_h\": 3.765, \"dissolved_oxygen_pct\": 40.361, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.611, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 4.089, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.817, \"viability_pct\": 98.698, \"viable_cell_density_million_ml\": 11.91}, {\"agitation_rpm\": 238.855, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.627, \"dissolved_oxygen_pct\": 38.202, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.481, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.236, \"our_mmol_l_h\": 5.043, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.853, \"viable_cell_density_million_ml\": 14.852}, {\"agitation_rpm\": 279.45, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 6.102, \"dissolved_oxygen_pct\": 28.784, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.367, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.208, \"our_mmol_l_h\": 6.621, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.724, \"viable_cell_density_million_ml\": 19.415}, {\"agitation_rpm\": 284.127, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 18.333, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.346, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.681, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 25.634}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9b17ebeea550ad93c87e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.435, \"airflow_vvm\": 0.212, \"cer_mmol_l_h\": 3.765, \"dissolved_oxygen_pct\": 40.361, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.611, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 4.089, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.817, \"viability_pct\": 98.698, \"viable_cell_density_million_ml\": 11.91}, {\"agitation_rpm\": 238.855, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.627, \"dissolved_oxygen_pct\": 38.202, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.481, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.236, \"our_mmol_l_h\": 5.043, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.853, \"viable_cell_density_million_ml\": 14.852}, {\"agitation_rpm\": 279.45, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 6.102, \"dissolved_oxygen_pct\": 28.784, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.367, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.208, \"our_mmol_l_h\": 6.621, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.724, \"viable_cell_density_million_ml\": 19.415}, {\"agitation_rpm\": 284.127, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 18.333, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.346, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.681, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 25.634}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a239e866383b1f3292eb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.435, \"airflow_vvm\": 0.212, \"cer_mmol_l_h\": 3.765, \"dissolved_oxygen_pct\": 40.361, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.611, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 4.089, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.817, \"viability_pct\": 98.698, \"viable_cell_density_million_ml\": 11.91}, {\"agitation_rpm\": 238.855, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.627, \"dissolved_oxygen_pct\": 38.202, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.481, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.236, \"our_mmol_l_h\": 5.043, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.853, \"viable_cell_density_million_ml\": 14.852}, {\"agitation_rpm\": 279.45, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 6.102, \"dissolved_oxygen_pct\": 28.784, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.367, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.208, \"our_mmol_l_h\": 6.621, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.724, \"viable_cell_density_million_ml\": 19.415}, {\"agitation_rpm\": 284.127, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 18.333, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.346, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.681, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 25.634}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-887a7a3bda5932c2b256", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.435, \"airflow_vvm\": 0.212, \"cer_mmol_l_h\": 3.765, \"dissolved_oxygen_pct\": 40.361, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.611, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 4.089, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.817, \"viability_pct\": 98.698, \"viable_cell_density_million_ml\": 11.91}, {\"agitation_rpm\": 238.855, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.627, \"dissolved_oxygen_pct\": 38.202, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.481, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.236, \"our_mmol_l_h\": 5.043, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.853, \"viable_cell_density_million_ml\": 14.852}, {\"agitation_rpm\": 279.45, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 6.102, \"dissolved_oxygen_pct\": 28.784, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.367, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.208, \"our_mmol_l_h\": 6.621, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.724, \"viable_cell_density_million_ml\": 19.415}, {\"agitation_rpm\": 284.127, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 18.333, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.346, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.681, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 25.634}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6f8a21db0311508bfd4a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.336, \"airflow_vvm\": 0.288, \"cer_mmol_l_h\": 6.005, \"dissolved_oxygen_pct\": 36.155, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.019, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 6.555, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.529, \"viable_cell_density_million_ml\": 19.27}, {\"agitation_rpm\": 284.305, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 35.958, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.957, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 6.998, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.606, \"viable_cell_density_million_ml\": 20.592}, {\"agitation_rpm\": 283.912, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.87, \"dissolved_oxygen_pct\": 36.86, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.928, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.643, \"our_mmol_l_h\": 6.958, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.568, \"viable_cell_density_million_ml\": 21.51}, {\"agitation_rpm\": 285.724, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.182, \"dissolved_oxygen_pct\": 36.089, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.59, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.478, \"our_mmol_l_h\": 6.629, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 96.711, \"viable_cell_density_million_ml\": 21.971}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d5db96a77f2974da3330", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.336, \"airflow_vvm\": 0.288, \"cer_mmol_l_h\": 6.005, \"dissolved_oxygen_pct\": 36.155, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.019, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 6.555, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.529, \"viable_cell_density_million_ml\": 19.27}, {\"agitation_rpm\": 284.305, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 35.958, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.957, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 6.998, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.606, \"viable_cell_density_million_ml\": 20.592}, {\"agitation_rpm\": 283.912, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.87, \"dissolved_oxygen_pct\": 36.86, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.928, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.643, \"our_mmol_l_h\": 6.958, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.568, \"viable_cell_density_million_ml\": 21.51}, {\"agitation_rpm\": 285.724, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.182, \"dissolved_oxygen_pct\": 36.089, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.59, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.478, \"our_mmol_l_h\": 6.629, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 96.711, \"viable_cell_density_million_ml\": 21.971}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c14fab5febb4b225c578", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.336, \"airflow_vvm\": 0.288, \"cer_mmol_l_h\": 6.005, \"dissolved_oxygen_pct\": 36.155, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.019, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 6.555, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.529, \"viable_cell_density_million_ml\": 19.27}, {\"agitation_rpm\": 284.305, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 35.958, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.957, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 6.998, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.606, \"viable_cell_density_million_ml\": 20.592}, {\"agitation_rpm\": 283.912, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.87, \"dissolved_oxygen_pct\": 36.86, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.928, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.643, \"our_mmol_l_h\": 6.958, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.568, \"viable_cell_density_million_ml\": 21.51}, {\"agitation_rpm\": 285.724, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.182, \"dissolved_oxygen_pct\": 36.089, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.59, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.478, \"our_mmol_l_h\": 6.629, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 96.711, \"viable_cell_density_million_ml\": 21.971}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8bb0e6f47614199c2b45", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.336, \"airflow_vvm\": 0.288, \"cer_mmol_l_h\": 6.005, \"dissolved_oxygen_pct\": 36.155, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.019, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 6.555, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.529, \"viable_cell_density_million_ml\": 19.27}, {\"agitation_rpm\": 284.305, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 35.958, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.957, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 6.998, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.606, \"viable_cell_density_million_ml\": 20.592}, {\"agitation_rpm\": 283.912, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.87, \"dissolved_oxygen_pct\": 36.86, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.928, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.643, \"our_mmol_l_h\": 6.958, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.568, \"viable_cell_density_million_ml\": 21.51}, {\"agitation_rpm\": 285.724, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.182, \"dissolved_oxygen_pct\": 36.089, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.59, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.478, \"our_mmol_l_h\": 6.629, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 96.711, \"viable_cell_density_million_ml\": 21.971}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6422556a1e0fc030f4bf", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.025, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.173, \"dissolved_oxygen_pct\": 36.723, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.074, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.908, \"our_mmol_l_h\": 5.651, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.564, \"viable_cell_density_million_ml\": 16.653}, {\"agitation_rpm\": 270.063, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.751, \"dissolved_oxygen_pct\": 36.072, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.011, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.836, \"our_mmol_l_h\": 6.245, \"ph\": 6.959, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.407, \"viable_cell_density_million_ml\": 18.416}, {\"agitation_rpm\": 284.269, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 6.396, \"dissolved_oxygen_pct\": 28.494, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.947, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.743, \"our_mmol_l_h\": 7.002, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 20.53}, {\"agitation_rpm\": 285.93, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.007, \"dissolved_oxygen_pct\": 17.806, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.897, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.604, \"our_mmol_l_h\": 7.625, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 97.906, \"viable_cell_density_million_ml\": 22.435}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c3e6be83db0f81cd97d0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.025, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.173, \"dissolved_oxygen_pct\": 36.723, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.074, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.908, \"our_mmol_l_h\": 5.651, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.564, \"viable_cell_density_million_ml\": 16.653}, {\"agitation_rpm\": 270.063, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.751, \"dissolved_oxygen_pct\": 36.072, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.011, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.836, \"our_mmol_l_h\": 6.245, \"ph\": 6.959, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.407, \"viable_cell_density_million_ml\": 18.416}, {\"agitation_rpm\": 284.269, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 6.396, \"dissolved_oxygen_pct\": 28.494, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.947, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.743, \"our_mmol_l_h\": 7.002, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 20.53}, {\"agitation_rpm\": 285.93, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.007, \"dissolved_oxygen_pct\": 17.806, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.897, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.604, \"our_mmol_l_h\": 7.625, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 97.906, \"viable_cell_density_million_ml\": 22.435}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8becacdb8639740bfabb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.025, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.173, \"dissolved_oxygen_pct\": 36.723, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.074, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.908, \"our_mmol_l_h\": 5.651, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.564, \"viable_cell_density_million_ml\": 16.653}, {\"agitation_rpm\": 270.063, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.751, \"dissolved_oxygen_pct\": 36.072, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.011, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.836, \"our_mmol_l_h\": 6.245, \"ph\": 6.959, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.407, \"viable_cell_density_million_ml\": 18.416}, {\"agitation_rpm\": 284.269, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 6.396, \"dissolved_oxygen_pct\": 28.494, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.947, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.743, \"our_mmol_l_h\": 7.002, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 20.53}, {\"agitation_rpm\": 285.93, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.007, \"dissolved_oxygen_pct\": 17.806, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.897, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.604, \"our_mmol_l_h\": 7.625, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 97.906, \"viable_cell_density_million_ml\": 22.435}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-56fd29496d99ac5ad484", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.025, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.173, \"dissolved_oxygen_pct\": 36.723, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.074, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.908, \"our_mmol_l_h\": 5.651, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.564, \"viable_cell_density_million_ml\": 16.653}, {\"agitation_rpm\": 270.063, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.751, \"dissolved_oxygen_pct\": 36.072, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.011, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.836, \"our_mmol_l_h\": 6.245, \"ph\": 6.959, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.407, \"viable_cell_density_million_ml\": 18.416}, {\"agitation_rpm\": 284.269, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 6.396, \"dissolved_oxygen_pct\": 28.494, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.947, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.743, \"our_mmol_l_h\": 7.002, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 20.53}, {\"agitation_rpm\": 285.93, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.007, \"dissolved_oxygen_pct\": 17.806, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.897, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.604, \"our_mmol_l_h\": 7.625, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 97.906, \"viable_cell_density_million_ml\": 22.435}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ee77cd1398fe5f4d36cc", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.157, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.561, \"dissolved_oxygen_pct\": 35.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.167, \"our_mmol_l_h\": 7.132, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.789, \"viability_pct\": 98.796, \"viable_cell_density_million_ml\": 21.108}, {\"agitation_rpm\": 285.259, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.194, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.352, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.19, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.683, \"viable_cell_density_million_ml\": 24.088}, {\"agitation_rpm\": 283.962, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.841, \"dissolved_oxygen_pct\": 36.23, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.441, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.098, \"our_mmol_l_h\": 9.097, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.703, \"viable_cell_density_million_ml\": 27.761}, {\"agitation_rpm\": 284.061, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 10.123, \"dissolved_oxygen_pct\": 36.683, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.401, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.051, \"our_mmol_l_h\": 9.802, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.742, \"viable_cell_density_million_ml\": 31.336}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2c590e555b977440f9f4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.157, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.561, \"dissolved_oxygen_pct\": 35.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.167, \"our_mmol_l_h\": 7.132, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.789, \"viability_pct\": 98.796, \"viable_cell_density_million_ml\": 21.108}, {\"agitation_rpm\": 285.259, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.194, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.352, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.19, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.683, \"viable_cell_density_million_ml\": 24.088}, {\"agitation_rpm\": 283.962, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.841, \"dissolved_oxygen_pct\": 36.23, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.441, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.098, \"our_mmol_l_h\": 9.097, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.703, \"viable_cell_density_million_ml\": 27.761}, {\"agitation_rpm\": 284.061, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 10.123, \"dissolved_oxygen_pct\": 36.683, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.401, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.051, \"our_mmol_l_h\": 9.802, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.742, \"viable_cell_density_million_ml\": 31.336}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3e21b6026bcee011bbda", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.157, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.561, \"dissolved_oxygen_pct\": 35.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.167, \"our_mmol_l_h\": 7.132, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.789, \"viability_pct\": 98.796, \"viable_cell_density_million_ml\": 21.108}, {\"agitation_rpm\": 285.259, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.194, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.352, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.19, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.683, \"viable_cell_density_million_ml\": 24.088}, {\"agitation_rpm\": 283.962, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.841, \"dissolved_oxygen_pct\": 36.23, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.441, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.098, \"our_mmol_l_h\": 9.097, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.703, \"viable_cell_density_million_ml\": 27.761}, {\"agitation_rpm\": 284.061, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 10.123, \"dissolved_oxygen_pct\": 36.683, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.401, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.051, \"our_mmol_l_h\": 9.802, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.742, \"viable_cell_density_million_ml\": 31.336}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-61293cd115da29694786", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.157, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.561, \"dissolved_oxygen_pct\": 35.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.167, \"our_mmol_l_h\": 7.132, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.789, \"viability_pct\": 98.796, \"viable_cell_density_million_ml\": 21.108}, {\"agitation_rpm\": 285.259, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.194, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.352, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.19, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.683, \"viable_cell_density_million_ml\": 24.088}, {\"agitation_rpm\": 283.962, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.841, \"dissolved_oxygen_pct\": 36.23, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.441, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.098, \"our_mmol_l_h\": 9.097, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.703, \"viable_cell_density_million_ml\": 27.761}, {\"agitation_rpm\": 284.061, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 10.123, \"dissolved_oxygen_pct\": 36.683, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.401, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.051, \"our_mmol_l_h\": 9.802, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.742, \"viable_cell_density_million_ml\": 31.336}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6f9554d1e5306ca0ea09", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.554, \"airflow_vvm\": 0.213, \"cer_mmol_l_h\": 3.754, \"dissolved_oxygen_pct\": 39.994, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.577, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.26, \"our_mmol_l_h\": 4.042, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.76, \"viable_cell_density_million_ml\": 11.944}, {\"agitation_rpm\": 238.694, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.624, \"dissolved_oxygen_pct\": 37.798, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.525, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.045, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.909, \"viable_cell_density_million_ml\": 14.733}, {\"agitation_rpm\": 279.607, \"airflow_vvm\": 0.243, \"cer_mmol_l_h\": 6.076, \"dissolved_oxygen_pct\": 28.315, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.365, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.19, \"our_mmol_l_h\": 6.618, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.654, \"viable_cell_density_million_ml\": 19.501}, {\"agitation_rpm\": 284.738, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.989, \"dissolved_oxygen_pct\": 18.893, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.372, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.704, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.945, \"viable_cell_density_million_ml\": 25.577}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5c41af55280767fde54a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.554, \"airflow_vvm\": 0.213, \"cer_mmol_l_h\": 3.754, \"dissolved_oxygen_pct\": 39.994, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.577, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.26, \"our_mmol_l_h\": 4.042, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.76, \"viable_cell_density_million_ml\": 11.944}, {\"agitation_rpm\": 238.694, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.624, \"dissolved_oxygen_pct\": 37.798, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.525, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.045, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.909, \"viable_cell_density_million_ml\": 14.733}, {\"agitation_rpm\": 279.607, \"airflow_vvm\": 0.243, \"cer_mmol_l_h\": 6.076, \"dissolved_oxygen_pct\": 28.315, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.365, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.19, \"our_mmol_l_h\": 6.618, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.654, \"viable_cell_density_million_ml\": 19.501}, {\"agitation_rpm\": 284.738, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.989, \"dissolved_oxygen_pct\": 18.893, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.372, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.704, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.945, \"viable_cell_density_million_ml\": 25.577}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8a87ba464f6b90a7850f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.554, \"airflow_vvm\": 0.213, \"cer_mmol_l_h\": 3.754, \"dissolved_oxygen_pct\": 39.994, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.577, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.26, \"our_mmol_l_h\": 4.042, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.76, \"viable_cell_density_million_ml\": 11.944}, {\"agitation_rpm\": 238.694, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.624, \"dissolved_oxygen_pct\": 37.798, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.525, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.045, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.909, \"viable_cell_density_million_ml\": 14.733}, {\"agitation_rpm\": 279.607, \"airflow_vvm\": 0.243, \"cer_mmol_l_h\": 6.076, \"dissolved_oxygen_pct\": 28.315, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.365, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.19, \"our_mmol_l_h\": 6.618, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.654, \"viable_cell_density_million_ml\": 19.501}, {\"agitation_rpm\": 284.738, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.989, \"dissolved_oxygen_pct\": 18.893, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.372, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.704, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.945, \"viable_cell_density_million_ml\": 25.577}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-87d101dbcdd77d1ca444", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.554, \"airflow_vvm\": 0.213, \"cer_mmol_l_h\": 3.754, \"dissolved_oxygen_pct\": 39.994, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.577, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.26, \"our_mmol_l_h\": 4.042, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.76, \"viable_cell_density_million_ml\": 11.944}, {\"agitation_rpm\": 238.694, \"airflow_vvm\": 0.244, \"cer_mmol_l_h\": 4.624, \"dissolved_oxygen_pct\": 37.798, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.525, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.045, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.909, \"viable_cell_density_million_ml\": 14.733}, {\"agitation_rpm\": 279.607, \"airflow_vvm\": 0.243, \"cer_mmol_l_h\": 6.076, \"dissolved_oxygen_pct\": 28.315, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.365, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.19, \"our_mmol_l_h\": 6.618, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.654, \"viable_cell_density_million_ml\": 19.501}, {\"agitation_rpm\": 284.738, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.989, \"dissolved_oxygen_pct\": 18.893, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.372, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.704, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.945, \"viable_cell_density_million_ml\": 25.577}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6525cd4895a261e68f0a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.08, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.031, \"dissolved_oxygen_pct\": 36.864, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.984, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.801, \"our_mmol_l_h\": 6.543, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.518, \"viable_cell_density_million_ml\": 19.253}, {\"agitation_rpm\": 285.022, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.419, \"dissolved_oxygen_pct\": 36.194, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.976, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.726, \"our_mmol_l_h\": 7.001, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.646, \"viable_cell_density_million_ml\": 20.49}, {\"agitation_rpm\": 285.656, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.892, \"dissolved_oxygen_pct\": 36.733, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.948, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.645, \"our_mmol_l_h\": 6.979, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.469, \"viable_cell_density_million_ml\": 21.617}, {\"agitation_rpm\": 284.922, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.208, \"dissolved_oxygen_pct\": 36.183, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.603, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.461, \"our_mmol_l_h\": 6.597, \"ph\": 6.982, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 96.541, \"viable_cell_density_million_ml\": 22.042}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8e066ce2354477644296", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.08, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.031, \"dissolved_oxygen_pct\": 36.864, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.984, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.801, \"our_mmol_l_h\": 6.543, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.518, \"viable_cell_density_million_ml\": 19.253}, {\"agitation_rpm\": 285.022, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.419, \"dissolved_oxygen_pct\": 36.194, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.976, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.726, \"our_mmol_l_h\": 7.001, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.646, \"viable_cell_density_million_ml\": 20.49}, {\"agitation_rpm\": 285.656, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.892, \"dissolved_oxygen_pct\": 36.733, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.948, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.645, \"our_mmol_l_h\": 6.979, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.469, \"viable_cell_density_million_ml\": 21.617}, {\"agitation_rpm\": 284.922, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.208, \"dissolved_oxygen_pct\": 36.183, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.603, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.461, \"our_mmol_l_h\": 6.597, \"ph\": 6.982, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 96.541, \"viable_cell_density_million_ml\": 22.042}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0b4169c135c24ce2c60c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.08, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.031, \"dissolved_oxygen_pct\": 36.864, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.984, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.801, \"our_mmol_l_h\": 6.543, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.518, \"viable_cell_density_million_ml\": 19.253}, {\"agitation_rpm\": 285.022, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.419, \"dissolved_oxygen_pct\": 36.194, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.976, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.726, \"our_mmol_l_h\": 7.001, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.646, \"viable_cell_density_million_ml\": 20.49}, {\"agitation_rpm\": 285.656, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.892, \"dissolved_oxygen_pct\": 36.733, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.948, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.645, \"our_mmol_l_h\": 6.979, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.469, \"viable_cell_density_million_ml\": 21.617}, {\"agitation_rpm\": 284.922, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.208, \"dissolved_oxygen_pct\": 36.183, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.603, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.461, \"our_mmol_l_h\": 6.597, \"ph\": 6.982, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 96.541, \"viable_cell_density_million_ml\": 22.042}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-86de62befa9020b93e7a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.08, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.031, \"dissolved_oxygen_pct\": 36.864, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.984, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.801, \"our_mmol_l_h\": 6.543, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.518, \"viable_cell_density_million_ml\": 19.253}, {\"agitation_rpm\": 285.022, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.419, \"dissolved_oxygen_pct\": 36.194, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.976, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.726, \"our_mmol_l_h\": 7.001, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.646, \"viable_cell_density_million_ml\": 20.49}, {\"agitation_rpm\": 285.656, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.892, \"dissolved_oxygen_pct\": 36.733, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.948, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.645, \"our_mmol_l_h\": 6.979, \"ph\": 6.979, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.469, \"viable_cell_density_million_ml\": 21.617}, {\"agitation_rpm\": 284.922, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.208, \"dissolved_oxygen_pct\": 36.183, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.603, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.461, \"our_mmol_l_h\": 6.597, \"ph\": 6.982, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 96.541, \"viable_cell_density_million_ml\": 22.042}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6e52b18a156063080314", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.524, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.19, \"dissolved_oxygen_pct\": 41.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.455, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.774, \"our_mmol_l_h\": 3.528, \"ph\": 6.973, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 10.277}, {\"agitation_rpm\": 215.995, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.919, \"dissolved_oxygen_pct\": 40.273, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.168, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.945, \"our_mmol_l_h\": 4.254, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.804, \"viability_pct\": 98.6, \"viable_cell_density_million_ml\": 12.399}, {\"agitation_rpm\": 245.708, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.866, \"dissolved_oxygen_pct\": 29.898, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.145, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.945, \"our_mmol_l_h\": 5.348, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 15.646}, {\"agitation_rpm\": 277.732, \"airflow_vvm\": 0.173, \"cer_mmol_l_h\": 6.032, \"dissolved_oxygen_pct\": 18.304, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.042, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.793, \"our_mmol_l_h\": 6.507, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.539, \"viable_cell_density_million_ml\": 19.19}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b17cd6df88d317394e18", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.524, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.19, \"dissolved_oxygen_pct\": 41.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.455, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.774, \"our_mmol_l_h\": 3.528, \"ph\": 6.973, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 10.277}, {\"agitation_rpm\": 215.995, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.919, \"dissolved_oxygen_pct\": 40.273, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.168, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.945, \"our_mmol_l_h\": 4.254, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.804, \"viability_pct\": 98.6, \"viable_cell_density_million_ml\": 12.399}, {\"agitation_rpm\": 245.708, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.866, \"dissolved_oxygen_pct\": 29.898, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.145, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.945, \"our_mmol_l_h\": 5.348, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 15.646}, {\"agitation_rpm\": 277.732, \"airflow_vvm\": 0.173, \"cer_mmol_l_h\": 6.032, \"dissolved_oxygen_pct\": 18.304, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.042, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.793, \"our_mmol_l_h\": 6.507, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.539, \"viable_cell_density_million_ml\": 19.19}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7a783777456c574233fa", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.524, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.19, \"dissolved_oxygen_pct\": 41.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.455, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.774, \"our_mmol_l_h\": 3.528, \"ph\": 6.973, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 10.277}, {\"agitation_rpm\": 215.995, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.919, \"dissolved_oxygen_pct\": 40.273, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.168, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.945, \"our_mmol_l_h\": 4.254, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.804, \"viability_pct\": 98.6, \"viable_cell_density_million_ml\": 12.399}, {\"agitation_rpm\": 245.708, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.866, \"dissolved_oxygen_pct\": 29.898, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.145, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.945, \"our_mmol_l_h\": 5.348, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 15.646}, {\"agitation_rpm\": 277.732, \"airflow_vvm\": 0.173, \"cer_mmol_l_h\": 6.032, \"dissolved_oxygen_pct\": 18.304, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.042, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.793, \"our_mmol_l_h\": 6.507, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.539, \"viable_cell_density_million_ml\": 19.19}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-36d957d4bb4d92c233e0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.524, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.19, \"dissolved_oxygen_pct\": 41.038, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.455, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.774, \"our_mmol_l_h\": 3.528, \"ph\": 6.973, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.468, \"viable_cell_density_million_ml\": 10.277}, {\"agitation_rpm\": 215.995, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.919, \"dissolved_oxygen_pct\": 40.273, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.168, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.945, \"our_mmol_l_h\": 4.254, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.804, \"viability_pct\": 98.6, \"viable_cell_density_million_ml\": 12.399}, {\"agitation_rpm\": 245.708, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.866, \"dissolved_oxygen_pct\": 29.898, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.145, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.945, \"our_mmol_l_h\": 5.348, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 15.646}, {\"agitation_rpm\": 277.732, \"airflow_vvm\": 0.173, \"cer_mmol_l_h\": 6.032, \"dissolved_oxygen_pct\": 18.304, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.042, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.793, \"our_mmol_l_h\": 6.507, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.539, \"viable_cell_density_million_ml\": 19.19}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e2c21551054b08fd3f76", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.072, \"airflow_vvm\": 0.256, \"cer_mmol_l_h\": 5.079, \"dissolved_oxygen_pct\": 38.09, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.475, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 5.545, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.834, \"viable_cell_density_million_ml\": 16.344}, {\"agitation_rpm\": 281.131, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.076, \"dissolved_oxygen_pct\": 36.661, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.376, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.203, \"our_mmol_l_h\": 6.628, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 19.373}, {\"agitation_rpm\": 285.535, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.519, \"dissolved_oxygen_pct\": 36.738, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.443, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.67, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.908, \"viable_cell_density_million_ml\": 23.676}, {\"agitation_rpm\": 285.325, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.228, \"dissolved_oxygen_pct\": 36.495, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.27, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 8.819, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.769, \"viable_cell_density_million_ml\": 28.414}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3ccc3bf8e3605521ad23", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.072, \"airflow_vvm\": 0.256, \"cer_mmol_l_h\": 5.079, \"dissolved_oxygen_pct\": 38.09, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.475, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 5.545, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.834, \"viable_cell_density_million_ml\": 16.344}, {\"agitation_rpm\": 281.131, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.076, \"dissolved_oxygen_pct\": 36.661, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.376, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.203, \"our_mmol_l_h\": 6.628, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 19.373}, {\"agitation_rpm\": 285.535, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.519, \"dissolved_oxygen_pct\": 36.738, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.443, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.67, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.908, \"viable_cell_density_million_ml\": 23.676}, {\"agitation_rpm\": 285.325, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.228, \"dissolved_oxygen_pct\": 36.495, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.27, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 8.819, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.769, \"viable_cell_density_million_ml\": 28.414}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1dfb4cf9384d369c092c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.072, \"airflow_vvm\": 0.256, \"cer_mmol_l_h\": 5.079, \"dissolved_oxygen_pct\": 38.09, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.475, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 5.545, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.834, \"viable_cell_density_million_ml\": 16.344}, {\"agitation_rpm\": 281.131, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.076, \"dissolved_oxygen_pct\": 36.661, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.376, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.203, \"our_mmol_l_h\": 6.628, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 19.373}, {\"agitation_rpm\": 285.535, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.519, \"dissolved_oxygen_pct\": 36.738, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.443, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.67, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.908, \"viable_cell_density_million_ml\": 23.676}, {\"agitation_rpm\": 285.325, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.228, \"dissolved_oxygen_pct\": 36.495, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.27, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 8.819, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.769, \"viable_cell_density_million_ml\": 28.414}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-544bb8a5ccbe6acb709a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.072, \"airflow_vvm\": 0.256, \"cer_mmol_l_h\": 5.079, \"dissolved_oxygen_pct\": 38.09, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.475, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 5.545, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.834, \"viable_cell_density_million_ml\": 16.344}, {\"agitation_rpm\": 281.131, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.076, \"dissolved_oxygen_pct\": 36.661, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.376, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.203, \"our_mmol_l_h\": 6.628, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.736, \"viable_cell_density_million_ml\": 19.373}, {\"agitation_rpm\": 285.535, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.519, \"dissolved_oxygen_pct\": 36.738, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.443, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 7.67, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.908, \"viable_cell_density_million_ml\": 23.676}, {\"agitation_rpm\": 285.325, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.228, \"dissolved_oxygen_pct\": 36.495, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.27, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 8.819, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.769, \"viable_cell_density_million_ml\": 28.414}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-24f7f2239cbae7ecbc9a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.778, \"airflow_vvm\": 0.258, \"cer_mmol_l_h\": 5.089, \"dissolved_oxygen_pct\": 38.021, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.477, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.25, \"our_mmol_l_h\": 5.554, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.682, \"viable_cell_density_million_ml\": 16.279}, {\"agitation_rpm\": 280.016, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 36.615, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.366, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.191, \"our_mmol_l_h\": 6.588, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.93, \"viable_cell_density_million_ml\": 19.479}, {\"agitation_rpm\": 284.157, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 7.514, \"dissolved_oxygen_pct\": 28.669, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.327, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.167, \"our_mmol_l_h\": 8.182, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.745, \"viable_cell_density_million_ml\": 24.131}, {\"agitation_rpm\": 285.883, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 9.216, \"dissolved_oxygen_pct\": 17.621, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.475, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.09, \"our_mmol_l_h\": 9.99, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 29.478}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9ac42463f17bd6bc4951", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.778, \"airflow_vvm\": 0.258, \"cer_mmol_l_h\": 5.089, \"dissolved_oxygen_pct\": 38.021, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.477, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.25, \"our_mmol_l_h\": 5.554, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.682, \"viable_cell_density_million_ml\": 16.279}, {\"agitation_rpm\": 280.016, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 36.615, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.366, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.191, \"our_mmol_l_h\": 6.588, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.93, \"viable_cell_density_million_ml\": 19.479}, {\"agitation_rpm\": 284.157, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 7.514, \"dissolved_oxygen_pct\": 28.669, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.327, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.167, \"our_mmol_l_h\": 8.182, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.745, \"viable_cell_density_million_ml\": 24.131}, {\"agitation_rpm\": 285.883, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 9.216, \"dissolved_oxygen_pct\": 17.621, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.475, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.09, \"our_mmol_l_h\": 9.99, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 29.478}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d1ca2339781263aaa034", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.778, \"airflow_vvm\": 0.258, \"cer_mmol_l_h\": 5.089, \"dissolved_oxygen_pct\": 38.021, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.477, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.25, \"our_mmol_l_h\": 5.554, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.682, \"viable_cell_density_million_ml\": 16.279}, {\"agitation_rpm\": 280.016, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 36.615, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.366, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.191, \"our_mmol_l_h\": 6.588, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.93, \"viable_cell_density_million_ml\": 19.479}, {\"agitation_rpm\": 284.157, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 7.514, \"dissolved_oxygen_pct\": 28.669, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.327, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.167, \"our_mmol_l_h\": 8.182, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.745, \"viable_cell_density_million_ml\": 24.131}, {\"agitation_rpm\": 285.883, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 9.216, \"dissolved_oxygen_pct\": 17.621, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.475, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.09, \"our_mmol_l_h\": 9.99, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 29.478}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-274eafd1fb7236f042bd", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.778, \"airflow_vvm\": 0.258, \"cer_mmol_l_h\": 5.089, \"dissolved_oxygen_pct\": 38.021, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.477, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.25, \"our_mmol_l_h\": 5.554, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.682, \"viable_cell_density_million_ml\": 16.279}, {\"agitation_rpm\": 280.016, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 36.615, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.366, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.191, \"our_mmol_l_h\": 6.588, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.808, \"viability_pct\": 98.93, \"viable_cell_density_million_ml\": 19.479}, {\"agitation_rpm\": 284.157, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 7.514, \"dissolved_oxygen_pct\": 28.669, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.327, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.167, \"our_mmol_l_h\": 8.182, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.745, \"viable_cell_density_million_ml\": 24.131}, {\"agitation_rpm\": 285.883, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 9.216, \"dissolved_oxygen_pct\": 17.621, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.475, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.09, \"our_mmol_l_h\": 9.99, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 29.478}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-192f8f4345efa5ed74a3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.211, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 36.387, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.031, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.82, \"our_mmol_l_h\": 6.507, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.456, \"viable_cell_density_million_ml\": 19.151}, {\"agitation_rpm\": 285.267, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.424, \"dissolved_oxygen_pct\": 36.111, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.993, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.741, \"our_mmol_l_h\": 7.004, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.553, \"viable_cell_density_million_ml\": 20.553}, {\"agitation_rpm\": 286.172, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.898, \"dissolved_oxygen_pct\": 36.165, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.934, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.605, \"our_mmol_l_h\": 6.949, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.409, \"viable_cell_density_million_ml\": 21.604}, {\"agitation_rpm\": 285.754, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.166, \"dissolved_oxygen_pct\": 36.941, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.616, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.483, \"our_mmol_l_h\": 6.636, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 96.715, \"viable_cell_density_million_ml\": 21.972}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e202de6f582fe96fee48", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.211, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 36.387, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.031, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.82, \"our_mmol_l_h\": 6.507, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.456, \"viable_cell_density_million_ml\": 19.151}, {\"agitation_rpm\": 285.267, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.424, \"dissolved_oxygen_pct\": 36.111, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.993, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.741, \"our_mmol_l_h\": 7.004, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.553, \"viable_cell_density_million_ml\": 20.553}, {\"agitation_rpm\": 286.172, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.898, \"dissolved_oxygen_pct\": 36.165, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.934, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.605, \"our_mmol_l_h\": 6.949, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.409, \"viable_cell_density_million_ml\": 21.604}, {\"agitation_rpm\": 285.754, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.166, \"dissolved_oxygen_pct\": 36.941, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.616, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.483, \"our_mmol_l_h\": 6.636, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 96.715, \"viable_cell_density_million_ml\": 21.972}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-631bc8ba182a49c026c3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.211, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 36.387, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.031, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.82, \"our_mmol_l_h\": 6.507, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.456, \"viable_cell_density_million_ml\": 19.151}, {\"agitation_rpm\": 285.267, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.424, \"dissolved_oxygen_pct\": 36.111, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.993, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.741, \"our_mmol_l_h\": 7.004, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.553, \"viable_cell_density_million_ml\": 20.553}, {\"agitation_rpm\": 286.172, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.898, \"dissolved_oxygen_pct\": 36.165, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.934, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.605, \"our_mmol_l_h\": 6.949, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.409, \"viable_cell_density_million_ml\": 21.604}, {\"agitation_rpm\": 285.754, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.166, \"dissolved_oxygen_pct\": 36.941, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.616, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.483, \"our_mmol_l_h\": 6.636, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 96.715, \"viable_cell_density_million_ml\": 21.972}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c4ba6f8f946244f9fa4a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.211, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 36.387, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.031, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.82, \"our_mmol_l_h\": 6.507, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.456, \"viable_cell_density_million_ml\": 19.151}, {\"agitation_rpm\": 285.267, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.424, \"dissolved_oxygen_pct\": 36.111, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.993, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.741, \"our_mmol_l_h\": 7.004, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.553, \"viable_cell_density_million_ml\": 20.553}, {\"agitation_rpm\": 286.172, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.898, \"dissolved_oxygen_pct\": 36.165, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.934, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.605, \"our_mmol_l_h\": 6.949, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.409, \"viable_cell_density_million_ml\": 21.604}, {\"agitation_rpm\": 285.754, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.166, \"dissolved_oxygen_pct\": 36.941, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.616, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.483, \"our_mmol_l_h\": 6.636, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 96.715, \"viable_cell_density_million_ml\": 21.972}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3f3d5d7fe0329d6639e2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.692, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.212, \"dissolved_oxygen_pct\": 41.118, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.463, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.733, \"our_mmol_l_h\": 3.501, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 10.284}, {\"agitation_rpm\": 217.509, \"airflow_vvm\": 0.219, \"cer_mmol_l_h\": 3.879, \"dissolved_oxygen_pct\": 39.698, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.184, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.93, \"our_mmol_l_h\": 4.247, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.473, \"viable_cell_density_million_ml\": 12.418}, {\"agitation_rpm\": 246.04, \"airflow_vvm\": 0.199, \"cer_mmol_l_h\": 4.884, \"dissolved_oxygen_pct\": 29.972, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.109, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.946, \"our_mmol_l_h\": 5.322, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.464, \"viable_cell_density_million_ml\": 15.577}, {\"agitation_rpm\": 277.31, \"airflow_vvm\": 0.171, \"cer_mmol_l_h\": 6.029, \"dissolved_oxygen_pct\": 18.364, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.006, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 6.509, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.353, \"viable_cell_density_million_ml\": 19.152}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-23a09a43ea486919f484", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.692, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.212, \"dissolved_oxygen_pct\": 41.118, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.463, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.733, \"our_mmol_l_h\": 3.501, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 10.284}, {\"agitation_rpm\": 217.509, \"airflow_vvm\": 0.219, \"cer_mmol_l_h\": 3.879, \"dissolved_oxygen_pct\": 39.698, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.184, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.93, \"our_mmol_l_h\": 4.247, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.473, \"viable_cell_density_million_ml\": 12.418}, {\"agitation_rpm\": 246.04, \"airflow_vvm\": 0.199, \"cer_mmol_l_h\": 4.884, \"dissolved_oxygen_pct\": 29.972, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.109, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.946, \"our_mmol_l_h\": 5.322, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.464, \"viable_cell_density_million_ml\": 15.577}, {\"agitation_rpm\": 277.31, \"airflow_vvm\": 0.171, \"cer_mmol_l_h\": 6.029, \"dissolved_oxygen_pct\": 18.364, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.006, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 6.509, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.353, \"viable_cell_density_million_ml\": 19.152}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e315baa364b9a2d85688", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.692, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.212, \"dissolved_oxygen_pct\": 41.118, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.463, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.733, \"our_mmol_l_h\": 3.501, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 10.284}, {\"agitation_rpm\": 217.509, \"airflow_vvm\": 0.219, \"cer_mmol_l_h\": 3.879, \"dissolved_oxygen_pct\": 39.698, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.184, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.93, \"our_mmol_l_h\": 4.247, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.473, \"viable_cell_density_million_ml\": 12.418}, {\"agitation_rpm\": 246.04, \"airflow_vvm\": 0.199, \"cer_mmol_l_h\": 4.884, \"dissolved_oxygen_pct\": 29.972, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.109, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.946, \"our_mmol_l_h\": 5.322, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.464, \"viable_cell_density_million_ml\": 15.577}, {\"agitation_rpm\": 277.31, \"airflow_vvm\": 0.171, \"cer_mmol_l_h\": 6.029, \"dissolved_oxygen_pct\": 18.364, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.006, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 6.509, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.353, \"viable_cell_density_million_ml\": 19.152}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e5146f5b916ea71fadbc", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.692, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.212, \"dissolved_oxygen_pct\": 41.118, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.463, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.733, \"our_mmol_l_h\": 3.501, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 10.284}, {\"agitation_rpm\": 217.509, \"airflow_vvm\": 0.219, \"cer_mmol_l_h\": 3.879, \"dissolved_oxygen_pct\": 39.698, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.184, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.93, \"our_mmol_l_h\": 4.247, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.473, \"viable_cell_density_million_ml\": 12.418}, {\"agitation_rpm\": 246.04, \"airflow_vvm\": 0.199, \"cer_mmol_l_h\": 4.884, \"dissolved_oxygen_pct\": 29.972, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.109, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.946, \"our_mmol_l_h\": 5.322, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.464, \"viable_cell_density_million_ml\": 15.577}, {\"agitation_rpm\": 277.31, \"airflow_vvm\": 0.171, \"cer_mmol_l_h\": 6.029, \"dissolved_oxygen_pct\": 18.364, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 4.006, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 6.509, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.353, \"viable_cell_density_million_ml\": 19.152}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7e6a5ea66dc670f85848", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.43, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.585, \"dissolved_oxygen_pct\": 35.886, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.336, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.178, \"our_mmol_l_h\": 7.137, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.859, \"viable_cell_density_million_ml\": 20.986}, {\"agitation_rpm\": 285.717, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.524, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.325, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 8.224, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 24.178}, {\"agitation_rpm\": 284.053, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.803, \"dissolved_oxygen_pct\": 36.725, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.473, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.089, \"our_mmol_l_h\": 9.085, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.794, \"viable_cell_density_million_ml\": 27.828}, {\"agitation_rpm\": 284.736, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.164, \"dissolved_oxygen_pct\": 36.389, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.37, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.032, \"our_mmol_l_h\": 9.823, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.821, \"viable_cell_density_million_ml\": 31.49}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-56011208d89f2e2f9a1c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.43, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.585, \"dissolved_oxygen_pct\": 35.886, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.336, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.178, \"our_mmol_l_h\": 7.137, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.859, \"viable_cell_density_million_ml\": 20.986}, {\"agitation_rpm\": 285.717, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.524, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.325, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 8.224, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 24.178}, {\"agitation_rpm\": 284.053, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.803, \"dissolved_oxygen_pct\": 36.725, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.473, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.089, \"our_mmol_l_h\": 9.085, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.794, \"viable_cell_density_million_ml\": 27.828}, {\"agitation_rpm\": 284.736, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.164, \"dissolved_oxygen_pct\": 36.389, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.37, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.032, \"our_mmol_l_h\": 9.823, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.821, \"viable_cell_density_million_ml\": 31.49}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ab205b31c1ebe9716560", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.43, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.585, \"dissolved_oxygen_pct\": 35.886, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.336, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.178, \"our_mmol_l_h\": 7.137, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.859, \"viable_cell_density_million_ml\": 20.986}, {\"agitation_rpm\": 285.717, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.524, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.325, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 8.224, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 24.178}, {\"agitation_rpm\": 284.053, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.803, \"dissolved_oxygen_pct\": 36.725, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.473, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.089, \"our_mmol_l_h\": 9.085, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.794, \"viable_cell_density_million_ml\": 27.828}, {\"agitation_rpm\": 284.736, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.164, \"dissolved_oxygen_pct\": 36.389, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.37, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.032, \"our_mmol_l_h\": 9.823, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.821, \"viable_cell_density_million_ml\": 31.49}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f39738541aec5b9a8fc8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.43, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.585, \"dissolved_oxygen_pct\": 35.886, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.336, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.178, \"our_mmol_l_h\": 7.137, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.859, \"viable_cell_density_million_ml\": 20.986}, {\"agitation_rpm\": 285.717, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.524, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.325, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 8.224, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 24.178}, {\"agitation_rpm\": 284.053, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.803, \"dissolved_oxygen_pct\": 36.725, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.473, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.089, \"our_mmol_l_h\": 9.085, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.794, \"viable_cell_density_million_ml\": 27.828}, {\"agitation_rpm\": 284.736, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.164, \"dissolved_oxygen_pct\": 36.389, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.37, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.032, \"our_mmol_l_h\": 9.823, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.821, \"viable_cell_density_million_ml\": 31.49}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-600feaa38a7bba0c138f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 283.908, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.974, \"dissolved_oxygen_pct\": 36.39, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.384, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.141, \"our_mmol_l_h\": 8.667, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.723, \"viable_cell_density_million_ml\": 25.547}, {\"agitation_rpm\": 285.589, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.816, \"dissolved_oxygen_pct\": 36.395, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.379, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.114, \"our_mmol_l_h\": 9.622, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.734, \"viable_cell_density_million_ml\": 28.289}, {\"agitation_rpm\": 284.716, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.856, \"dissolved_oxygen_pct\": 28.412, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.569, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.048, \"our_mmol_l_h\": 10.727, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.86, \"viable_cell_density_million_ml\": 31.531}, {\"agitation_rpm\": 286.059, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 10.822, \"dissolved_oxygen_pct\": 18.308, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.692, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.985, \"our_mmol_l_h\": 11.768, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.886, \"viable_cell_density_million_ml\": 34.488}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-53ba02e3442647e88cec", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 283.908, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.974, \"dissolved_oxygen_pct\": 36.39, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.384, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.141, \"our_mmol_l_h\": 8.667, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.723, \"viable_cell_density_million_ml\": 25.547}, {\"agitation_rpm\": 285.589, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.816, \"dissolved_oxygen_pct\": 36.395, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.379, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.114, \"our_mmol_l_h\": 9.622, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.734, \"viable_cell_density_million_ml\": 28.289}, {\"agitation_rpm\": 284.716, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.856, \"dissolved_oxygen_pct\": 28.412, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.569, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.048, \"our_mmol_l_h\": 10.727, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.86, \"viable_cell_density_million_ml\": 31.531}, {\"agitation_rpm\": 286.059, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 10.822, \"dissolved_oxygen_pct\": 18.308, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.692, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.985, \"our_mmol_l_h\": 11.768, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.886, \"viable_cell_density_million_ml\": 34.488}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1898ee34a99cd431bb2e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 283.908, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.974, \"dissolved_oxygen_pct\": 36.39, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.384, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.141, \"our_mmol_l_h\": 8.667, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.723, \"viable_cell_density_million_ml\": 25.547}, {\"agitation_rpm\": 285.589, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.816, \"dissolved_oxygen_pct\": 36.395, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.379, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.114, \"our_mmol_l_h\": 9.622, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.734, \"viable_cell_density_million_ml\": 28.289}, {\"agitation_rpm\": 284.716, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.856, \"dissolved_oxygen_pct\": 28.412, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.569, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.048, \"our_mmol_l_h\": 10.727, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.86, \"viable_cell_density_million_ml\": 31.531}, {\"agitation_rpm\": 286.059, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 10.822, \"dissolved_oxygen_pct\": 18.308, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.692, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.985, \"our_mmol_l_h\": 11.768, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.886, \"viable_cell_density_million_ml\": 34.488}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d2de25efa3d627828e85", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 283.908, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.974, \"dissolved_oxygen_pct\": 36.39, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.384, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.141, \"our_mmol_l_h\": 8.667, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.723, \"viable_cell_density_million_ml\": 25.547}, {\"agitation_rpm\": 285.589, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.816, \"dissolved_oxygen_pct\": 36.395, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.379, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.114, \"our_mmol_l_h\": 9.622, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.734, \"viable_cell_density_million_ml\": 28.289}, {\"agitation_rpm\": 284.716, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.856, \"dissolved_oxygen_pct\": 28.412, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.569, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.048, \"our_mmol_l_h\": 10.727, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.86, \"viable_cell_density_million_ml\": 31.531}, {\"agitation_rpm\": 286.059, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 10.822, \"dissolved_oxygen_pct\": 18.308, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.692, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.985, \"our_mmol_l_h\": 11.768, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.886, \"viable_cell_density_million_ml\": 34.488}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5a096c660f729e4962cd", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.57, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.219, \"dissolved_oxygen_pct\": 37.332, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.075, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.894, \"our_mmol_l_h\": 5.627, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.598, \"viable_cell_density_million_ml\": 16.623}, {\"agitation_rpm\": 270.664, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.748, \"dissolved_oxygen_pct\": 35.997, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.837, \"our_mmol_l_h\": 6.286, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.503, \"viable_cell_density_million_ml\": 18.352}, {\"agitation_rpm\": 285.948, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.43, \"dissolved_oxygen_pct\": 36.073, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.02, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.725, \"our_mmol_l_h\": 6.45, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.633, \"viable_cell_density_million_ml\": 20.052}, {\"agitation_rpm\": 284.805, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.019, \"dissolved_oxygen_pct\": 36.112, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.634, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.586, \"our_mmol_l_h\": 6.426, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.062, \"viable_cell_density_million_ml\": 21.348}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-73b5bfb3dce9cc722793", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.57, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.219, \"dissolved_oxygen_pct\": 37.332, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.075, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.894, \"our_mmol_l_h\": 5.627, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.598, \"viable_cell_density_million_ml\": 16.623}, {\"agitation_rpm\": 270.664, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.748, \"dissolved_oxygen_pct\": 35.997, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.837, \"our_mmol_l_h\": 6.286, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.503, \"viable_cell_density_million_ml\": 18.352}, {\"agitation_rpm\": 285.948, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.43, \"dissolved_oxygen_pct\": 36.073, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.02, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.725, \"our_mmol_l_h\": 6.45, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.633, \"viable_cell_density_million_ml\": 20.052}, {\"agitation_rpm\": 284.805, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.019, \"dissolved_oxygen_pct\": 36.112, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.634, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.586, \"our_mmol_l_h\": 6.426, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.062, \"viable_cell_density_million_ml\": 21.348}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-93b7d026c86c60b723ac", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.57, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.219, \"dissolved_oxygen_pct\": 37.332, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.075, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.894, \"our_mmol_l_h\": 5.627, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.598, \"viable_cell_density_million_ml\": 16.623}, {\"agitation_rpm\": 270.664, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.748, \"dissolved_oxygen_pct\": 35.997, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.837, \"our_mmol_l_h\": 6.286, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.503, \"viable_cell_density_million_ml\": 18.352}, {\"agitation_rpm\": 285.948, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.43, \"dissolved_oxygen_pct\": 36.073, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.02, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.725, \"our_mmol_l_h\": 6.45, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.633, \"viable_cell_density_million_ml\": 20.052}, {\"agitation_rpm\": 284.805, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.019, \"dissolved_oxygen_pct\": 36.112, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.634, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.586, \"our_mmol_l_h\": 6.426, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.062, \"viable_cell_density_million_ml\": 21.348}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1356210c244e71e6abf0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.57, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.219, \"dissolved_oxygen_pct\": 37.332, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.075, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.894, \"our_mmol_l_h\": 5.627, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.805, \"viability_pct\": 98.598, \"viable_cell_density_million_ml\": 16.623}, {\"agitation_rpm\": 270.664, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.748, \"dissolved_oxygen_pct\": 35.997, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.837, \"our_mmol_l_h\": 6.286, \"ph\": 6.961, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.503, \"viable_cell_density_million_ml\": 18.352}, {\"agitation_rpm\": 285.948, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.43, \"dissolved_oxygen_pct\": 36.073, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.02, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.725, \"our_mmol_l_h\": 6.45, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.633, \"viable_cell_density_million_ml\": 20.052}, {\"agitation_rpm\": 284.805, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.019, \"dissolved_oxygen_pct\": 36.112, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.634, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.586, \"our_mmol_l_h\": 6.426, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.062, \"viable_cell_density_million_ml\": 21.348}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9035616ea240716a9993", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.391, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.212, \"dissolved_oxygen_pct\": 37.231, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.086, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.924, \"our_mmol_l_h\": 5.652, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.491, \"viable_cell_density_million_ml\": 16.703}, {\"agitation_rpm\": 271.187, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 36.35, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.056, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.826, \"our_mmol_l_h\": 6.276, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 18.42}, {\"agitation_rpm\": 285.832, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 28.154, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.945, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.72, \"our_mmol_l_h\": 6.962, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 20.527}, {\"agitation_rpm\": 283.828, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.018, \"dissolved_oxygen_pct\": 18.398, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.893, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.591, \"our_mmol_l_h\": 7.597, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 97.967, \"viable_cell_density_million_ml\": 22.349}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c40c9701f6a6b64e74fe", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.391, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.212, \"dissolved_oxygen_pct\": 37.231, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.086, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.924, \"our_mmol_l_h\": 5.652, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.491, \"viable_cell_density_million_ml\": 16.703}, {\"agitation_rpm\": 271.187, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 36.35, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.056, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.826, \"our_mmol_l_h\": 6.276, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 18.42}, {\"agitation_rpm\": 285.832, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 28.154, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.945, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.72, \"our_mmol_l_h\": 6.962, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 20.527}, {\"agitation_rpm\": 283.828, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.018, \"dissolved_oxygen_pct\": 18.398, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.893, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.591, \"our_mmol_l_h\": 7.597, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 97.967, \"viable_cell_density_million_ml\": 22.349}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4594345f0b5660090efc", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.391, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.212, \"dissolved_oxygen_pct\": 37.231, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.086, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.924, \"our_mmol_l_h\": 5.652, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.491, \"viable_cell_density_million_ml\": 16.703}, {\"agitation_rpm\": 271.187, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 36.35, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.056, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.826, \"our_mmol_l_h\": 6.276, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 18.42}, {\"agitation_rpm\": 285.832, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 28.154, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.945, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.72, \"our_mmol_l_h\": 6.962, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 20.527}, {\"agitation_rpm\": 283.828, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.018, \"dissolved_oxygen_pct\": 18.398, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.893, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.591, \"our_mmol_l_h\": 7.597, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 97.967, \"viable_cell_density_million_ml\": 22.349}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-10c6ae6db2381651a526", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.391, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.212, \"dissolved_oxygen_pct\": 37.231, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.086, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.924, \"our_mmol_l_h\": 5.652, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.491, \"viable_cell_density_million_ml\": 16.703}, {\"agitation_rpm\": 271.187, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 36.35, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.056, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.826, \"our_mmol_l_h\": 6.276, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 18.42}, {\"agitation_rpm\": 285.832, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 28.154, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.945, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.72, \"our_mmol_l_h\": 6.962, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.636, \"viable_cell_density_million_ml\": 20.527}, {\"agitation_rpm\": 283.828, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.018, \"dissolved_oxygen_pct\": 18.398, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.893, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.591, \"our_mmol_l_h\": 7.597, \"ph\": 6.985, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 97.967, \"viable_cell_density_million_ml\": 22.349}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-066e6d4efcdf2dd8b837", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.956, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.578, \"dissolved_oxygen_pct\": 36.503, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.358, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.166, \"our_mmol_l_h\": 7.145, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 21.063}, {\"agitation_rpm\": 284.722, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.467, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.357, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 8.203, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.666, \"viable_cell_density_million_ml\": 24.052}, {\"agitation_rpm\": 285.651, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.839, \"dissolved_oxygen_pct\": 36.161, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.448, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.093, \"our_mmol_l_h\": 9.067, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.843, \"viable_cell_density_million_ml\": 27.807}, {\"agitation_rpm\": 284.268, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 10.13, \"dissolved_oxygen_pct\": 36.634, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.367, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.063, \"our_mmol_l_h\": 9.797, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.758, \"viable_cell_density_million_ml\": 31.367}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4902ac951f749c2b8980", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.956, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.578, \"dissolved_oxygen_pct\": 36.503, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.358, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.166, \"our_mmol_l_h\": 7.145, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 21.063}, {\"agitation_rpm\": 284.722, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.467, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.357, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 8.203, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.666, \"viable_cell_density_million_ml\": 24.052}, {\"agitation_rpm\": 285.651, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.839, \"dissolved_oxygen_pct\": 36.161, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.448, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.093, \"our_mmol_l_h\": 9.067, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.843, \"viable_cell_density_million_ml\": 27.807}, {\"agitation_rpm\": 284.268, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 10.13, \"dissolved_oxygen_pct\": 36.634, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.367, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.063, \"our_mmol_l_h\": 9.797, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.758, \"viable_cell_density_million_ml\": 31.367}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5c7791b98d2ec665cda8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.956, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.578, \"dissolved_oxygen_pct\": 36.503, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.358, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.166, \"our_mmol_l_h\": 7.145, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 21.063}, {\"agitation_rpm\": 284.722, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.467, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.357, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 8.203, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.666, \"viable_cell_density_million_ml\": 24.052}, {\"agitation_rpm\": 285.651, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.839, \"dissolved_oxygen_pct\": 36.161, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.448, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.093, \"our_mmol_l_h\": 9.067, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.843, \"viable_cell_density_million_ml\": 27.807}, {\"agitation_rpm\": 284.268, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 10.13, \"dissolved_oxygen_pct\": 36.634, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.367, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.063, \"our_mmol_l_h\": 9.797, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.758, \"viable_cell_density_million_ml\": 31.367}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-68cdc07e79aacadb2c38", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.956, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.578, \"dissolved_oxygen_pct\": 36.503, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.358, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.166, \"our_mmol_l_h\": 7.145, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 21.063}, {\"agitation_rpm\": 284.722, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.523, \"dissolved_oxygen_pct\": 36.467, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.357, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.163, \"our_mmol_l_h\": 8.203, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.666, \"viable_cell_density_million_ml\": 24.052}, {\"agitation_rpm\": 285.651, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.839, \"dissolved_oxygen_pct\": 36.161, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.448, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.093, \"our_mmol_l_h\": 9.067, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.843, \"viable_cell_density_million_ml\": 27.807}, {\"agitation_rpm\": 284.268, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 10.13, \"dissolved_oxygen_pct\": 36.634, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.367, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.063, \"our_mmol_l_h\": 9.797, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.758, \"viable_cell_density_million_ml\": 31.367}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6e4268a7638b7b9aab76", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.867, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.76, \"dissolved_oxygen_pct\": 40.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.616, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.254, \"our_mmol_l_h\": 4.044, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.801, \"viability_pct\": 98.849, \"viable_cell_density_million_ml\": 11.943}, {\"agitation_rpm\": 238.718, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.612, \"dissolved_oxygen_pct\": 38.634, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.516, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.244, \"our_mmol_l_h\": 5.026, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 14.784}, {\"agitation_rpm\": 279.442, \"airflow_vvm\": 0.243, \"cer_mmol_l_h\": 6.084, \"dissolved_oxygen_pct\": 29.052, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.391, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.219, \"our_mmol_l_h\": 6.602, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.692, \"viable_cell_density_million_ml\": 19.449}, {\"agitation_rpm\": 285.112, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.996, \"dissolved_oxygen_pct\": 18.591, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.392, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.126, \"our_mmol_l_h\": 8.697, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.726, \"viable_cell_density_million_ml\": 25.525}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d3d1e1b6bde04dd8f860", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.867, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.76, \"dissolved_oxygen_pct\": 40.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.616, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.254, \"our_mmol_l_h\": 4.044, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.801, \"viability_pct\": 98.849, \"viable_cell_density_million_ml\": 11.943}, {\"agitation_rpm\": 238.718, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.612, \"dissolved_oxygen_pct\": 38.634, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.516, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.244, \"our_mmol_l_h\": 5.026, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 14.784}, {\"agitation_rpm\": 279.442, \"airflow_vvm\": 0.243, \"cer_mmol_l_h\": 6.084, \"dissolved_oxygen_pct\": 29.052, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.391, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.219, \"our_mmol_l_h\": 6.602, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.692, \"viable_cell_density_million_ml\": 19.449}, {\"agitation_rpm\": 285.112, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.996, \"dissolved_oxygen_pct\": 18.591, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.392, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.126, \"our_mmol_l_h\": 8.697, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.726, \"viable_cell_density_million_ml\": 25.525}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-733cbbef2c2b8b0532ac", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.867, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.76, \"dissolved_oxygen_pct\": 40.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.616, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.254, \"our_mmol_l_h\": 4.044, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.801, \"viability_pct\": 98.849, \"viable_cell_density_million_ml\": 11.943}, {\"agitation_rpm\": 238.718, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.612, \"dissolved_oxygen_pct\": 38.634, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.516, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.244, \"our_mmol_l_h\": 5.026, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 14.784}, {\"agitation_rpm\": 279.442, \"airflow_vvm\": 0.243, \"cer_mmol_l_h\": 6.084, \"dissolved_oxygen_pct\": 29.052, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.391, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.219, \"our_mmol_l_h\": 6.602, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.692, \"viable_cell_density_million_ml\": 19.449}, {\"agitation_rpm\": 285.112, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.996, \"dissolved_oxygen_pct\": 18.591, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.392, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.126, \"our_mmol_l_h\": 8.697, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.726, \"viable_cell_density_million_ml\": 25.525}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ada95d45f00160893737", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.867, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.76, \"dissolved_oxygen_pct\": 40.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.616, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.254, \"our_mmol_l_h\": 4.044, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.801, \"viability_pct\": 98.849, \"viable_cell_density_million_ml\": 11.943}, {\"agitation_rpm\": 238.718, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.612, \"dissolved_oxygen_pct\": 38.634, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.516, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.244, \"our_mmol_l_h\": 5.026, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.684, \"viable_cell_density_million_ml\": 14.784}, {\"agitation_rpm\": 279.442, \"airflow_vvm\": 0.243, \"cer_mmol_l_h\": 6.084, \"dissolved_oxygen_pct\": 29.052, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.391, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.219, \"our_mmol_l_h\": 6.602, \"ph\": 7.004, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.692, \"viable_cell_density_million_ml\": 19.449}, {\"agitation_rpm\": 285.112, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 7.996, \"dissolved_oxygen_pct\": 18.591, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.392, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.126, \"our_mmol_l_h\": 8.697, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.726, \"viable_cell_density_million_ml\": 25.525}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ff4abe2c2f566e62b93f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.409, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.225, \"dissolved_oxygen_pct\": 40.834, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.496, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.737, \"our_mmol_l_h\": 3.49, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.49, \"viable_cell_density_million_ml\": 10.317}, {\"agitation_rpm\": 218.004, \"airflow_vvm\": 0.215, \"cer_mmol_l_h\": 3.907, \"dissolved_oxygen_pct\": 39.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.189, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.913, \"our_mmol_l_h\": 4.212, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.482, \"viable_cell_density_million_ml\": 12.501}, {\"agitation_rpm\": 246.731, \"airflow_vvm\": 0.253, \"cer_mmol_l_h\": 4.883, \"dissolved_oxygen_pct\": 38.278, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.169, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.957, \"our_mmol_l_h\": 4.775, \"ph\": 6.965, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.501, \"viable_cell_density_million_ml\": 15.285}, {\"agitation_rpm\": 277.94, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 35.777, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.785, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.824, \"our_mmol_l_h\": 5.332, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.384, \"viable_cell_density_million_ml\": 18.135}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c252d75a9c5dea743ba2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.409, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.225, \"dissolved_oxygen_pct\": 40.834, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.496, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.737, \"our_mmol_l_h\": 3.49, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.49, \"viable_cell_density_million_ml\": 10.317}, {\"agitation_rpm\": 218.004, \"airflow_vvm\": 0.215, \"cer_mmol_l_h\": 3.907, \"dissolved_oxygen_pct\": 39.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.189, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.913, \"our_mmol_l_h\": 4.212, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.482, \"viable_cell_density_million_ml\": 12.501}, {\"agitation_rpm\": 246.731, \"airflow_vvm\": 0.253, \"cer_mmol_l_h\": 4.883, \"dissolved_oxygen_pct\": 38.278, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.169, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.957, \"our_mmol_l_h\": 4.775, \"ph\": 6.965, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.501, \"viable_cell_density_million_ml\": 15.285}, {\"agitation_rpm\": 277.94, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 35.777, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.785, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.824, \"our_mmol_l_h\": 5.332, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.384, \"viable_cell_density_million_ml\": 18.135}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9ee01aeb920b8105e912", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.409, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.225, \"dissolved_oxygen_pct\": 40.834, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.496, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.737, \"our_mmol_l_h\": 3.49, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.49, \"viable_cell_density_million_ml\": 10.317}, {\"agitation_rpm\": 218.004, \"airflow_vvm\": 0.215, \"cer_mmol_l_h\": 3.907, \"dissolved_oxygen_pct\": 39.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.189, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.913, \"our_mmol_l_h\": 4.212, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.482, \"viable_cell_density_million_ml\": 12.501}, {\"agitation_rpm\": 246.731, \"airflow_vvm\": 0.253, \"cer_mmol_l_h\": 4.883, \"dissolved_oxygen_pct\": 38.278, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.169, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.957, \"our_mmol_l_h\": 4.775, \"ph\": 6.965, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.501, \"viable_cell_density_million_ml\": 15.285}, {\"agitation_rpm\": 277.94, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 35.777, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.785, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.824, \"our_mmol_l_h\": 5.332, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.384, \"viable_cell_density_million_ml\": 18.135}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fd9b7caa81232db37317", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.409, \"airflow_vvm\": 0.192, \"cer_mmol_l_h\": 3.225, \"dissolved_oxygen_pct\": 40.834, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.496, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.737, \"our_mmol_l_h\": 3.49, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.49, \"viable_cell_density_million_ml\": 10.317}, {\"agitation_rpm\": 218.004, \"airflow_vvm\": 0.215, \"cer_mmol_l_h\": 3.907, \"dissolved_oxygen_pct\": 39.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.189, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.913, \"our_mmol_l_h\": 4.212, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.482, \"viable_cell_density_million_ml\": 12.501}, {\"agitation_rpm\": 246.731, \"airflow_vvm\": 0.253, \"cer_mmol_l_h\": 4.883, \"dissolved_oxygen_pct\": 38.278, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.169, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.957, \"our_mmol_l_h\": 4.775, \"ph\": 6.965, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.501, \"viable_cell_density_million_ml\": 15.285}, {\"agitation_rpm\": 277.94, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.035, \"dissolved_oxygen_pct\": 35.777, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.785, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.824, \"our_mmol_l_h\": 5.332, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.384, \"viable_cell_density_million_ml\": 18.135}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-13368f105fa0cf6c0593", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 276.964, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.994, \"dissolved_oxygen_pct\": 35.916, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.012, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.795, \"our_mmol_l_h\": 6.514, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.36, \"viable_cell_density_million_ml\": 19.211}, {\"agitation_rpm\": 285.257, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.413, \"dissolved_oxygen_pct\": 36.813, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.989, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.716, \"our_mmol_l_h\": 6.981, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 20.572}, {\"agitation_rpm\": 284.134, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.897, \"dissolved_oxygen_pct\": 28.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.856, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.636, \"our_mmol_l_h\": 7.484, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.357, \"viable_cell_density_million_ml\": 21.978}, {\"agitation_rpm\": 283.987, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.197, \"dissolved_oxygen_pct\": 17.779, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.822, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.485, \"our_mmol_l_h\": 7.809, \"ph\": 6.981, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 96.618, \"viable_cell_density_million_ml\": 23.002}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-93b21b571b987fa5de13", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 276.964, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.994, \"dissolved_oxygen_pct\": 35.916, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.012, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.795, \"our_mmol_l_h\": 6.514, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.36, \"viable_cell_density_million_ml\": 19.211}, {\"agitation_rpm\": 285.257, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.413, \"dissolved_oxygen_pct\": 36.813, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.989, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.716, \"our_mmol_l_h\": 6.981, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 20.572}, {\"agitation_rpm\": 284.134, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.897, \"dissolved_oxygen_pct\": 28.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.856, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.636, \"our_mmol_l_h\": 7.484, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.357, \"viable_cell_density_million_ml\": 21.978}, {\"agitation_rpm\": 283.987, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.197, \"dissolved_oxygen_pct\": 17.779, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.822, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.485, \"our_mmol_l_h\": 7.809, \"ph\": 6.981, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 96.618, \"viable_cell_density_million_ml\": 23.002}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d2312d6dae2c82824267", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 276.964, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.994, \"dissolved_oxygen_pct\": 35.916, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.012, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.795, \"our_mmol_l_h\": 6.514, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.36, \"viable_cell_density_million_ml\": 19.211}, {\"agitation_rpm\": 285.257, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.413, \"dissolved_oxygen_pct\": 36.813, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.989, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.716, \"our_mmol_l_h\": 6.981, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 20.572}, {\"agitation_rpm\": 284.134, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.897, \"dissolved_oxygen_pct\": 28.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.856, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.636, \"our_mmol_l_h\": 7.484, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.357, \"viable_cell_density_million_ml\": 21.978}, {\"agitation_rpm\": 283.987, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.197, \"dissolved_oxygen_pct\": 17.779, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.822, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.485, \"our_mmol_l_h\": 7.809, \"ph\": 6.981, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 96.618, \"viable_cell_density_million_ml\": 23.002}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-003d49dd25fae683be9d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 276.964, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.994, \"dissolved_oxygen_pct\": 35.916, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.012, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.795, \"our_mmol_l_h\": 6.514, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.36, \"viable_cell_density_million_ml\": 19.211}, {\"agitation_rpm\": 285.257, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.413, \"dissolved_oxygen_pct\": 36.813, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.989, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.716, \"our_mmol_l_h\": 6.981, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 20.572}, {\"agitation_rpm\": 284.134, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.897, \"dissolved_oxygen_pct\": 28.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.856, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.636, \"our_mmol_l_h\": 7.484, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.357, \"viable_cell_density_million_ml\": 21.978}, {\"agitation_rpm\": 283.987, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.197, \"dissolved_oxygen_pct\": 17.779, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.822, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.485, \"our_mmol_l_h\": 7.809, \"ph\": 6.981, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 96.618, \"viable_cell_density_million_ml\": 23.002}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5a4b326b8c117ee38ad0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.738, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.017, \"dissolved_oxygen_pct\": 36.793, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.376, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.147, \"our_mmol_l_h\": 8.703, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.775, \"viable_cell_density_million_ml\": 25.619}, {\"agitation_rpm\": 285.416, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.851, \"dissolved_oxygen_pct\": 35.847, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.419, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.095, \"our_mmol_l_h\": 9.604, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 28.226}, {\"agitation_rpm\": 285.528, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.844, \"dissolved_oxygen_pct\": 36.935, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.618, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.052, \"our_mmol_l_h\": 10.22, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.783, \"viable_cell_density_million_ml\": 31.106}, {\"agitation_rpm\": 285.665, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 10.814, \"dissolved_oxygen_pct\": 35.87, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.502, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.015, \"our_mmol_l_h\": 10.537, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.732, \"viable_cell_density_million_ml\": 33.576}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2e921f9f358754e92718", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.738, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.017, \"dissolved_oxygen_pct\": 36.793, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.376, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.147, \"our_mmol_l_h\": 8.703, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.775, \"viable_cell_density_million_ml\": 25.619}, {\"agitation_rpm\": 285.416, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.851, \"dissolved_oxygen_pct\": 35.847, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.419, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.095, \"our_mmol_l_h\": 9.604, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 28.226}, {\"agitation_rpm\": 285.528, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.844, \"dissolved_oxygen_pct\": 36.935, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.618, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.052, \"our_mmol_l_h\": 10.22, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.783, \"viable_cell_density_million_ml\": 31.106}, {\"agitation_rpm\": 285.665, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 10.814, \"dissolved_oxygen_pct\": 35.87, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.502, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.015, \"our_mmol_l_h\": 10.537, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.732, \"viable_cell_density_million_ml\": 33.576}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-15358ccf90c220703899", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.738, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.017, \"dissolved_oxygen_pct\": 36.793, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.376, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.147, \"our_mmol_l_h\": 8.703, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.775, \"viable_cell_density_million_ml\": 25.619}, {\"agitation_rpm\": 285.416, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.851, \"dissolved_oxygen_pct\": 35.847, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.419, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.095, \"our_mmol_l_h\": 9.604, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 28.226}, {\"agitation_rpm\": 285.528, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.844, \"dissolved_oxygen_pct\": 36.935, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.618, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.052, \"our_mmol_l_h\": 10.22, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.783, \"viable_cell_density_million_ml\": 31.106}, {\"agitation_rpm\": 285.665, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 10.814, \"dissolved_oxygen_pct\": 35.87, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.502, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.015, \"our_mmol_l_h\": 10.537, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.732, \"viable_cell_density_million_ml\": 33.576}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-35dde5f391b7a55dc523", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.738, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.017, \"dissolved_oxygen_pct\": 36.793, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.376, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.147, \"our_mmol_l_h\": 8.703, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.775, \"viable_cell_density_million_ml\": 25.619}, {\"agitation_rpm\": 285.416, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.851, \"dissolved_oxygen_pct\": 35.847, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.419, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.095, \"our_mmol_l_h\": 9.604, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 28.226}, {\"agitation_rpm\": 285.528, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 9.844, \"dissolved_oxygen_pct\": 36.935, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.618, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.052, \"our_mmol_l_h\": 10.22, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.783, \"viable_cell_density_million_ml\": 31.106}, {\"agitation_rpm\": 285.665, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 10.814, \"dissolved_oxygen_pct\": 35.87, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.502, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.015, \"our_mmol_l_h\": 10.537, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.732, \"viable_cell_density_million_ml\": 33.576}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-426088df11a0c8beaf7f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.447, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.007, \"dissolved_oxygen_pct\": 36.438, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.371, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.663, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 25.638}, {\"agitation_rpm\": 284.118, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.86, \"dissolved_oxygen_pct\": 35.899, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.415, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.116, \"our_mmol_l_h\": 9.625, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.688, \"viable_cell_density_million_ml\": 28.309}, {\"agitation_rpm\": 285.692, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.855, \"dissolved_oxygen_pct\": 28.569, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.495, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.056, \"our_mmol_l_h\": 10.719, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.831, \"viable_cell_density_million_ml\": 31.474}, {\"agitation_rpm\": 286.157, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.796, \"dissolved_oxygen_pct\": 18.203, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.726, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.005, \"our_mmol_l_h\": 11.717, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.765, \"viable_cell_density_million_ml\": 34.586}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a1ed362754c3798eb551", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.447, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.007, \"dissolved_oxygen_pct\": 36.438, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.371, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.663, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 25.638}, {\"agitation_rpm\": 284.118, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.86, \"dissolved_oxygen_pct\": 35.899, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.415, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.116, \"our_mmol_l_h\": 9.625, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.688, \"viable_cell_density_million_ml\": 28.309}, {\"agitation_rpm\": 285.692, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.855, \"dissolved_oxygen_pct\": 28.569, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.495, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.056, \"our_mmol_l_h\": 10.719, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.831, \"viable_cell_density_million_ml\": 31.474}, {\"agitation_rpm\": 286.157, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.796, \"dissolved_oxygen_pct\": 18.203, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.726, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.005, \"our_mmol_l_h\": 11.717, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.765, \"viable_cell_density_million_ml\": 34.586}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d4688d6bae36b4c4db93", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.447, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.007, \"dissolved_oxygen_pct\": 36.438, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.371, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.663, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 25.638}, {\"agitation_rpm\": 284.118, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.86, \"dissolved_oxygen_pct\": 35.899, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.415, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.116, \"our_mmol_l_h\": 9.625, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.688, \"viable_cell_density_million_ml\": 28.309}, {\"agitation_rpm\": 285.692, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.855, \"dissolved_oxygen_pct\": 28.569, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.495, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.056, \"our_mmol_l_h\": 10.719, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.831, \"viable_cell_density_million_ml\": 31.474}, {\"agitation_rpm\": 286.157, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.796, \"dissolved_oxygen_pct\": 18.203, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.726, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.005, \"our_mmol_l_h\": 11.717, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.765, \"viable_cell_density_million_ml\": 34.586}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2ce8606057284d931447", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.447, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.007, \"dissolved_oxygen_pct\": 36.438, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.371, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.663, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 25.638}, {\"agitation_rpm\": 284.118, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.86, \"dissolved_oxygen_pct\": 35.899, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.415, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.116, \"our_mmol_l_h\": 9.625, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.688, \"viable_cell_density_million_ml\": 28.309}, {\"agitation_rpm\": 285.692, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 9.855, \"dissolved_oxygen_pct\": 28.569, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.495, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.056, \"our_mmol_l_h\": 10.719, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.831, \"viable_cell_density_million_ml\": 31.474}, {\"agitation_rpm\": 286.157, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.796, \"dissolved_oxygen_pct\": 18.203, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.726, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.005, \"our_mmol_l_h\": 11.717, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.765, \"viable_cell_density_million_ml\": 34.586}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a31fb3f6d22f65e4b05e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.955, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.209, \"dissolved_oxygen_pct\": 36.681, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.903, \"our_mmol_l_h\": 5.631, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.47, \"viable_cell_density_million_ml\": 16.65}, {\"agitation_rpm\": 271.039, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.778, \"dissolved_oxygen_pct\": 36.842, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.022, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 6.29, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.567, \"viable_cell_density_million_ml\": 18.423}, {\"agitation_rpm\": 286.181, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.442, \"dissolved_oxygen_pct\": 36.829, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.016, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.713, \"our_mmol_l_h\": 6.481, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.4, \"viable_cell_density_million_ml\": 20.122}, {\"agitation_rpm\": 284.694, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.019, \"dissolved_oxygen_pct\": 36.401, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.638, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.57, \"our_mmol_l_h\": 6.387, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.139, \"viable_cell_density_million_ml\": 21.346}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2f24b9cad69429300a15", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.955, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.209, \"dissolved_oxygen_pct\": 36.681, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.903, \"our_mmol_l_h\": 5.631, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.47, \"viable_cell_density_million_ml\": 16.65}, {\"agitation_rpm\": 271.039, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.778, \"dissolved_oxygen_pct\": 36.842, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.022, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 6.29, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.567, \"viable_cell_density_million_ml\": 18.423}, {\"agitation_rpm\": 286.181, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.442, \"dissolved_oxygen_pct\": 36.829, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.016, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.713, \"our_mmol_l_h\": 6.481, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.4, \"viable_cell_density_million_ml\": 20.122}, {\"agitation_rpm\": 284.694, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.019, \"dissolved_oxygen_pct\": 36.401, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.638, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.57, \"our_mmol_l_h\": 6.387, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.139, \"viable_cell_density_million_ml\": 21.346}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fdb412a88c53181d5e0a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.955, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.209, \"dissolved_oxygen_pct\": 36.681, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.903, \"our_mmol_l_h\": 5.631, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.47, \"viable_cell_density_million_ml\": 16.65}, {\"agitation_rpm\": 271.039, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.778, \"dissolved_oxygen_pct\": 36.842, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.022, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 6.29, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.567, \"viable_cell_density_million_ml\": 18.423}, {\"agitation_rpm\": 286.181, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.442, \"dissolved_oxygen_pct\": 36.829, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.016, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.713, \"our_mmol_l_h\": 6.481, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.4, \"viable_cell_density_million_ml\": 20.122}, {\"agitation_rpm\": 284.694, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.019, \"dissolved_oxygen_pct\": 36.401, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.638, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.57, \"our_mmol_l_h\": 6.387, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.139, \"viable_cell_density_million_ml\": 21.346}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-49a64b29da38c9dd579c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.955, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.209, \"dissolved_oxygen_pct\": 36.681, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.903, \"our_mmol_l_h\": 5.631, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.47, \"viable_cell_density_million_ml\": 16.65}, {\"agitation_rpm\": 271.039, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.778, \"dissolved_oxygen_pct\": 36.842, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.022, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 6.29, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.8, \"viability_pct\": 98.567, \"viable_cell_density_million_ml\": 18.423}, {\"agitation_rpm\": 286.181, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.442, \"dissolved_oxygen_pct\": 36.829, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.016, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.713, \"our_mmol_l_h\": 6.481, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.4, \"viable_cell_density_million_ml\": 20.122}, {\"agitation_rpm\": 284.694, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.019, \"dissolved_oxygen_pct\": 36.401, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.638, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.57, \"our_mmol_l_h\": 6.387, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.139, \"viable_cell_density_million_ml\": 21.346}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-182c82c557a409932295", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.417, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.2, \"dissolved_oxygen_pct\": 36.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.086, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.931, \"our_mmol_l_h\": 5.675, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.357, \"viable_cell_density_million_ml\": 16.668}, {\"agitation_rpm\": 271.876, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.758, \"dissolved_oxygen_pct\": 36.389, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.044, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.846, \"our_mmol_l_h\": 6.245, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.36, \"viable_cell_density_million_ml\": 18.393}, {\"agitation_rpm\": 285.674, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 6.406, \"dissolved_oxygen_pct\": 28.687, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.942, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.753, \"our_mmol_l_h\": 7.013, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.616, \"viable_cell_density_million_ml\": 20.598}, {\"agitation_rpm\": 285.293, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 6.975, \"dissolved_oxygen_pct\": 17.968, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.854, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.572, \"our_mmol_l_h\": 7.618, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 97.939, \"viable_cell_density_million_ml\": 22.365}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5470e4ed7a00231d48fd", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.417, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.2, \"dissolved_oxygen_pct\": 36.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.086, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.931, \"our_mmol_l_h\": 5.675, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.357, \"viable_cell_density_million_ml\": 16.668}, {\"agitation_rpm\": 271.876, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.758, \"dissolved_oxygen_pct\": 36.389, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.044, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.846, \"our_mmol_l_h\": 6.245, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.36, \"viable_cell_density_million_ml\": 18.393}, {\"agitation_rpm\": 285.674, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 6.406, \"dissolved_oxygen_pct\": 28.687, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.942, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.753, \"our_mmol_l_h\": 7.013, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.616, \"viable_cell_density_million_ml\": 20.598}, {\"agitation_rpm\": 285.293, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 6.975, \"dissolved_oxygen_pct\": 17.968, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.854, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.572, \"our_mmol_l_h\": 7.618, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 97.939, \"viable_cell_density_million_ml\": 22.365}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4d424f05f823d214bfd6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.417, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.2, \"dissolved_oxygen_pct\": 36.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.086, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.931, \"our_mmol_l_h\": 5.675, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.357, \"viable_cell_density_million_ml\": 16.668}, {\"agitation_rpm\": 271.876, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.758, \"dissolved_oxygen_pct\": 36.389, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.044, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.846, \"our_mmol_l_h\": 6.245, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.36, \"viable_cell_density_million_ml\": 18.393}, {\"agitation_rpm\": 285.674, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 6.406, \"dissolved_oxygen_pct\": 28.687, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.942, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.753, \"our_mmol_l_h\": 7.013, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.616, \"viable_cell_density_million_ml\": 20.598}, {\"agitation_rpm\": 285.293, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 6.975, \"dissolved_oxygen_pct\": 17.968, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.854, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.572, \"our_mmol_l_h\": 7.618, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 97.939, \"viable_cell_density_million_ml\": 22.365}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-37ca67bc94b657ca8d60", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.417, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.2, \"dissolved_oxygen_pct\": 36.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.086, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.931, \"our_mmol_l_h\": 5.675, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.357, \"viable_cell_density_million_ml\": 16.668}, {\"agitation_rpm\": 271.876, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.758, \"dissolved_oxygen_pct\": 36.389, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.044, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.846, \"our_mmol_l_h\": 6.245, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.36, \"viable_cell_density_million_ml\": 18.393}, {\"agitation_rpm\": 285.674, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 6.406, \"dissolved_oxygen_pct\": 28.687, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.942, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.753, \"our_mmol_l_h\": 7.013, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.616, \"viable_cell_density_million_ml\": 20.598}, {\"agitation_rpm\": 285.293, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 6.975, \"dissolved_oxygen_pct\": 17.968, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.854, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.572, \"our_mmol_l_h\": 7.618, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 97.939, \"viable_cell_density_million_ml\": 22.365}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-88b19b0bc5372b9fa49b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.527, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.994, \"dissolved_oxygen_pct\": 36.014, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.695, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 98.706, \"viable_cell_density_million_ml\": 25.636}, {\"agitation_rpm\": 285.157, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.854, \"dissolved_oxygen_pct\": 36.514, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.39, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.091, \"our_mmol_l_h\": 9.618, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 28.308}, {\"agitation_rpm\": 284.488, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.854, \"dissolved_oxygen_pct\": 35.629, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.605, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.057, \"our_mmol_l_h\": 10.201, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.67, \"viable_cell_density_million_ml\": 31.141}, {\"agitation_rpm\": 284.222, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.795, \"dissolved_oxygen_pct\": 36.146, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.541, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.008, \"our_mmol_l_h\": 10.541, \"ph\": 7.013, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.804, \"viable_cell_density_million_ml\": 33.591}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bee9c34d22692b6c7d78", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.527, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.994, \"dissolved_oxygen_pct\": 36.014, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.695, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 98.706, \"viable_cell_density_million_ml\": 25.636}, {\"agitation_rpm\": 285.157, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.854, \"dissolved_oxygen_pct\": 36.514, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.39, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.091, \"our_mmol_l_h\": 9.618, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 28.308}, {\"agitation_rpm\": 284.488, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.854, \"dissolved_oxygen_pct\": 35.629, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.605, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.057, \"our_mmol_l_h\": 10.201, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.67, \"viable_cell_density_million_ml\": 31.141}, {\"agitation_rpm\": 284.222, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.795, \"dissolved_oxygen_pct\": 36.146, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.541, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.008, \"our_mmol_l_h\": 10.541, \"ph\": 7.013, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.804, \"viable_cell_density_million_ml\": 33.591}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ca98e7c295a733601efc", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.527, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.994, \"dissolved_oxygen_pct\": 36.014, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.695, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 98.706, \"viable_cell_density_million_ml\": 25.636}, {\"agitation_rpm\": 285.157, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.854, \"dissolved_oxygen_pct\": 36.514, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.39, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.091, \"our_mmol_l_h\": 9.618, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 28.308}, {\"agitation_rpm\": 284.488, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.854, \"dissolved_oxygen_pct\": 35.629, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.605, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.057, \"our_mmol_l_h\": 10.201, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.67, \"viable_cell_density_million_ml\": 31.141}, {\"agitation_rpm\": 284.222, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.795, \"dissolved_oxygen_pct\": 36.146, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.541, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.008, \"our_mmol_l_h\": 10.541, \"ph\": 7.013, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.804, \"viable_cell_density_million_ml\": 33.591}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-520e869b528085b29dc4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.527, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.994, \"dissolved_oxygen_pct\": 36.014, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.127, \"our_mmol_l_h\": 8.695, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.775, \"viability_pct\": 98.706, \"viable_cell_density_million_ml\": 25.636}, {\"agitation_rpm\": 285.157, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.854, \"dissolved_oxygen_pct\": 36.514, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.39, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.091, \"our_mmol_l_h\": 9.618, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 28.308}, {\"agitation_rpm\": 284.488, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.854, \"dissolved_oxygen_pct\": 35.629, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.605, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.057, \"our_mmol_l_h\": 10.201, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.67, \"viable_cell_density_million_ml\": 31.141}, {\"agitation_rpm\": 284.222, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 10.795, \"dissolved_oxygen_pct\": 36.146, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.541, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.008, \"our_mmol_l_h\": 10.541, \"ph\": 7.013, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.804, \"viable_cell_density_million_ml\": 33.591}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2dd8901082c0ea040614", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.678, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.578, \"dissolved_oxygen_pct\": 36.151, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.383, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.194, \"our_mmol_l_h\": 7.148, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.915, \"viable_cell_density_million_ml\": 20.955}, {\"agitation_rpm\": 286.023, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.532, \"dissolved_oxygen_pct\": 36.757, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.323, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.166, \"our_mmol_l_h\": 8.222, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 24.068}, {\"agitation_rpm\": 285.362, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 8.836, \"dissolved_oxygen_pct\": 28.407, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.445, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.088, \"our_mmol_l_h\": 9.599, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 98.79, \"viable_cell_density_million_ml\": 28.27}, {\"agitation_rpm\": 285.015, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.162, \"dissolved_oxygen_pct\": 18.591, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.576, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.026, \"our_mmol_l_h\": 10.996, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 32.345}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e3637cca7d4d84e62b51", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.678, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.578, \"dissolved_oxygen_pct\": 36.151, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.383, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.194, \"our_mmol_l_h\": 7.148, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.915, \"viable_cell_density_million_ml\": 20.955}, {\"agitation_rpm\": 286.023, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.532, \"dissolved_oxygen_pct\": 36.757, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.323, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.166, \"our_mmol_l_h\": 8.222, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 24.068}, {\"agitation_rpm\": 285.362, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 8.836, \"dissolved_oxygen_pct\": 28.407, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.445, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.088, \"our_mmol_l_h\": 9.599, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 98.79, \"viable_cell_density_million_ml\": 28.27}, {\"agitation_rpm\": 285.015, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.162, \"dissolved_oxygen_pct\": 18.591, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.576, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.026, \"our_mmol_l_h\": 10.996, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 32.345}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-17dcaa1f5726bc7f7854", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.678, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.578, \"dissolved_oxygen_pct\": 36.151, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.383, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.194, \"our_mmol_l_h\": 7.148, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.915, \"viable_cell_density_million_ml\": 20.955}, {\"agitation_rpm\": 286.023, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.532, \"dissolved_oxygen_pct\": 36.757, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.323, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.166, \"our_mmol_l_h\": 8.222, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 24.068}, {\"agitation_rpm\": 285.362, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 8.836, \"dissolved_oxygen_pct\": 28.407, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.445, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.088, \"our_mmol_l_h\": 9.599, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 98.79, \"viable_cell_density_million_ml\": 28.27}, {\"agitation_rpm\": 285.015, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.162, \"dissolved_oxygen_pct\": 18.591, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.576, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.026, \"our_mmol_l_h\": 10.996, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 32.345}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-52edd935138a4b9ccb80", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.678, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.578, \"dissolved_oxygen_pct\": 36.151, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.383, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.194, \"our_mmol_l_h\": 7.148, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.915, \"viable_cell_density_million_ml\": 20.955}, {\"agitation_rpm\": 286.023, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.532, \"dissolved_oxygen_pct\": 36.757, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.323, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.166, \"our_mmol_l_h\": 8.222, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.73, \"viable_cell_density_million_ml\": 24.068}, {\"agitation_rpm\": 285.362, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 8.836, \"dissolved_oxygen_pct\": 28.407, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.445, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.088, \"our_mmol_l_h\": 9.599, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.804, \"viability_pct\": 98.79, \"viable_cell_density_million_ml\": 28.27}, {\"agitation_rpm\": 285.015, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.162, \"dissolved_oxygen_pct\": 18.591, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.576, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.026, \"our_mmol_l_h\": 10.996, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 32.345}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6aa2767e4bfbd47759c6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.2, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.245, \"dissolved_oxygen_pct\": 39.399, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.141, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.004, \"our_mmol_l_h\": 4.575, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.365, \"viable_cell_density_million_ml\": 13.561}, {\"agitation_rpm\": 246.2, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.883, \"dissolved_oxygen_pct\": 38.026, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.109, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.939, \"our_mmol_l_h\": 5.292, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.532, \"viable_cell_density_million_ml\": 15.644}, {\"agitation_rpm\": 271.846, \"airflow_vvm\": 0.282, \"cer_mmol_l_h\": 5.759, \"dissolved_oxygen_pct\": 36.321, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.085, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 5.744, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.627, \"viable_cell_density_million_ml\": 18.016}, {\"agitation_rpm\": 283.971, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.603, \"dissolved_oxygen_pct\": 36.072, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.703, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.695, \"our_mmol_l_h\": 5.959, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.634, \"viable_cell_density_million_ml\": 20.116}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d093b0c26399d5d8f0bb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.2, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.245, \"dissolved_oxygen_pct\": 39.399, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.141, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.004, \"our_mmol_l_h\": 4.575, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.365, \"viable_cell_density_million_ml\": 13.561}, {\"agitation_rpm\": 246.2, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.883, \"dissolved_oxygen_pct\": 38.026, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.109, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.939, \"our_mmol_l_h\": 5.292, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.532, \"viable_cell_density_million_ml\": 15.644}, {\"agitation_rpm\": 271.846, \"airflow_vvm\": 0.282, \"cer_mmol_l_h\": 5.759, \"dissolved_oxygen_pct\": 36.321, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.085, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 5.744, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.627, \"viable_cell_density_million_ml\": 18.016}, {\"agitation_rpm\": 283.971, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.603, \"dissolved_oxygen_pct\": 36.072, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.703, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.695, \"our_mmol_l_h\": 5.959, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.634, \"viable_cell_density_million_ml\": 20.116}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ee73232168412027df34", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.2, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.245, \"dissolved_oxygen_pct\": 39.399, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.141, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.004, \"our_mmol_l_h\": 4.575, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.365, \"viable_cell_density_million_ml\": 13.561}, {\"agitation_rpm\": 246.2, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.883, \"dissolved_oxygen_pct\": 38.026, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.109, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.939, \"our_mmol_l_h\": 5.292, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.532, \"viable_cell_density_million_ml\": 15.644}, {\"agitation_rpm\": 271.846, \"airflow_vvm\": 0.282, \"cer_mmol_l_h\": 5.759, \"dissolved_oxygen_pct\": 36.321, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.085, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 5.744, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.627, \"viable_cell_density_million_ml\": 18.016}, {\"agitation_rpm\": 283.971, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.603, \"dissolved_oxygen_pct\": 36.072, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.703, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.695, \"our_mmol_l_h\": 5.959, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.634, \"viable_cell_density_million_ml\": 20.116}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d21113ba73a185aff7ea", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.2, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.245, \"dissolved_oxygen_pct\": 39.399, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.141, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.004, \"our_mmol_l_h\": 4.575, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.365, \"viable_cell_density_million_ml\": 13.561}, {\"agitation_rpm\": 246.2, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.883, \"dissolved_oxygen_pct\": 38.026, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.109, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.939, \"our_mmol_l_h\": 5.292, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.532, \"viable_cell_density_million_ml\": 15.644}, {\"agitation_rpm\": 271.846, \"airflow_vvm\": 0.282, \"cer_mmol_l_h\": 5.759, \"dissolved_oxygen_pct\": 36.321, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.085, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.833, \"our_mmol_l_h\": 5.744, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.627, \"viable_cell_density_million_ml\": 18.016}, {\"agitation_rpm\": 283.971, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.603, \"dissolved_oxygen_pct\": 36.072, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.703, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.695, \"our_mmol_l_h\": 5.959, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.781, \"viability_pct\": 98.634, \"viable_cell_density_million_ml\": 20.116}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fd32ed699b6cf62cfe95", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.168, \"airflow_vvm\": 0.264, \"cer_mmol_l_h\": 5.225, \"dissolved_oxygen_pct\": 36.631, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.073, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.9, \"our_mmol_l_h\": 5.645, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 16.701}, {\"agitation_rpm\": 269.914, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.781, \"dissolved_oxygen_pct\": 36.546, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.001, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.235, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.38, \"viable_cell_density_million_ml\": 18.344}, {\"agitation_rpm\": 284.324, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.444, \"dissolved_oxygen_pct\": 28.946, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.98, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.715, \"our_mmol_l_h\": 6.961, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.601, \"viable_cell_density_million_ml\": 20.537}, {\"agitation_rpm\": 285.384, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.026, \"dissolved_oxygen_pct\": 17.684, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.892, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.602, \"our_mmol_l_h\": 7.586, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.044, \"viable_cell_density_million_ml\": 22.328}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3b77389f8c49f6bfbeae", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.168, \"airflow_vvm\": 0.264, \"cer_mmol_l_h\": 5.225, \"dissolved_oxygen_pct\": 36.631, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.073, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.9, \"our_mmol_l_h\": 5.645, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 16.701}, {\"agitation_rpm\": 269.914, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.781, \"dissolved_oxygen_pct\": 36.546, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.001, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.235, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.38, \"viable_cell_density_million_ml\": 18.344}, {\"agitation_rpm\": 284.324, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.444, \"dissolved_oxygen_pct\": 28.946, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.98, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.715, \"our_mmol_l_h\": 6.961, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.601, \"viable_cell_density_million_ml\": 20.537}, {\"agitation_rpm\": 285.384, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.026, \"dissolved_oxygen_pct\": 17.684, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.892, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.602, \"our_mmol_l_h\": 7.586, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.044, \"viable_cell_density_million_ml\": 22.328}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-989cdfc26f761b4c1a34", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.168, \"airflow_vvm\": 0.264, \"cer_mmol_l_h\": 5.225, \"dissolved_oxygen_pct\": 36.631, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.073, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.9, \"our_mmol_l_h\": 5.645, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 16.701}, {\"agitation_rpm\": 269.914, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.781, \"dissolved_oxygen_pct\": 36.546, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.001, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.235, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.38, \"viable_cell_density_million_ml\": 18.344}, {\"agitation_rpm\": 284.324, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.444, \"dissolved_oxygen_pct\": 28.946, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.98, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.715, \"our_mmol_l_h\": 6.961, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.601, \"viable_cell_density_million_ml\": 20.537}, {\"agitation_rpm\": 285.384, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.026, \"dissolved_oxygen_pct\": 17.684, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.892, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.602, \"our_mmol_l_h\": 7.586, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.044, \"viable_cell_density_million_ml\": 22.328}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-650191c0aa0e5833d625", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.168, \"airflow_vvm\": 0.264, \"cer_mmol_l_h\": 5.225, \"dissolved_oxygen_pct\": 36.631, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.073, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.9, \"our_mmol_l_h\": 5.645, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.791, \"viability_pct\": 98.58, \"viable_cell_density_million_ml\": 16.701}, {\"agitation_rpm\": 269.914, \"airflow_vvm\": 0.28, \"cer_mmol_l_h\": 5.781, \"dissolved_oxygen_pct\": 36.546, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.001, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.822, \"our_mmol_l_h\": 6.235, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.38, \"viable_cell_density_million_ml\": 18.344}, {\"agitation_rpm\": 284.324, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.444, \"dissolved_oxygen_pct\": 28.946, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.98, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.715, \"our_mmol_l_h\": 6.961, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.601, \"viable_cell_density_million_ml\": 20.537}, {\"agitation_rpm\": 285.384, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.026, \"dissolved_oxygen_pct\": 17.684, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.892, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.602, \"our_mmol_l_h\": 7.586, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.044, \"viable_cell_density_million_ml\": 22.328}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0b9da8970b12147505a4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.366, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.552, \"dissolved_oxygen_pct\": 36.039, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.389, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.197, \"our_mmol_l_h\": 7.169, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.764, \"viable_cell_density_million_ml\": 20.971}, {\"agitation_rpm\": 285.482, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.518, \"dissolved_oxygen_pct\": 35.658, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.318, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.198, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.827, \"viable_cell_density_million_ml\": 24.182}, {\"agitation_rpm\": 284.132, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.813, \"dissolved_oxygen_pct\": 35.703, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.452, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.09, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 27.733}, {\"agitation_rpm\": 285.314, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.147, \"dissolved_oxygen_pct\": 36.878, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.363, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.03, \"our_mmol_l_h\": 9.843, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.709, \"viable_cell_density_million_ml\": 31.427}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c6a12e259777fbe03fbd", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.366, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.552, \"dissolved_oxygen_pct\": 36.039, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.389, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.197, \"our_mmol_l_h\": 7.169, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.764, \"viable_cell_density_million_ml\": 20.971}, {\"agitation_rpm\": 285.482, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.518, \"dissolved_oxygen_pct\": 35.658, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.318, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.198, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.827, \"viable_cell_density_million_ml\": 24.182}, {\"agitation_rpm\": 284.132, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.813, \"dissolved_oxygen_pct\": 35.703, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.452, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.09, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 27.733}, {\"agitation_rpm\": 285.314, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.147, \"dissolved_oxygen_pct\": 36.878, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.363, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.03, \"our_mmol_l_h\": 9.843, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.709, \"viable_cell_density_million_ml\": 31.427}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-57e433db3cee4b9538ce", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.366, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.552, \"dissolved_oxygen_pct\": 36.039, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.389, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.197, \"our_mmol_l_h\": 7.169, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.764, \"viable_cell_density_million_ml\": 20.971}, {\"agitation_rpm\": 285.482, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.518, \"dissolved_oxygen_pct\": 35.658, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.318, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.198, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.827, \"viable_cell_density_million_ml\": 24.182}, {\"agitation_rpm\": 284.132, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.813, \"dissolved_oxygen_pct\": 35.703, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.452, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.09, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 27.733}, {\"agitation_rpm\": 285.314, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.147, \"dissolved_oxygen_pct\": 36.878, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.363, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.03, \"our_mmol_l_h\": 9.843, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.709, \"viable_cell_density_million_ml\": 31.427}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cb19d6c11b47ad989bac", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.366, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.552, \"dissolved_oxygen_pct\": 36.039, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.389, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.197, \"our_mmol_l_h\": 7.169, \"ph\": 6.997, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.764, \"viable_cell_density_million_ml\": 20.971}, {\"agitation_rpm\": 285.482, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.518, \"dissolved_oxygen_pct\": 35.658, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.318, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.133, \"our_mmol_l_h\": 8.198, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.827, \"viable_cell_density_million_ml\": 24.182}, {\"agitation_rpm\": 284.132, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 8.813, \"dissolved_oxygen_pct\": 35.703, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.452, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.09, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.728, \"viable_cell_density_million_ml\": 27.733}, {\"agitation_rpm\": 285.314, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 10.147, \"dissolved_oxygen_pct\": 36.878, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.363, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.03, \"our_mmol_l_h\": 9.843, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.709, \"viable_cell_density_million_ml\": 31.427}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1c10f3ee21f0f9b3e70a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.012, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.988, \"dissolved_oxygen_pct\": 35.768, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.712, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.658, \"viable_cell_density_million_ml\": 25.623}, {\"agitation_rpm\": 286.174, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.802, \"dissolved_oxygen_pct\": 36.572, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.441, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.108, \"our_mmol_l_h\": 9.576, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.86, \"viable_cell_density_million_ml\": 28.235}, {\"agitation_rpm\": 284.756, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.878, \"dissolved_oxygen_pct\": 28.687, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.541, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.074, \"our_mmol_l_h\": 10.744, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.904, \"viable_cell_density_million_ml\": 31.577}, {\"agitation_rpm\": 285.301, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.782, \"dissolved_oxygen_pct\": 18.145, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.688, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.996, \"our_mmol_l_h\": 11.769, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.843, \"viable_cell_density_million_ml\": 34.532}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ff328ae74c9cafcf6366", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.012, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.988, \"dissolved_oxygen_pct\": 35.768, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.712, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.658, \"viable_cell_density_million_ml\": 25.623}, {\"agitation_rpm\": 286.174, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.802, \"dissolved_oxygen_pct\": 36.572, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.441, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.108, \"our_mmol_l_h\": 9.576, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.86, \"viable_cell_density_million_ml\": 28.235}, {\"agitation_rpm\": 284.756, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.878, \"dissolved_oxygen_pct\": 28.687, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.541, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.074, \"our_mmol_l_h\": 10.744, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.904, \"viable_cell_density_million_ml\": 31.577}, {\"agitation_rpm\": 285.301, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.782, \"dissolved_oxygen_pct\": 18.145, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.688, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.996, \"our_mmol_l_h\": 11.769, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.843, \"viable_cell_density_million_ml\": 34.532}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cd0420b9ecec6b0192e8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.012, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.988, \"dissolved_oxygen_pct\": 35.768, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.712, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.658, \"viable_cell_density_million_ml\": 25.623}, {\"agitation_rpm\": 286.174, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.802, \"dissolved_oxygen_pct\": 36.572, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.441, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.108, \"our_mmol_l_h\": 9.576, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.86, \"viable_cell_density_million_ml\": 28.235}, {\"agitation_rpm\": 284.756, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.878, \"dissolved_oxygen_pct\": 28.687, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.541, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.074, \"our_mmol_l_h\": 10.744, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.904, \"viable_cell_density_million_ml\": 31.577}, {\"agitation_rpm\": 285.301, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.782, \"dissolved_oxygen_pct\": 18.145, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.688, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.996, \"our_mmol_l_h\": 11.769, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.843, \"viable_cell_density_million_ml\": 34.532}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f9c78c29280259ebbe09", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.012, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.988, \"dissolved_oxygen_pct\": 35.768, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.155, \"our_mmol_l_h\": 8.712, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.658, \"viable_cell_density_million_ml\": 25.623}, {\"agitation_rpm\": 286.174, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 8.802, \"dissolved_oxygen_pct\": 36.572, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.441, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.108, \"our_mmol_l_h\": 9.576, \"ph\": 7.005, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.86, \"viable_cell_density_million_ml\": 28.235}, {\"agitation_rpm\": 284.756, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.878, \"dissolved_oxygen_pct\": 28.687, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.541, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.074, \"our_mmol_l_h\": 10.744, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.904, \"viable_cell_density_million_ml\": 31.577}, {\"agitation_rpm\": 285.301, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.782, \"dissolved_oxygen_pct\": 18.145, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.688, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.996, \"our_mmol_l_h\": 11.769, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.843, \"viable_cell_density_million_ml\": 34.532}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3d6b5c697d190b539b34", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.824, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.23, \"dissolved_oxygen_pct\": 37.177, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.076, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.932, \"our_mmol_l_h\": 5.632, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.396, \"viable_cell_density_million_ml\": 16.667}, {\"agitation_rpm\": 271.295, \"airflow_vvm\": 0.282, \"cer_mmol_l_h\": 5.773, \"dissolved_oxygen_pct\": 36.627, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.033, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.819, \"our_mmol_l_h\": 6.252, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.442, \"viable_cell_density_million_ml\": 18.491}, {\"agitation_rpm\": 284.314, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.434, \"dissolved_oxygen_pct\": 36.574, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.051, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 6.466, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.366, \"viable_cell_density_million_ml\": 20.136}, {\"agitation_rpm\": 284.397, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.99, \"dissolved_oxygen_pct\": 36.261, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.681, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.601, \"our_mmol_l_h\": 6.431, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.023, \"viable_cell_density_million_ml\": 21.362}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-477c8eb37d145f6658cb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.824, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.23, \"dissolved_oxygen_pct\": 37.177, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.076, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.932, \"our_mmol_l_h\": 5.632, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.396, \"viable_cell_density_million_ml\": 16.667}, {\"agitation_rpm\": 271.295, \"airflow_vvm\": 0.282, \"cer_mmol_l_h\": 5.773, \"dissolved_oxygen_pct\": 36.627, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.033, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.819, \"our_mmol_l_h\": 6.252, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.442, \"viable_cell_density_million_ml\": 18.491}, {\"agitation_rpm\": 284.314, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.434, \"dissolved_oxygen_pct\": 36.574, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.051, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 6.466, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.366, \"viable_cell_density_million_ml\": 20.136}, {\"agitation_rpm\": 284.397, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.99, \"dissolved_oxygen_pct\": 36.261, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.681, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.601, \"our_mmol_l_h\": 6.431, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.023, \"viable_cell_density_million_ml\": 21.362}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ead8cfc58449e99e18b5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.824, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.23, \"dissolved_oxygen_pct\": 37.177, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.076, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.932, \"our_mmol_l_h\": 5.632, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.396, \"viable_cell_density_million_ml\": 16.667}, {\"agitation_rpm\": 271.295, \"airflow_vvm\": 0.282, \"cer_mmol_l_h\": 5.773, \"dissolved_oxygen_pct\": 36.627, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.033, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.819, \"our_mmol_l_h\": 6.252, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.442, \"viable_cell_density_million_ml\": 18.491}, {\"agitation_rpm\": 284.314, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.434, \"dissolved_oxygen_pct\": 36.574, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.051, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 6.466, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.366, \"viable_cell_density_million_ml\": 20.136}, {\"agitation_rpm\": 284.397, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.99, \"dissolved_oxygen_pct\": 36.261, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.681, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.601, \"our_mmol_l_h\": 6.431, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.023, \"viable_cell_density_million_ml\": 21.362}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ce8a74c86fcc33beba14", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.824, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.23, \"dissolved_oxygen_pct\": 37.177, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.076, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.932, \"our_mmol_l_h\": 5.632, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.396, \"viable_cell_density_million_ml\": 16.667}, {\"agitation_rpm\": 271.295, \"airflow_vvm\": 0.282, \"cer_mmol_l_h\": 5.773, \"dissolved_oxygen_pct\": 36.627, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.033, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.819, \"our_mmol_l_h\": 6.252, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.816, \"viability_pct\": 98.442, \"viable_cell_density_million_ml\": 18.491}, {\"agitation_rpm\": 284.314, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.434, \"dissolved_oxygen_pct\": 36.574, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.051, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.747, \"our_mmol_l_h\": 6.466, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.366, \"viable_cell_density_million_ml\": 20.136}, {\"agitation_rpm\": 284.397, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 6.99, \"dissolved_oxygen_pct\": 36.261, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.681, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.601, \"our_mmol_l_h\": 6.431, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.023, \"viable_cell_density_million_ml\": 21.362}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-82c92ba90a15f28b2a3e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.738, \"airflow_vvm\": 0.226, \"cer_mmol_l_h\": 4.232, \"dissolved_oxygen_pct\": 38.785, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.146, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.038, \"our_mmol_l_h\": 4.591, \"ph\": 6.955, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.491, \"viable_cell_density_million_ml\": 13.602}, {\"agitation_rpm\": 246.44, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.872, \"dissolved_oxygen_pct\": 38.266, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.068, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.959, \"our_mmol_l_h\": 5.326, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.504, \"viable_cell_density_million_ml\": 15.652}, {\"agitation_rpm\": 270.509, \"airflow_vvm\": 0.234, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 28.168, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.044, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.865, \"our_mmol_l_h\": 6.273, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.489, \"viable_cell_density_million_ml\": 18.407}, {\"agitation_rpm\": 284.744, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 6.628, \"dissolved_oxygen_pct\": 18.29, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.968, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.687, \"our_mmol_l_h\": 7.191, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 21.068}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1eb7bc727be576d84148", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.738, \"airflow_vvm\": 0.226, \"cer_mmol_l_h\": 4.232, \"dissolved_oxygen_pct\": 38.785, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.146, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.038, \"our_mmol_l_h\": 4.591, \"ph\": 6.955, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.491, \"viable_cell_density_million_ml\": 13.602}, {\"agitation_rpm\": 246.44, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.872, \"dissolved_oxygen_pct\": 38.266, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.068, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.959, \"our_mmol_l_h\": 5.326, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.504, \"viable_cell_density_million_ml\": 15.652}, {\"agitation_rpm\": 270.509, \"airflow_vvm\": 0.234, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 28.168, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.044, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.865, \"our_mmol_l_h\": 6.273, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.489, \"viable_cell_density_million_ml\": 18.407}, {\"agitation_rpm\": 284.744, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 6.628, \"dissolved_oxygen_pct\": 18.29, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.968, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.687, \"our_mmol_l_h\": 7.191, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 21.068}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3cbe7412bbbfaff73493", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.738, \"airflow_vvm\": 0.226, \"cer_mmol_l_h\": 4.232, \"dissolved_oxygen_pct\": 38.785, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.146, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.038, \"our_mmol_l_h\": 4.591, \"ph\": 6.955, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.491, \"viable_cell_density_million_ml\": 13.602}, {\"agitation_rpm\": 246.44, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.872, \"dissolved_oxygen_pct\": 38.266, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.068, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.959, \"our_mmol_l_h\": 5.326, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.504, \"viable_cell_density_million_ml\": 15.652}, {\"agitation_rpm\": 270.509, \"airflow_vvm\": 0.234, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 28.168, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.044, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.865, \"our_mmol_l_h\": 6.273, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.489, \"viable_cell_density_million_ml\": 18.407}, {\"agitation_rpm\": 284.744, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 6.628, \"dissolved_oxygen_pct\": 18.29, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.968, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.687, \"our_mmol_l_h\": 7.191, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 21.068}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dc47e94d5804d636d28b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 225.738, \"airflow_vvm\": 0.226, \"cer_mmol_l_h\": 4.232, \"dissolved_oxygen_pct\": 38.785, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.146, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.038, \"our_mmol_l_h\": 4.591, \"ph\": 6.955, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.491, \"viable_cell_density_million_ml\": 13.602}, {\"agitation_rpm\": 246.44, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.872, \"dissolved_oxygen_pct\": 38.266, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.068, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.959, \"our_mmol_l_h\": 5.326, \"ph\": 6.963, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.504, \"viable_cell_density_million_ml\": 15.652}, {\"agitation_rpm\": 270.509, \"airflow_vvm\": 0.234, \"cer_mmol_l_h\": 5.786, \"dissolved_oxygen_pct\": 28.168, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.044, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.865, \"our_mmol_l_h\": 6.273, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.489, \"viable_cell_density_million_ml\": 18.407}, {\"agitation_rpm\": 284.744, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 6.628, \"dissolved_oxygen_pct\": 18.29, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.968, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.687, \"our_mmol_l_h\": 7.191, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 21.068}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a1d6565e36151e19e335", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.11, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.587, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.378, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.205, \"our_mmol_l_h\": 7.171, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.926, \"viable_cell_density_million_ml\": 21.03}, {\"agitation_rpm\": 284.095, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.516, \"dissolved_oxygen_pct\": 36.306, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.331, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.167, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.746, \"viable_cell_density_million_ml\": 24.13}, {\"agitation_rpm\": 283.866, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.837, \"dissolved_oxygen_pct\": 36.785, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.45, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.064, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.872, \"viable_cell_density_million_ml\": 27.757}, {\"agitation_rpm\": 284.035, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.118, \"dissolved_oxygen_pct\": 36.978, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.34, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.051, \"our_mmol_l_h\": 9.82, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 31.443}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bd12b19dd5ab83d617b5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.11, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.587, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.378, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.205, \"our_mmol_l_h\": 7.171, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.926, \"viable_cell_density_million_ml\": 21.03}, {\"agitation_rpm\": 284.095, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.516, \"dissolved_oxygen_pct\": 36.306, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.331, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.167, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.746, \"viable_cell_density_million_ml\": 24.13}, {\"agitation_rpm\": 283.866, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.837, \"dissolved_oxygen_pct\": 36.785, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.45, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.064, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.872, \"viable_cell_density_million_ml\": 27.757}, {\"agitation_rpm\": 284.035, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.118, \"dissolved_oxygen_pct\": 36.978, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.34, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.051, \"our_mmol_l_h\": 9.82, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 31.443}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-00181cfeba58ce20e7ba", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.11, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.587, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.378, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.205, \"our_mmol_l_h\": 7.171, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.926, \"viable_cell_density_million_ml\": 21.03}, {\"agitation_rpm\": 284.095, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.516, \"dissolved_oxygen_pct\": 36.306, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.331, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.167, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.746, \"viable_cell_density_million_ml\": 24.13}, {\"agitation_rpm\": 283.866, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.837, \"dissolved_oxygen_pct\": 36.785, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.45, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.064, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.872, \"viable_cell_density_million_ml\": 27.757}, {\"agitation_rpm\": 284.035, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.118, \"dissolved_oxygen_pct\": 36.978, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.34, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.051, \"our_mmol_l_h\": 9.82, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 31.443}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1f271b5cb68e2c548ec0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.11, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.587, \"dissolved_oxygen_pct\": 35.646, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.378, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.205, \"our_mmol_l_h\": 7.171, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.818, \"viability_pct\": 98.926, \"viable_cell_density_million_ml\": 21.03}, {\"agitation_rpm\": 284.095, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 7.516, \"dissolved_oxygen_pct\": 36.306, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.331, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.167, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.746, \"viable_cell_density_million_ml\": 24.13}, {\"agitation_rpm\": 283.866, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.837, \"dissolved_oxygen_pct\": 36.785, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.45, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.104, \"our_mmol_l_h\": 9.064, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.872, \"viable_cell_density_million_ml\": 27.757}, {\"agitation_rpm\": 284.035, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.118, \"dissolved_oxygen_pct\": 36.978, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.34, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.051, \"our_mmol_l_h\": 9.82, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 31.443}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b229456a1e331e0dd2af", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.726, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.085, \"dissolved_oxygen_pct\": 37.188, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.426, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.55, \"ph\": 7.002, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.685, \"viable_cell_density_million_ml\": 16.315}, {\"agitation_rpm\": 281.041, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.093, \"dissolved_oxygen_pct\": 35.983, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.405, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.22, \"our_mmol_l_h\": 6.599, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.901, \"viable_cell_density_million_ml\": 19.521}, {\"agitation_rpm\": 285.36, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.517, \"dissolved_oxygen_pct\": 29.146, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.354, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.173, \"our_mmol_l_h\": 8.223, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.683, \"viable_cell_density_million_ml\": 24.127}, {\"agitation_rpm\": 284.849, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 9.176, \"dissolved_oxygen_pct\": 17.823, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.431, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.089, \"our_mmol_l_h\": 10.021, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 29.382}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-07b1dd0f54c93a5afa0a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.726, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.085, \"dissolved_oxygen_pct\": 37.188, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.426, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.55, \"ph\": 7.002, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.685, \"viable_cell_density_million_ml\": 16.315}, {\"agitation_rpm\": 281.041, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.093, \"dissolved_oxygen_pct\": 35.983, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.405, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.22, \"our_mmol_l_h\": 6.599, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.901, \"viable_cell_density_million_ml\": 19.521}, {\"agitation_rpm\": 285.36, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.517, \"dissolved_oxygen_pct\": 29.146, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.354, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.173, \"our_mmol_l_h\": 8.223, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.683, \"viable_cell_density_million_ml\": 24.127}, {\"agitation_rpm\": 284.849, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 9.176, \"dissolved_oxygen_pct\": 17.823, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.431, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.089, \"our_mmol_l_h\": 10.021, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 29.382}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ba5cfc253816237d7b3b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.726, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.085, \"dissolved_oxygen_pct\": 37.188, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.426, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.55, \"ph\": 7.002, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.685, \"viable_cell_density_million_ml\": 16.315}, {\"agitation_rpm\": 281.041, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.093, \"dissolved_oxygen_pct\": 35.983, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.405, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.22, \"our_mmol_l_h\": 6.599, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.901, \"viable_cell_density_million_ml\": 19.521}, {\"agitation_rpm\": 285.36, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.517, \"dissolved_oxygen_pct\": 29.146, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.354, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.173, \"our_mmol_l_h\": 8.223, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.683, \"viable_cell_density_million_ml\": 24.127}, {\"agitation_rpm\": 284.849, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 9.176, \"dissolved_oxygen_pct\": 17.823, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.431, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.089, \"our_mmol_l_h\": 10.021, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 29.382}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4476621468306b69df0e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.726, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.085, \"dissolved_oxygen_pct\": 37.188, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.426, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.55, \"ph\": 7.002, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.685, \"viable_cell_density_million_ml\": 16.315}, {\"agitation_rpm\": 281.041, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.093, \"dissolved_oxygen_pct\": 35.983, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.405, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.22, \"our_mmol_l_h\": 6.599, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.901, \"viable_cell_density_million_ml\": 19.521}, {\"agitation_rpm\": 285.36, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.517, \"dissolved_oxygen_pct\": 29.146, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.354, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.173, \"our_mmol_l_h\": 8.223, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.683, \"viable_cell_density_million_ml\": 24.127}, {\"agitation_rpm\": 284.849, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 9.176, \"dissolved_oxygen_pct\": 17.823, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.431, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.089, \"our_mmol_l_h\": 10.021, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 29.382}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-60a02cb3f8c6ceccd0f7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.333, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.224, \"dissolved_oxygen_pct\": 39.551, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.162, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.031, \"our_mmol_l_h\": 4.623, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.783, \"viability_pct\": 98.422, \"viable_cell_density_million_ml\": 13.473}, {\"agitation_rpm\": 245.234, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.892, \"dissolved_oxygen_pct\": 38.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.079, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.955, \"our_mmol_l_h\": 5.331, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.586, \"viable_cell_density_million_ml\": 15.576}, {\"agitation_rpm\": 271.35, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.732, \"dissolved_oxygen_pct\": 36.601, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.065, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.85, \"our_mmol_l_h\": 5.735, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.565, \"viable_cell_density_million_ml\": 17.986}, {\"agitation_rpm\": 284.099, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.598, \"dissolved_oxygen_pct\": 36.766, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.75, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.691, \"our_mmol_l_h\": 5.944, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.649, \"viable_cell_density_million_ml\": 20.14}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5d061110b4ee5a7fbe82", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.333, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.224, \"dissolved_oxygen_pct\": 39.551, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.162, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.031, \"our_mmol_l_h\": 4.623, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.783, \"viability_pct\": 98.422, \"viable_cell_density_million_ml\": 13.473}, {\"agitation_rpm\": 245.234, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.892, \"dissolved_oxygen_pct\": 38.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.079, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.955, \"our_mmol_l_h\": 5.331, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.586, \"viable_cell_density_million_ml\": 15.576}, {\"agitation_rpm\": 271.35, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.732, \"dissolved_oxygen_pct\": 36.601, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.065, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.85, \"our_mmol_l_h\": 5.735, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.565, \"viable_cell_density_million_ml\": 17.986}, {\"agitation_rpm\": 284.099, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.598, \"dissolved_oxygen_pct\": 36.766, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.75, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.691, \"our_mmol_l_h\": 5.944, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.649, \"viable_cell_density_million_ml\": 20.14}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a52742e778bf2e8c26b2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.333, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.224, \"dissolved_oxygen_pct\": 39.551, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.162, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.031, \"our_mmol_l_h\": 4.623, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.783, \"viability_pct\": 98.422, \"viable_cell_density_million_ml\": 13.473}, {\"agitation_rpm\": 245.234, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.892, \"dissolved_oxygen_pct\": 38.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.079, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.955, \"our_mmol_l_h\": 5.331, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.586, \"viable_cell_density_million_ml\": 15.576}, {\"agitation_rpm\": 271.35, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.732, \"dissolved_oxygen_pct\": 36.601, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.065, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.85, \"our_mmol_l_h\": 5.735, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.565, \"viable_cell_density_million_ml\": 17.986}, {\"agitation_rpm\": 284.099, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.598, \"dissolved_oxygen_pct\": 36.766, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.75, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.691, \"our_mmol_l_h\": 5.944, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.649, \"viable_cell_density_million_ml\": 20.14}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d62c03bf9329e0b38234", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.333, \"airflow_vvm\": 0.228, \"cer_mmol_l_h\": 4.224, \"dissolved_oxygen_pct\": 39.551, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.162, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.031, \"our_mmol_l_h\": 4.623, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.783, \"viability_pct\": 98.422, \"viable_cell_density_million_ml\": 13.473}, {\"agitation_rpm\": 245.234, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.892, \"dissolved_oxygen_pct\": 38.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.079, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.955, \"our_mmol_l_h\": 5.331, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.586, \"viable_cell_density_million_ml\": 15.576}, {\"agitation_rpm\": 271.35, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.732, \"dissolved_oxygen_pct\": 36.601, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.065, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.85, \"our_mmol_l_h\": 5.735, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.565, \"viable_cell_density_million_ml\": 17.986}, {\"agitation_rpm\": 284.099, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.598, \"dissolved_oxygen_pct\": 36.766, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.75, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.691, \"our_mmol_l_h\": 5.944, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.649, \"viable_cell_density_million_ml\": 20.14}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-423f7911435359e3c03e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.801, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.208, \"dissolved_oxygen_pct\": 37.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.1, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.92, \"our_mmol_l_h\": 5.626, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.515, \"viable_cell_density_million_ml\": 16.586}, {\"agitation_rpm\": 269.823, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.754, \"dissolved_oxygen_pct\": 36.937, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.032, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.858, \"our_mmol_l_h\": 6.282, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.594, \"viable_cell_density_million_ml\": 18.399}, {\"agitation_rpm\": 284.349, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 6.439, \"dissolved_oxygen_pct\": 29.215, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.937, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.751, \"our_mmol_l_h\": 7.008, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.57, \"viable_cell_density_million_ml\": 20.519}, {\"agitation_rpm\": 284.298, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.0, \"dissolved_oxygen_pct\": 18.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.853, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.595, \"our_mmol_l_h\": 7.628, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.112, \"viable_cell_density_million_ml\": 22.31}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cbdac574f00c188ee1ef", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.801, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.208, \"dissolved_oxygen_pct\": 37.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.1, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.92, \"our_mmol_l_h\": 5.626, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.515, \"viable_cell_density_million_ml\": 16.586}, {\"agitation_rpm\": 269.823, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.754, \"dissolved_oxygen_pct\": 36.937, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.032, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.858, \"our_mmol_l_h\": 6.282, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.594, \"viable_cell_density_million_ml\": 18.399}, {\"agitation_rpm\": 284.349, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 6.439, \"dissolved_oxygen_pct\": 29.215, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.937, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.751, \"our_mmol_l_h\": 7.008, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.57, \"viable_cell_density_million_ml\": 20.519}, {\"agitation_rpm\": 284.298, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.0, \"dissolved_oxygen_pct\": 18.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.853, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.595, \"our_mmol_l_h\": 7.628, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.112, \"viable_cell_density_million_ml\": 22.31}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-59b0a91c7c44a00da5ee", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.801, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.208, \"dissolved_oxygen_pct\": 37.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.1, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.92, \"our_mmol_l_h\": 5.626, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.515, \"viable_cell_density_million_ml\": 16.586}, {\"agitation_rpm\": 269.823, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.754, \"dissolved_oxygen_pct\": 36.937, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.032, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.858, \"our_mmol_l_h\": 6.282, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.594, \"viable_cell_density_million_ml\": 18.399}, {\"agitation_rpm\": 284.349, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 6.439, \"dissolved_oxygen_pct\": 29.215, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.937, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.751, \"our_mmol_l_h\": 7.008, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.57, \"viable_cell_density_million_ml\": 20.519}, {\"agitation_rpm\": 284.298, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.0, \"dissolved_oxygen_pct\": 18.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.853, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.595, \"our_mmol_l_h\": 7.628, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.112, \"viable_cell_density_million_ml\": 22.31}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7138edd669c3ccd67efd", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.801, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.208, \"dissolved_oxygen_pct\": 37.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.1, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.92, \"our_mmol_l_h\": 5.626, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.515, \"viable_cell_density_million_ml\": 16.586}, {\"agitation_rpm\": 269.823, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.754, \"dissolved_oxygen_pct\": 36.937, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.032, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.858, \"our_mmol_l_h\": 6.282, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.594, \"viable_cell_density_million_ml\": 18.399}, {\"agitation_rpm\": 284.349, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 6.439, \"dissolved_oxygen_pct\": 29.215, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.937, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.751, \"our_mmol_l_h\": 7.008, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.57, \"viable_cell_density_million_ml\": 20.519}, {\"agitation_rpm\": 284.298, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.0, \"dissolved_oxygen_pct\": 18.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.853, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.595, \"our_mmol_l_h\": 7.628, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.112, \"viable_cell_density_million_ml\": 22.31}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c6f0aaf49ffaab981f36", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.534, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.991, \"dissolved_oxygen_pct\": 36.159, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.379, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.119, \"our_mmol_l_h\": 8.685, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.754, \"viable_cell_density_million_ml\": 25.539}, {\"agitation_rpm\": 284.426, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.835, \"dissolved_oxygen_pct\": 35.74, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.44, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.11, \"our_mmol_l_h\": 9.59, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.781, \"viable_cell_density_million_ml\": 28.184}, {\"agitation_rpm\": 284.778, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.875, \"dissolved_oxygen_pct\": 36.073, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.562, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.037, \"our_mmol_l_h\": 10.175, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.844, \"viable_cell_density_million_ml\": 31.093}, {\"agitation_rpm\": 285.684, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 10.824, \"dissolved_oxygen_pct\": 36.095, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.532, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.982, \"our_mmol_l_h\": 10.547, \"ph\": 7.012, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.902, \"viable_cell_density_million_ml\": 33.506}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-10d9205eea966bcae253", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.534, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.991, \"dissolved_oxygen_pct\": 36.159, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.379, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.119, \"our_mmol_l_h\": 8.685, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.754, \"viable_cell_density_million_ml\": 25.539}, {\"agitation_rpm\": 284.426, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.835, \"dissolved_oxygen_pct\": 35.74, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.44, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.11, \"our_mmol_l_h\": 9.59, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.781, \"viable_cell_density_million_ml\": 28.184}, {\"agitation_rpm\": 284.778, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.875, \"dissolved_oxygen_pct\": 36.073, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.562, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.037, \"our_mmol_l_h\": 10.175, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.844, \"viable_cell_density_million_ml\": 31.093}, {\"agitation_rpm\": 285.684, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 10.824, \"dissolved_oxygen_pct\": 36.095, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.532, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.982, \"our_mmol_l_h\": 10.547, \"ph\": 7.012, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.902, \"viable_cell_density_million_ml\": 33.506}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e275d9bb01d12f5170c1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.534, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.991, \"dissolved_oxygen_pct\": 36.159, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.379, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.119, \"our_mmol_l_h\": 8.685, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.754, \"viable_cell_density_million_ml\": 25.539}, {\"agitation_rpm\": 284.426, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.835, \"dissolved_oxygen_pct\": 35.74, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.44, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.11, \"our_mmol_l_h\": 9.59, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.781, \"viable_cell_density_million_ml\": 28.184}, {\"agitation_rpm\": 284.778, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.875, \"dissolved_oxygen_pct\": 36.073, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.562, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.037, \"our_mmol_l_h\": 10.175, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.844, \"viable_cell_density_million_ml\": 31.093}, {\"agitation_rpm\": 285.684, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 10.824, \"dissolved_oxygen_pct\": 36.095, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.532, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.982, \"our_mmol_l_h\": 10.547, \"ph\": 7.012, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.902, \"viable_cell_density_million_ml\": 33.506}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b1907fa82967e5ed240f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.534, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.991, \"dissolved_oxygen_pct\": 36.159, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.379, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.119, \"our_mmol_l_h\": 8.685, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.754, \"viable_cell_density_million_ml\": 25.539}, {\"agitation_rpm\": 284.426, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.835, \"dissolved_oxygen_pct\": 35.74, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.44, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.11, \"our_mmol_l_h\": 9.59, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.794, \"viability_pct\": 98.781, \"viable_cell_density_million_ml\": 28.184}, {\"agitation_rpm\": 284.778, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.875, \"dissolved_oxygen_pct\": 36.073, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.562, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.037, \"our_mmol_l_h\": 10.175, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.844, \"viable_cell_density_million_ml\": 31.093}, {\"agitation_rpm\": 285.684, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 10.824, \"dissolved_oxygen_pct\": 36.095, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.532, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.982, \"our_mmol_l_h\": 10.547, \"ph\": 7.012, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 98.902, \"viable_cell_density_million_ml\": 33.506}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cd5c99c693a17922a889", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.752, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.998, \"dissolved_oxygen_pct\": 36.929, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.363, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.145, \"our_mmol_l_h\": 8.683, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.663, \"viable_cell_density_million_ml\": 25.51}, {\"agitation_rpm\": 284.881, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.855, \"dissolved_oxygen_pct\": 36.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.389, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.11, \"our_mmol_l_h\": 9.576, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.92, \"viable_cell_density_million_ml\": 28.155}, {\"agitation_rpm\": 284.9, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.866, \"dissolved_oxygen_pct\": 28.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.495, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.042, \"our_mmol_l_h\": 10.735, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.694, \"viable_cell_density_million_ml\": 31.496}, {\"agitation_rpm\": 285.318, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.807, \"dissolved_oxygen_pct\": 17.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.702, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 11.741, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.783, \"viable_cell_density_million_ml\": 34.487}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bd05902d9235343651ce", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.752, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.998, \"dissolved_oxygen_pct\": 36.929, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.363, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.145, \"our_mmol_l_h\": 8.683, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.663, \"viable_cell_density_million_ml\": 25.51}, {\"agitation_rpm\": 284.881, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.855, \"dissolved_oxygen_pct\": 36.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.389, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.11, \"our_mmol_l_h\": 9.576, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.92, \"viable_cell_density_million_ml\": 28.155}, {\"agitation_rpm\": 284.9, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.866, \"dissolved_oxygen_pct\": 28.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.495, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.042, \"our_mmol_l_h\": 10.735, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.694, \"viable_cell_density_million_ml\": 31.496}, {\"agitation_rpm\": 285.318, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.807, \"dissolved_oxygen_pct\": 17.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.702, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 11.741, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.783, \"viable_cell_density_million_ml\": 34.487}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-53e84268b7cca23b1628", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.752, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.998, \"dissolved_oxygen_pct\": 36.929, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.363, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.145, \"our_mmol_l_h\": 8.683, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.663, \"viable_cell_density_million_ml\": 25.51}, {\"agitation_rpm\": 284.881, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.855, \"dissolved_oxygen_pct\": 36.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.389, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.11, \"our_mmol_l_h\": 9.576, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.92, \"viable_cell_density_million_ml\": 28.155}, {\"agitation_rpm\": 284.9, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.866, \"dissolved_oxygen_pct\": 28.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.495, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.042, \"our_mmol_l_h\": 10.735, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.694, \"viable_cell_density_million_ml\": 31.496}, {\"agitation_rpm\": 285.318, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.807, \"dissolved_oxygen_pct\": 17.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.702, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 11.741, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.783, \"viable_cell_density_million_ml\": 34.487}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-730abe6bf7eb2b24eaa7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.752, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.998, \"dissolved_oxygen_pct\": 36.929, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.363, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.145, \"our_mmol_l_h\": 8.683, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.663, \"viable_cell_density_million_ml\": 25.51}, {\"agitation_rpm\": 284.881, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 8.855, \"dissolved_oxygen_pct\": 36.674, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.389, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.11, \"our_mmol_l_h\": 9.576, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.92, \"viable_cell_density_million_ml\": 28.155}, {\"agitation_rpm\": 284.9, \"airflow_vvm\": 0.249, \"cer_mmol_l_h\": 9.866, \"dissolved_oxygen_pct\": 28.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.495, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.042, \"our_mmol_l_h\": 10.735, \"ph\": 7.009, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.694, \"viable_cell_density_million_ml\": 31.496}, {\"agitation_rpm\": 285.318, \"airflow_vvm\": 0.181, \"cer_mmol_l_h\": 10.807, \"dissolved_oxygen_pct\": 17.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.702, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.99, \"our_mmol_l_h\": 11.741, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.783, \"viable_cell_density_million_ml\": 34.487}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7cc8293e8530860e64ea", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.732, \"airflow_vvm\": 0.191, \"cer_mmol_l_h\": 3.236, \"dissolved_oxygen_pct\": 40.986, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.446, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.76, \"our_mmol_l_h\": 3.51, \"ph\": 6.974, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 10.303}, {\"agitation_rpm\": 215.9, \"airflow_vvm\": 0.218, \"cer_mmol_l_h\": 3.922, \"dissolved_oxygen_pct\": 39.231, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.187, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.925, \"our_mmol_l_h\": 4.203, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.627, \"viable_cell_density_million_ml\": 12.441}, {\"agitation_rpm\": 246.704, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.91, \"dissolved_oxygen_pct\": 37.35, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.152, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.942, \"our_mmol_l_h\": 4.807, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.452, \"viable_cell_density_million_ml\": 15.177}, {\"agitation_rpm\": 277.133, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.007, \"dissolved_oxygen_pct\": 36.881, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.781, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 5.356, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.496, \"viable_cell_density_million_ml\": 18.206}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3c105e050a1374857931", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.732, \"airflow_vvm\": 0.191, \"cer_mmol_l_h\": 3.236, \"dissolved_oxygen_pct\": 40.986, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.446, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.76, \"our_mmol_l_h\": 3.51, \"ph\": 6.974, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 10.303}, {\"agitation_rpm\": 215.9, \"airflow_vvm\": 0.218, \"cer_mmol_l_h\": 3.922, \"dissolved_oxygen_pct\": 39.231, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.187, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.925, \"our_mmol_l_h\": 4.203, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.627, \"viable_cell_density_million_ml\": 12.441}, {\"agitation_rpm\": 246.704, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.91, \"dissolved_oxygen_pct\": 37.35, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.152, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.942, \"our_mmol_l_h\": 4.807, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.452, \"viable_cell_density_million_ml\": 15.177}, {\"agitation_rpm\": 277.133, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.007, \"dissolved_oxygen_pct\": 36.881, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.781, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 5.356, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.496, \"viable_cell_density_million_ml\": 18.206}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5d763c47eeefec8a86d0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.732, \"airflow_vvm\": 0.191, \"cer_mmol_l_h\": 3.236, \"dissolved_oxygen_pct\": 40.986, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.446, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.76, \"our_mmol_l_h\": 3.51, \"ph\": 6.974, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 10.303}, {\"agitation_rpm\": 215.9, \"airflow_vvm\": 0.218, \"cer_mmol_l_h\": 3.922, \"dissolved_oxygen_pct\": 39.231, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.187, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.925, \"our_mmol_l_h\": 4.203, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.627, \"viable_cell_density_million_ml\": 12.441}, {\"agitation_rpm\": 246.704, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.91, \"dissolved_oxygen_pct\": 37.35, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.152, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.942, \"our_mmol_l_h\": 4.807, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.452, \"viable_cell_density_million_ml\": 15.177}, {\"agitation_rpm\": 277.133, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.007, \"dissolved_oxygen_pct\": 36.881, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.781, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 5.356, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.496, \"viable_cell_density_million_ml\": 18.206}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-88f6788dedbbe5bb156b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.732, \"airflow_vvm\": 0.191, \"cer_mmol_l_h\": 3.236, \"dissolved_oxygen_pct\": 40.986, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.446, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.76, \"our_mmol_l_h\": 3.51, \"ph\": 6.974, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.426, \"viable_cell_density_million_ml\": 10.303}, {\"agitation_rpm\": 215.9, \"airflow_vvm\": 0.218, \"cer_mmol_l_h\": 3.922, \"dissolved_oxygen_pct\": 39.231, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.187, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.925, \"our_mmol_l_h\": 4.203, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.812, \"viability_pct\": 98.627, \"viable_cell_density_million_ml\": 12.441}, {\"agitation_rpm\": 246.704, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.91, \"dissolved_oxygen_pct\": 37.35, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.152, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.942, \"our_mmol_l_h\": 4.807, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.452, \"viable_cell_density_million_ml\": 15.177}, {\"agitation_rpm\": 277.133, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 6.007, \"dissolved_oxygen_pct\": 36.881, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.781, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 5.356, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.496, \"viable_cell_density_million_ml\": 18.206}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-91cf464a7438fd4045ea", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.751, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.187, \"dissolved_oxygen_pct\": 36.748, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 5.636, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.376, \"viable_cell_density_million_ml\": 16.662}, {\"agitation_rpm\": 270.519, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.776, \"dissolved_oxygen_pct\": 36.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.824, \"our_mmol_l_h\": 6.282, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.451, \"viable_cell_density_million_ml\": 18.341}, {\"agitation_rpm\": 283.903, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 28.438, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.995, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.758, \"our_mmol_l_h\": 7.005, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.618, \"viable_cell_density_million_ml\": 20.579}, {\"agitation_rpm\": 284.075, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 7.014, \"dissolved_oxygen_pct\": 17.978, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.84, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.606, \"our_mmol_l_h\": 7.622, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.158, \"viable_cell_density_million_ml\": 22.417}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c393228de4c9dde30950", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.751, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.187, \"dissolved_oxygen_pct\": 36.748, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 5.636, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.376, \"viable_cell_density_million_ml\": 16.662}, {\"agitation_rpm\": 270.519, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.776, \"dissolved_oxygen_pct\": 36.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.824, \"our_mmol_l_h\": 6.282, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.451, \"viable_cell_density_million_ml\": 18.341}, {\"agitation_rpm\": 283.903, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 28.438, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.995, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.758, \"our_mmol_l_h\": 7.005, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.618, \"viable_cell_density_million_ml\": 20.579}, {\"agitation_rpm\": 284.075, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 7.014, \"dissolved_oxygen_pct\": 17.978, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.84, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.606, \"our_mmol_l_h\": 7.622, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.158, \"viable_cell_density_million_ml\": 22.417}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5f491fc1ad83428054c7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.751, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.187, \"dissolved_oxygen_pct\": 36.748, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 5.636, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.376, \"viable_cell_density_million_ml\": 16.662}, {\"agitation_rpm\": 270.519, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.776, \"dissolved_oxygen_pct\": 36.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.824, \"our_mmol_l_h\": 6.282, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.451, \"viable_cell_density_million_ml\": 18.341}, {\"agitation_rpm\": 283.903, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 28.438, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.995, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.758, \"our_mmol_l_h\": 7.005, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.618, \"viable_cell_density_million_ml\": 20.579}, {\"agitation_rpm\": 284.075, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 7.014, \"dissolved_oxygen_pct\": 17.978, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.84, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.606, \"our_mmol_l_h\": 7.622, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.158, \"viable_cell_density_million_ml\": 22.417}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-622838cb14e67fd0d1ad", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 253.751, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.187, \"dissolved_oxygen_pct\": 36.748, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.045, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 5.636, \"ph\": 6.96, \"phase\": \"exponential\", \"temperature_c\": 36.782, \"viability_pct\": 98.376, \"viable_cell_density_million_ml\": 16.662}, {\"agitation_rpm\": 270.519, \"airflow_vvm\": 0.281, \"cer_mmol_l_h\": 5.776, \"dissolved_oxygen_pct\": 36.73, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.824, \"our_mmol_l_h\": 6.282, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.451, \"viable_cell_density_million_ml\": 18.341}, {\"agitation_rpm\": 283.903, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 28.438, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.995, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.758, \"our_mmol_l_h\": 7.005, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 98.618, \"viable_cell_density_million_ml\": 20.579}, {\"agitation_rpm\": 284.075, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 7.014, \"dissolved_oxygen_pct\": 17.978, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.84, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.606, \"our_mmol_l_h\": 7.622, \"ph\": 6.977, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.158, \"viable_cell_density_million_ml\": 22.417}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ed791ab56ee4b6d78a1b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.078, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.561, \"dissolved_oxygen_pct\": 35.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.363, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 7.122, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.793, \"viability_pct\": 98.796, \"viable_cell_density_million_ml\": 21.069}, {\"agitation_rpm\": 283.847, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.547, \"dissolved_oxygen_pct\": 36.857, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.327, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.131, \"our_mmol_l_h\": 8.218, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.916, \"viable_cell_density_million_ml\": 24.155}, {\"agitation_rpm\": 284.031, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.828, \"dissolved_oxygen_pct\": 36.81, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.469, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 9.058, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.71, \"viable_cell_density_million_ml\": 27.734}, {\"agitation_rpm\": 285.128, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.166, \"dissolved_oxygen_pct\": 36.574, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.372, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.027, \"our_mmol_l_h\": 9.802, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.814, \"viable_cell_density_million_ml\": 31.357}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2f007a64d3037004842b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.078, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.561, \"dissolved_oxygen_pct\": 35.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.363, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 7.122, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.793, \"viability_pct\": 98.796, \"viable_cell_density_million_ml\": 21.069}, {\"agitation_rpm\": 283.847, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.547, \"dissolved_oxygen_pct\": 36.857, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.327, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.131, \"our_mmol_l_h\": 8.218, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.916, \"viable_cell_density_million_ml\": 24.155}, {\"agitation_rpm\": 284.031, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.828, \"dissolved_oxygen_pct\": 36.81, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.469, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 9.058, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.71, \"viable_cell_density_million_ml\": 27.734}, {\"agitation_rpm\": 285.128, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.166, \"dissolved_oxygen_pct\": 36.574, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.372, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.027, \"our_mmol_l_h\": 9.802, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.814, \"viable_cell_density_million_ml\": 31.357}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-666e64b39ba31ca266b3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.078, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.561, \"dissolved_oxygen_pct\": 35.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.363, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 7.122, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.793, \"viability_pct\": 98.796, \"viable_cell_density_million_ml\": 21.069}, {\"agitation_rpm\": 283.847, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.547, \"dissolved_oxygen_pct\": 36.857, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.327, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.131, \"our_mmol_l_h\": 8.218, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.916, \"viable_cell_density_million_ml\": 24.155}, {\"agitation_rpm\": 284.031, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.828, \"dissolved_oxygen_pct\": 36.81, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.469, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 9.058, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.71, \"viable_cell_density_million_ml\": 27.734}, {\"agitation_rpm\": 285.128, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.166, \"dissolved_oxygen_pct\": 36.574, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.372, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.027, \"our_mmol_l_h\": 9.802, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.814, \"viable_cell_density_million_ml\": 31.357}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7bd79ab403d6d2dfb56f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.078, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.561, \"dissolved_oxygen_pct\": 35.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.363, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.192, \"our_mmol_l_h\": 7.122, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.793, \"viability_pct\": 98.796, \"viable_cell_density_million_ml\": 21.069}, {\"agitation_rpm\": 283.847, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.547, \"dissolved_oxygen_pct\": 36.857, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.327, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.131, \"our_mmol_l_h\": 8.218, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.822, \"viability_pct\": 98.916, \"viable_cell_density_million_ml\": 24.155}, {\"agitation_rpm\": 284.031, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.828, \"dissolved_oxygen_pct\": 36.81, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.469, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.082, \"our_mmol_l_h\": 9.058, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.71, \"viable_cell_density_million_ml\": 27.734}, {\"agitation_rpm\": 285.128, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.166, \"dissolved_oxygen_pct\": 36.574, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.372, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.027, \"our_mmol_l_h\": 9.802, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.814, \"viable_cell_density_million_ml\": 31.357}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-82e613d0053b68739888", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.639, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.72, \"dissolved_oxygen_pct\": 39.604, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.613, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.257, \"our_mmol_l_h\": 4.045, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.93, \"viable_cell_density_million_ml\": 11.895}, {\"agitation_rpm\": 238.908, \"airflow_vvm\": 0.24, \"cer_mmol_l_h\": 4.626, \"dissolved_oxygen_pct\": 38.697, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.511, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.269, \"our_mmol_l_h\": 5.009, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.739, \"viable_cell_density_million_ml\": 14.777}, {\"agitation_rpm\": 280.497, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 27.951, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.351, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.2, \"our_mmol_l_h\": 6.604, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.832, \"viable_cell_density_million_ml\": 19.432}, {\"agitation_rpm\": 286.188, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 8.006, \"dissolved_oxygen_pct\": 18.051, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.339, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 8.712, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 25.5}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-252bebd4439612e901c7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.639, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.72, \"dissolved_oxygen_pct\": 39.604, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.613, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.257, \"our_mmol_l_h\": 4.045, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.93, \"viable_cell_density_million_ml\": 11.895}, {\"agitation_rpm\": 238.908, \"airflow_vvm\": 0.24, \"cer_mmol_l_h\": 4.626, \"dissolved_oxygen_pct\": 38.697, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.511, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.269, \"our_mmol_l_h\": 5.009, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.739, \"viable_cell_density_million_ml\": 14.777}, {\"agitation_rpm\": 280.497, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 27.951, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.351, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.2, \"our_mmol_l_h\": 6.604, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.832, \"viable_cell_density_million_ml\": 19.432}, {\"agitation_rpm\": 286.188, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 8.006, \"dissolved_oxygen_pct\": 18.051, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.339, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 8.712, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 25.5}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-df53c2c10f06842ae263", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.639, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.72, \"dissolved_oxygen_pct\": 39.604, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.613, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.257, \"our_mmol_l_h\": 4.045, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.93, \"viable_cell_density_million_ml\": 11.895}, {\"agitation_rpm\": 238.908, \"airflow_vvm\": 0.24, \"cer_mmol_l_h\": 4.626, \"dissolved_oxygen_pct\": 38.697, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.511, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.269, \"our_mmol_l_h\": 5.009, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.739, \"viable_cell_density_million_ml\": 14.777}, {\"agitation_rpm\": 280.497, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 27.951, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.351, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.2, \"our_mmol_l_h\": 6.604, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.832, \"viable_cell_density_million_ml\": 19.432}, {\"agitation_rpm\": 286.188, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 8.006, \"dissolved_oxygen_pct\": 18.051, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.339, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 8.712, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 25.5}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fd8900a2f572f1200629", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.639, \"airflow_vvm\": 0.211, \"cer_mmol_l_h\": 3.72, \"dissolved_oxygen_pct\": 39.604, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.613, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.257, \"our_mmol_l_h\": 4.045, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.93, \"viable_cell_density_million_ml\": 11.895}, {\"agitation_rpm\": 238.908, \"airflow_vvm\": 0.24, \"cer_mmol_l_h\": 4.626, \"dissolved_oxygen_pct\": 38.697, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.511, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.269, \"our_mmol_l_h\": 5.009, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.739, \"viable_cell_density_million_ml\": 14.777}, {\"agitation_rpm\": 280.497, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 6.113, \"dissolved_oxygen_pct\": 27.951, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.351, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.2, \"our_mmol_l_h\": 6.604, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.832, \"viable_cell_density_million_ml\": 19.432}, {\"agitation_rpm\": 286.188, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 8.006, \"dissolved_oxygen_pct\": 18.051, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.339, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 8.712, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.876, \"viable_cell_density_million_ml\": 25.5}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-823c8f04bd0f3a003547", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.485, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.196, \"dissolved_oxygen_pct\": 40.824, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.436, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 3.477, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.576, \"viable_cell_density_million_ml\": 10.245}, {\"agitation_rpm\": 217.209, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.924, \"dissolved_oxygen_pct\": 40.184, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.193, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 4.218, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.575, \"viable_cell_density_million_ml\": 12.467}, {\"agitation_rpm\": 246.749, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.914, \"dissolved_oxygen_pct\": 37.498, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.126, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 4.8, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 15.209}, {\"agitation_rpm\": 278.648, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 5.999, \"dissolved_oxygen_pct\": 36.214, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.773, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.826, \"our_mmol_l_h\": 5.352, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.437, \"viable_cell_density_million_ml\": 18.208}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4fa242e8c96ed767d8b8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.485, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.196, \"dissolved_oxygen_pct\": 40.824, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.436, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 3.477, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.576, \"viable_cell_density_million_ml\": 10.245}, {\"agitation_rpm\": 217.209, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.924, \"dissolved_oxygen_pct\": 40.184, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.193, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 4.218, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.575, \"viable_cell_density_million_ml\": 12.467}, {\"agitation_rpm\": 246.749, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.914, \"dissolved_oxygen_pct\": 37.498, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.126, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 4.8, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 15.209}, {\"agitation_rpm\": 278.648, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 5.999, \"dissolved_oxygen_pct\": 36.214, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.773, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.826, \"our_mmol_l_h\": 5.352, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.437, \"viable_cell_density_million_ml\": 18.208}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8505a7c72e360974248a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.485, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.196, \"dissolved_oxygen_pct\": 40.824, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.436, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 3.477, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.576, \"viable_cell_density_million_ml\": 10.245}, {\"agitation_rpm\": 217.209, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.924, \"dissolved_oxygen_pct\": 40.184, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.193, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 4.218, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.575, \"viable_cell_density_million_ml\": 12.467}, {\"agitation_rpm\": 246.749, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.914, \"dissolved_oxygen_pct\": 37.498, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.126, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 4.8, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 15.209}, {\"agitation_rpm\": 278.648, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 5.999, \"dissolved_oxygen_pct\": 36.214, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.773, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.826, \"our_mmol_l_h\": 5.352, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.437, \"viable_cell_density_million_ml\": 18.208}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7b4e640af9f9b3b1ffb3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.485, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.196, \"dissolved_oxygen_pct\": 40.824, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.436, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 3.477, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.803, \"viability_pct\": 98.576, \"viable_cell_density_million_ml\": 10.245}, {\"agitation_rpm\": 217.209, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.924, \"dissolved_oxygen_pct\": 40.184, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.193, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.909, \"our_mmol_l_h\": 4.218, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.82, \"viability_pct\": 98.575, \"viable_cell_density_million_ml\": 12.467}, {\"agitation_rpm\": 246.749, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.914, \"dissolved_oxygen_pct\": 37.498, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.126, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 4.8, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.443, \"viable_cell_density_million_ml\": 15.209}, {\"agitation_rpm\": 278.648, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 5.999, \"dissolved_oxygen_pct\": 36.214, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.773, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.826, \"our_mmol_l_h\": 5.352, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.786, \"viability_pct\": 98.437, \"viable_cell_density_million_ml\": 18.208}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-47e13cb1dd82beba48e7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.445, \"airflow_vvm\": 0.227, \"cer_mmol_l_h\": 4.227, \"dissolved_oxygen_pct\": 38.866, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.173, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.031, \"our_mmol_l_h\": 4.623, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.804, \"viability_pct\": 98.404, \"viable_cell_density_million_ml\": 13.605}, {\"agitation_rpm\": 245.511, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.918, \"dissolved_oxygen_pct\": 38.199, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.127, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.969, \"our_mmol_l_h\": 5.294, \"ph\": 6.954, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.378, \"viable_cell_density_million_ml\": 15.579}, {\"agitation_rpm\": 270.7, \"airflow_vvm\": 0.229, \"cer_mmol_l_h\": 5.733, \"dissolved_oxygen_pct\": 28.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.019, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.866, \"our_mmol_l_h\": 6.232, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.498, \"viable_cell_density_million_ml\": 18.362}, {\"agitation_rpm\": 285.233, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.589, \"dissolved_oxygen_pct\": 18.907, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.923, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.677, \"our_mmol_l_h\": 7.178, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.474, \"viable_cell_density_million_ml\": 21.145}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8da828f4ff74aef1825d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.445, \"airflow_vvm\": 0.227, \"cer_mmol_l_h\": 4.227, \"dissolved_oxygen_pct\": 38.866, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.173, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.031, \"our_mmol_l_h\": 4.623, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.804, \"viability_pct\": 98.404, \"viable_cell_density_million_ml\": 13.605}, {\"agitation_rpm\": 245.511, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.918, \"dissolved_oxygen_pct\": 38.199, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.127, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.969, \"our_mmol_l_h\": 5.294, \"ph\": 6.954, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.378, \"viable_cell_density_million_ml\": 15.579}, {\"agitation_rpm\": 270.7, \"airflow_vvm\": 0.229, \"cer_mmol_l_h\": 5.733, \"dissolved_oxygen_pct\": 28.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.019, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.866, \"our_mmol_l_h\": 6.232, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.498, \"viable_cell_density_million_ml\": 18.362}, {\"agitation_rpm\": 285.233, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.589, \"dissolved_oxygen_pct\": 18.907, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.923, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.677, \"our_mmol_l_h\": 7.178, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.474, \"viable_cell_density_million_ml\": 21.145}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-35fbdbdedc7d2d21f2c6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.445, \"airflow_vvm\": 0.227, \"cer_mmol_l_h\": 4.227, \"dissolved_oxygen_pct\": 38.866, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.173, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.031, \"our_mmol_l_h\": 4.623, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.804, \"viability_pct\": 98.404, \"viable_cell_density_million_ml\": 13.605}, {\"agitation_rpm\": 245.511, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.918, \"dissolved_oxygen_pct\": 38.199, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.127, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.969, \"our_mmol_l_h\": 5.294, \"ph\": 6.954, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.378, \"viable_cell_density_million_ml\": 15.579}, {\"agitation_rpm\": 270.7, \"airflow_vvm\": 0.229, \"cer_mmol_l_h\": 5.733, \"dissolved_oxygen_pct\": 28.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.019, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.866, \"our_mmol_l_h\": 6.232, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.498, \"viable_cell_density_million_ml\": 18.362}, {\"agitation_rpm\": 285.233, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.589, \"dissolved_oxygen_pct\": 18.907, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.923, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.677, \"our_mmol_l_h\": 7.178, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.474, \"viable_cell_density_million_ml\": 21.145}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2420f5e3cc7ec8a9490e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 227.445, \"airflow_vvm\": 0.227, \"cer_mmol_l_h\": 4.227, \"dissolved_oxygen_pct\": 38.866, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.173, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.031, \"our_mmol_l_h\": 4.623, \"ph\": 6.958, \"phase\": \"exponential\", \"temperature_c\": 36.804, \"viability_pct\": 98.404, \"viable_cell_density_million_ml\": 13.605}, {\"agitation_rpm\": 245.511, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.918, \"dissolved_oxygen_pct\": 38.199, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.127, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.969, \"our_mmol_l_h\": 5.294, \"ph\": 6.954, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.378, \"viable_cell_density_million_ml\": 15.579}, {\"agitation_rpm\": 270.7, \"airflow_vvm\": 0.229, \"cer_mmol_l_h\": 5.733, \"dissolved_oxygen_pct\": 28.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.019, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.866, \"our_mmol_l_h\": 6.232, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.498, \"viable_cell_density_million_ml\": 18.362}, {\"agitation_rpm\": 285.233, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 6.589, \"dissolved_oxygen_pct\": 18.907, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.923, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.677, \"our_mmol_l_h\": 7.178, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.474, \"viable_cell_density_million_ml\": 21.145}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-24e6a319b1f86968e844", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.812, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.977, \"dissolved_oxygen_pct\": 36.33, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.38, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.144, \"our_mmol_l_h\": 8.673, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.781, \"viable_cell_density_million_ml\": 25.554}, {\"agitation_rpm\": 285.802, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.849, \"dissolved_oxygen_pct\": 36.063, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.425, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.102, \"our_mmol_l_h\": 9.606, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.771, \"viable_cell_density_million_ml\": 28.3}, {\"agitation_rpm\": 284.29, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.874, \"dissolved_oxygen_pct\": 36.051, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.593, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.069, \"our_mmol_l_h\": 10.196, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.706, \"viable_cell_density_million_ml\": 31.075}, {\"agitation_rpm\": 285.862, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.818, \"dissolved_oxygen_pct\": 36.919, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.507, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.975, \"our_mmol_l_h\": 10.53, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.763, \"viable_cell_density_million_ml\": 33.521}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b685a405d3df5d4d0abb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.812, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.977, \"dissolved_oxygen_pct\": 36.33, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.38, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.144, \"our_mmol_l_h\": 8.673, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.781, \"viable_cell_density_million_ml\": 25.554}, {\"agitation_rpm\": 285.802, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.849, \"dissolved_oxygen_pct\": 36.063, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.425, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.102, \"our_mmol_l_h\": 9.606, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.771, \"viable_cell_density_million_ml\": 28.3}, {\"agitation_rpm\": 284.29, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.874, \"dissolved_oxygen_pct\": 36.051, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.593, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.069, \"our_mmol_l_h\": 10.196, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.706, \"viable_cell_density_million_ml\": 31.075}, {\"agitation_rpm\": 285.862, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.818, \"dissolved_oxygen_pct\": 36.919, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.507, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.975, \"our_mmol_l_h\": 10.53, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.763, \"viable_cell_density_million_ml\": 33.521}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-430387b375d77c1e875c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.812, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.977, \"dissolved_oxygen_pct\": 36.33, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.38, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.144, \"our_mmol_l_h\": 8.673, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.781, \"viable_cell_density_million_ml\": 25.554}, {\"agitation_rpm\": 285.802, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.849, \"dissolved_oxygen_pct\": 36.063, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.425, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.102, \"our_mmol_l_h\": 9.606, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.771, \"viable_cell_density_million_ml\": 28.3}, {\"agitation_rpm\": 284.29, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.874, \"dissolved_oxygen_pct\": 36.051, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.593, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.069, \"our_mmol_l_h\": 10.196, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.706, \"viable_cell_density_million_ml\": 31.075}, {\"agitation_rpm\": 285.862, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.818, \"dissolved_oxygen_pct\": 36.919, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.507, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.975, \"our_mmol_l_h\": 10.53, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.763, \"viable_cell_density_million_ml\": 33.521}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4fbc54f1aea267fafca6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.812, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.977, \"dissolved_oxygen_pct\": 36.33, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.38, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.144, \"our_mmol_l_h\": 8.673, \"ph\": 6.993, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.781, \"viable_cell_density_million_ml\": 25.554}, {\"agitation_rpm\": 285.802, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.849, \"dissolved_oxygen_pct\": 36.063, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.425, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.102, \"our_mmol_l_h\": 9.606, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.771, \"viable_cell_density_million_ml\": 28.3}, {\"agitation_rpm\": 284.29, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.874, \"dissolved_oxygen_pct\": 36.051, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.593, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.069, \"our_mmol_l_h\": 10.196, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.706, \"viable_cell_density_million_ml\": 31.075}, {\"agitation_rpm\": 285.862, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 10.818, \"dissolved_oxygen_pct\": 36.919, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.507, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 0.975, \"our_mmol_l_h\": 10.53, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.763, \"viable_cell_density_million_ml\": 33.521}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a426f8b665a6af18d9ae", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.706, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.966, \"dissolved_oxygen_pct\": 35.836, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.375, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.136, \"our_mmol_l_h\": 8.674, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 25.496}, {\"agitation_rpm\": 285.226, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.803, \"dissolved_oxygen_pct\": 35.839, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.414, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 9.615, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.68, \"viable_cell_density_million_ml\": 28.281}, {\"agitation_rpm\": 284.731, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 9.886, \"dissolved_oxygen_pct\": 28.54, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.516, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.054, \"our_mmol_l_h\": 10.729, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.72, \"viable_cell_density_million_ml\": 31.444}, {\"agitation_rpm\": 286.027, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.835, \"dissolved_oxygen_pct\": 18.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.743, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.017, \"our_mmol_l_h\": 11.761, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.91, \"viable_cell_density_million_ml\": 34.527}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3f467bcb5527b2aba14c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.706, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.966, \"dissolved_oxygen_pct\": 35.836, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.375, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.136, \"our_mmol_l_h\": 8.674, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 25.496}, {\"agitation_rpm\": 285.226, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.803, \"dissolved_oxygen_pct\": 35.839, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.414, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 9.615, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.68, \"viable_cell_density_million_ml\": 28.281}, {\"agitation_rpm\": 284.731, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 9.886, \"dissolved_oxygen_pct\": 28.54, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.516, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.054, \"our_mmol_l_h\": 10.729, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.72, \"viable_cell_density_million_ml\": 31.444}, {\"agitation_rpm\": 286.027, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.835, \"dissolved_oxygen_pct\": 18.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.743, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.017, \"our_mmol_l_h\": 11.761, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.91, \"viable_cell_density_million_ml\": 34.527}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-45f489a50f68c559ceca", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.706, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.966, \"dissolved_oxygen_pct\": 35.836, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.375, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.136, \"our_mmol_l_h\": 8.674, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 25.496}, {\"agitation_rpm\": 285.226, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.803, \"dissolved_oxygen_pct\": 35.839, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.414, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 9.615, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.68, \"viable_cell_density_million_ml\": 28.281}, {\"agitation_rpm\": 284.731, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 9.886, \"dissolved_oxygen_pct\": 28.54, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.516, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.054, \"our_mmol_l_h\": 10.729, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.72, \"viable_cell_density_million_ml\": 31.444}, {\"agitation_rpm\": 286.027, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.835, \"dissolved_oxygen_pct\": 18.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.743, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.017, \"our_mmol_l_h\": 11.761, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.91, \"viable_cell_density_million_ml\": 34.527}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3be8f326c28a60a952af", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 284.706, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.966, \"dissolved_oxygen_pct\": 35.836, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.375, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.136, \"our_mmol_l_h\": 8.674, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.78, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 25.496}, {\"agitation_rpm\": 285.226, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 8.803, \"dissolved_oxygen_pct\": 35.839, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.414, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.129, \"our_mmol_l_h\": 9.615, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.68, \"viable_cell_density_million_ml\": 28.281}, {\"agitation_rpm\": 284.731, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 9.886, \"dissolved_oxygen_pct\": 28.54, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.516, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.054, \"our_mmol_l_h\": 10.729, \"ph\": 7.008, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.72, \"viable_cell_density_million_ml\": 31.444}, {\"agitation_rpm\": 286.027, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.835, \"dissolved_oxygen_pct\": 18.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.743, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.017, \"our_mmol_l_h\": 11.761, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.809, \"viability_pct\": 98.91, \"viable_cell_density_million_ml\": 34.527}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4d238568e56ba05c9e37", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.297, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 5.984, \"dissolved_oxygen_pct\": 35.95, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.033, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.83, \"our_mmol_l_h\": 6.554, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.399, \"viable_cell_density_million_ml\": 19.154}, {\"agitation_rpm\": 284.871, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 36.252, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.935, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.744, \"our_mmol_l_h\": 6.982, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 20.48}, {\"agitation_rpm\": 284.905, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.869, \"dissolved_oxygen_pct\": 36.146, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.96, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.617, \"our_mmol_l_h\": 6.982, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.474, \"viable_cell_density_million_ml\": 21.667}, {\"agitation_rpm\": 284.641, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.204, \"dissolved_oxygen_pct\": 36.274, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.636, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.469, \"our_mmol_l_h\": 6.62, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 96.625, \"viable_cell_density_million_ml\": 22.054}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-061ef52d588759a3cc06", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.297, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 5.984, \"dissolved_oxygen_pct\": 35.95, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.033, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.83, \"our_mmol_l_h\": 6.554, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.399, \"viable_cell_density_million_ml\": 19.154}, {\"agitation_rpm\": 284.871, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 36.252, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.935, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.744, \"our_mmol_l_h\": 6.982, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 20.48}, {\"agitation_rpm\": 284.905, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.869, \"dissolved_oxygen_pct\": 36.146, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.96, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.617, \"our_mmol_l_h\": 6.982, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.474, \"viable_cell_density_million_ml\": 21.667}, {\"agitation_rpm\": 284.641, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.204, \"dissolved_oxygen_pct\": 36.274, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.636, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.469, \"our_mmol_l_h\": 6.62, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 96.625, \"viable_cell_density_million_ml\": 22.054}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-59e79be1c2db2cfa1674", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.297, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 5.984, \"dissolved_oxygen_pct\": 35.95, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.033, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.83, \"our_mmol_l_h\": 6.554, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.399, \"viable_cell_density_million_ml\": 19.154}, {\"agitation_rpm\": 284.871, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 36.252, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.935, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.744, \"our_mmol_l_h\": 6.982, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 20.48}, {\"agitation_rpm\": 284.905, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.869, \"dissolved_oxygen_pct\": 36.146, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.96, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.617, \"our_mmol_l_h\": 6.982, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.474, \"viable_cell_density_million_ml\": 21.667}, {\"agitation_rpm\": 284.641, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.204, \"dissolved_oxygen_pct\": 36.274, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.636, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.469, \"our_mmol_l_h\": 6.62, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 96.625, \"viable_cell_density_million_ml\": 22.054}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-621d8dbdf5cb676eb8c3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.297, \"airflow_vvm\": 0.289, \"cer_mmol_l_h\": 5.984, \"dissolved_oxygen_pct\": 35.95, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.033, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.83, \"our_mmol_l_h\": 6.554, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.399, \"viable_cell_density_million_ml\": 19.154}, {\"agitation_rpm\": 284.871, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 36.252, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.935, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.744, \"our_mmol_l_h\": 6.982, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 20.48}, {\"agitation_rpm\": 284.905, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 6.869, \"dissolved_oxygen_pct\": 36.146, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.96, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.617, \"our_mmol_l_h\": 6.982, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.474, \"viable_cell_density_million_ml\": 21.667}, {\"agitation_rpm\": 284.641, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.204, \"dissolved_oxygen_pct\": 36.274, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.636, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.469, \"our_mmol_l_h\": 6.62, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.807, \"viability_pct\": 96.625, \"viable_cell_density_million_ml\": 22.054}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-57121e617f8c8eb17009", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.879, \"airflow_vvm\": 0.263, \"cer_mmol_l_h\": 5.188, \"dissolved_oxygen_pct\": 36.82, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.08, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.898, \"our_mmol_l_h\": 5.642, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.78, \"viability_pct\": 98.383, \"viable_cell_density_million_ml\": 16.605}, {\"agitation_rpm\": 269.911, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.765, \"dissolved_oxygen_pct\": 36.825, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.06, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.832, \"our_mmol_l_h\": 6.25, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 18.365}, {\"agitation_rpm\": 285.121, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 28.627, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.004, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.714, \"our_mmol_l_h\": 6.99, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.629, \"viable_cell_density_million_ml\": 20.461}, {\"agitation_rpm\": 285.192, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 6.993, \"dissolved_oxygen_pct\": 18.509, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.845, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.572, \"our_mmol_l_h\": 7.586, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 97.894, \"viable_cell_density_million_ml\": 22.45}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d833dc4036081452dedf", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.879, \"airflow_vvm\": 0.263, \"cer_mmol_l_h\": 5.188, \"dissolved_oxygen_pct\": 36.82, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.08, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.898, \"our_mmol_l_h\": 5.642, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.78, \"viability_pct\": 98.383, \"viable_cell_density_million_ml\": 16.605}, {\"agitation_rpm\": 269.911, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.765, \"dissolved_oxygen_pct\": 36.825, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.06, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.832, \"our_mmol_l_h\": 6.25, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 18.365}, {\"agitation_rpm\": 285.121, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 28.627, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.004, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.714, \"our_mmol_l_h\": 6.99, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.629, \"viable_cell_density_million_ml\": 20.461}, {\"agitation_rpm\": 285.192, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 6.993, \"dissolved_oxygen_pct\": 18.509, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.845, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.572, \"our_mmol_l_h\": 7.586, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 97.894, \"viable_cell_density_million_ml\": 22.45}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-19cce75c7bdbc72916c2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.879, \"airflow_vvm\": 0.263, \"cer_mmol_l_h\": 5.188, \"dissolved_oxygen_pct\": 36.82, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.08, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.898, \"our_mmol_l_h\": 5.642, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.78, \"viability_pct\": 98.383, \"viable_cell_density_million_ml\": 16.605}, {\"agitation_rpm\": 269.911, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.765, \"dissolved_oxygen_pct\": 36.825, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.06, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.832, \"our_mmol_l_h\": 6.25, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 18.365}, {\"agitation_rpm\": 285.121, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 28.627, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.004, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.714, \"our_mmol_l_h\": 6.99, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.629, \"viable_cell_density_million_ml\": 20.461}, {\"agitation_rpm\": 285.192, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 6.993, \"dissolved_oxygen_pct\": 18.509, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.845, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.572, \"our_mmol_l_h\": 7.586, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 97.894, \"viable_cell_density_million_ml\": 22.45}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e5fd2648299160726a4a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.879, \"airflow_vvm\": 0.263, \"cer_mmol_l_h\": 5.188, \"dissolved_oxygen_pct\": 36.82, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.08, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.898, \"our_mmol_l_h\": 5.642, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.78, \"viability_pct\": 98.383, \"viable_cell_density_million_ml\": 16.605}, {\"agitation_rpm\": 269.911, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.765, \"dissolved_oxygen_pct\": 36.825, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.06, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.832, \"our_mmol_l_h\": 6.25, \"ph\": 6.962, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 18.365}, {\"agitation_rpm\": 285.121, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.422, \"dissolved_oxygen_pct\": 28.627, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.004, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.714, \"our_mmol_l_h\": 6.99, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.815, \"viability_pct\": 98.629, \"viable_cell_density_million_ml\": 20.461}, {\"agitation_rpm\": 285.192, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 6.993, \"dissolved_oxygen_pct\": 18.509, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.845, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.572, \"our_mmol_l_h\": 7.586, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 97.894, \"viable_cell_density_million_ml\": 22.45}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1e2573a3f140e3b19e6d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.388, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.114, \"dissolved_oxygen_pct\": 37.59, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.459, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.557, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.815, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 16.339}, {\"agitation_rpm\": 279.562, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.086, \"dissolved_oxygen_pct\": 36.776, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.374, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.222, \"our_mmol_l_h\": 6.639, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.735, \"viable_cell_density_million_ml\": 19.385}, {\"agitation_rpm\": 286.103, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.53, \"dissolved_oxygen_pct\": 36.691, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.415, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.138, \"our_mmol_l_h\": 7.661, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 23.677}, {\"agitation_rpm\": 284.846, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.177, \"dissolved_oxygen_pct\": 36.675, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.211, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 8.826, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.937, \"viable_cell_density_million_ml\": 28.348}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1eebc09d154b537ad4f7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.388, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.114, \"dissolved_oxygen_pct\": 37.59, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.459, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.557, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.815, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 16.339}, {\"agitation_rpm\": 279.562, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.086, \"dissolved_oxygen_pct\": 36.776, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.374, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.222, \"our_mmol_l_h\": 6.639, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.735, \"viable_cell_density_million_ml\": 19.385}, {\"agitation_rpm\": 286.103, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.53, \"dissolved_oxygen_pct\": 36.691, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.415, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.138, \"our_mmol_l_h\": 7.661, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 23.677}, {\"agitation_rpm\": 284.846, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.177, \"dissolved_oxygen_pct\": 36.675, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.211, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 8.826, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.937, \"viable_cell_density_million_ml\": 28.348}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6125b64acffd4073a572", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.388, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.114, \"dissolved_oxygen_pct\": 37.59, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.459, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.557, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.815, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 16.339}, {\"agitation_rpm\": 279.562, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.086, \"dissolved_oxygen_pct\": 36.776, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.374, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.222, \"our_mmol_l_h\": 6.639, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.735, \"viable_cell_density_million_ml\": 19.385}, {\"agitation_rpm\": 286.103, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.53, \"dissolved_oxygen_pct\": 36.691, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.415, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.138, \"our_mmol_l_h\": 7.661, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 23.677}, {\"agitation_rpm\": 284.846, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.177, \"dissolved_oxygen_pct\": 36.675, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.211, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 8.826, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.937, \"viable_cell_density_million_ml\": 28.348}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3893a2be1619a22dffb9", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.388, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.114, \"dissolved_oxygen_pct\": 37.59, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.459, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.557, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.815, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 16.339}, {\"agitation_rpm\": 279.562, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.086, \"dissolved_oxygen_pct\": 36.776, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.374, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.222, \"our_mmol_l_h\": 6.639, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.735, \"viable_cell_density_million_ml\": 19.385}, {\"agitation_rpm\": 286.103, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.53, \"dissolved_oxygen_pct\": 36.691, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.415, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.138, \"our_mmol_l_h\": 7.661, \"ph\": 7.006, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.862, \"viable_cell_density_million_ml\": 23.677}, {\"agitation_rpm\": 284.846, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.177, \"dissolved_oxygen_pct\": 36.675, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.211, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 8.826, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.937, \"viable_cell_density_million_ml\": 28.348}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0ca957831189f5cd4a9d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.431, \"airflow_vvm\": 0.259, \"cer_mmol_l_h\": 5.077, \"dissolved_oxygen_pct\": 37.565, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.41, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.242, \"our_mmol_l_h\": 5.555, \"ph\": 6.991, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.692, \"viable_cell_density_million_ml\": 16.383}, {\"agitation_rpm\": 279.82, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.082, \"dissolved_oxygen_pct\": 36.55, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.352, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.185, \"our_mmol_l_h\": 6.616, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.703, \"viable_cell_density_million_ml\": 19.489}, {\"agitation_rpm\": 285.285, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.527, \"dissolved_oxygen_pct\": 28.638, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.379, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.169, \"our_mmol_l_h\": 8.206, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.878, \"viable_cell_density_million_ml\": 24.124}, {\"agitation_rpm\": 285.696, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 9.197, \"dissolved_oxygen_pct\": 17.959, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.434, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.067, \"our_mmol_l_h\": 10.007, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 29.36}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7c26d318ece44e2294cc", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.431, \"airflow_vvm\": 0.259, \"cer_mmol_l_h\": 5.077, \"dissolved_oxygen_pct\": 37.565, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.41, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.242, \"our_mmol_l_h\": 5.555, \"ph\": 6.991, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.692, \"viable_cell_density_million_ml\": 16.383}, {\"agitation_rpm\": 279.82, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.082, \"dissolved_oxygen_pct\": 36.55, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.352, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.185, \"our_mmol_l_h\": 6.616, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.703, \"viable_cell_density_million_ml\": 19.489}, {\"agitation_rpm\": 285.285, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.527, \"dissolved_oxygen_pct\": 28.638, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.379, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.169, \"our_mmol_l_h\": 8.206, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.878, \"viable_cell_density_million_ml\": 24.124}, {\"agitation_rpm\": 285.696, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 9.197, \"dissolved_oxygen_pct\": 17.959, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.434, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.067, \"our_mmol_l_h\": 10.007, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 29.36}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-28f0ecd22c796e06d378", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.431, \"airflow_vvm\": 0.259, \"cer_mmol_l_h\": 5.077, \"dissolved_oxygen_pct\": 37.565, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.41, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.242, \"our_mmol_l_h\": 5.555, \"ph\": 6.991, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.692, \"viable_cell_density_million_ml\": 16.383}, {\"agitation_rpm\": 279.82, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.082, \"dissolved_oxygen_pct\": 36.55, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.352, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.185, \"our_mmol_l_h\": 6.616, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.703, \"viable_cell_density_million_ml\": 19.489}, {\"agitation_rpm\": 285.285, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.527, \"dissolved_oxygen_pct\": 28.638, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.379, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.169, \"our_mmol_l_h\": 8.206, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.878, \"viable_cell_density_million_ml\": 24.124}, {\"agitation_rpm\": 285.696, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 9.197, \"dissolved_oxygen_pct\": 17.959, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.434, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.067, \"our_mmol_l_h\": 10.007, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 29.36}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9ba34a4826a6a64b49c0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.431, \"airflow_vvm\": 0.259, \"cer_mmol_l_h\": 5.077, \"dissolved_oxygen_pct\": 37.565, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.41, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.242, \"our_mmol_l_h\": 5.555, \"ph\": 6.991, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.692, \"viable_cell_density_million_ml\": 16.383}, {\"agitation_rpm\": 279.82, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.082, \"dissolved_oxygen_pct\": 36.55, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.352, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.185, \"our_mmol_l_h\": 6.616, \"ph\": 6.994, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.703, \"viable_cell_density_million_ml\": 19.489}, {\"agitation_rpm\": 285.285, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 7.527, \"dissolved_oxygen_pct\": 28.638, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.379, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.169, \"our_mmol_l_h\": 8.206, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.821, \"viability_pct\": 98.878, \"viable_cell_density_million_ml\": 24.124}, {\"agitation_rpm\": 285.696, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 9.197, \"dissolved_oxygen_pct\": 17.959, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.434, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.067, \"our_mmol_l_h\": 10.007, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.824, \"viability_pct\": 98.875, \"viable_cell_density_million_ml\": 29.36}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2ec8cfc7b8a343462ada", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.917, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 5.991, \"dissolved_oxygen_pct\": 35.731, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.02, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.784, \"our_mmol_l_h\": 6.503, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.399, \"viable_cell_density_million_ml\": 19.161}, {\"agitation_rpm\": 284.865, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.405, \"dissolved_oxygen_pct\": 35.778, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.937, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.739, \"our_mmol_l_h\": 6.984, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.481, \"viable_cell_density_million_ml\": 20.509}, {\"agitation_rpm\": 283.954, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.905, \"dissolved_oxygen_pct\": 36.828, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.933, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.601, \"our_mmol_l_h\": 6.953, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.441, \"viable_cell_density_million_ml\": 21.646}, {\"agitation_rpm\": 285.609, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.189, \"dissolved_oxygen_pct\": 35.751, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.622, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.503, \"our_mmol_l_h\": 6.618, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 96.622, \"viable_cell_density_million_ml\": 21.913}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-002fbc619ebff93084c2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.917, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 5.991, \"dissolved_oxygen_pct\": 35.731, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.02, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.784, \"our_mmol_l_h\": 6.503, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.399, \"viable_cell_density_million_ml\": 19.161}, {\"agitation_rpm\": 284.865, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.405, \"dissolved_oxygen_pct\": 35.778, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.937, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.739, \"our_mmol_l_h\": 6.984, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.481, \"viable_cell_density_million_ml\": 20.509}, {\"agitation_rpm\": 283.954, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.905, \"dissolved_oxygen_pct\": 36.828, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.933, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.601, \"our_mmol_l_h\": 6.953, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.441, \"viable_cell_density_million_ml\": 21.646}, {\"agitation_rpm\": 285.609, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.189, \"dissolved_oxygen_pct\": 35.751, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.622, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.503, \"our_mmol_l_h\": 6.618, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 96.622, \"viable_cell_density_million_ml\": 21.913}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-69de098afc52e84b0e91", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.917, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 5.991, \"dissolved_oxygen_pct\": 35.731, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.02, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.784, \"our_mmol_l_h\": 6.503, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.399, \"viable_cell_density_million_ml\": 19.161}, {\"agitation_rpm\": 284.865, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.405, \"dissolved_oxygen_pct\": 35.778, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.937, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.739, \"our_mmol_l_h\": 6.984, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.481, \"viable_cell_density_million_ml\": 20.509}, {\"agitation_rpm\": 283.954, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.905, \"dissolved_oxygen_pct\": 36.828, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.933, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.601, \"our_mmol_l_h\": 6.953, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.441, \"viable_cell_density_million_ml\": 21.646}, {\"agitation_rpm\": 285.609, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.189, \"dissolved_oxygen_pct\": 35.751, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.622, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.503, \"our_mmol_l_h\": 6.618, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 96.622, \"viable_cell_density_million_ml\": 21.913}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-20bae22b4f659a1b8016", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.917, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 5.991, \"dissolved_oxygen_pct\": 35.731, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.02, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.784, \"our_mmol_l_h\": 6.503, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.399, \"viable_cell_density_million_ml\": 19.161}, {\"agitation_rpm\": 284.865, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.405, \"dissolved_oxygen_pct\": 35.778, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.937, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.739, \"our_mmol_l_h\": 6.984, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.481, \"viable_cell_density_million_ml\": 20.509}, {\"agitation_rpm\": 283.954, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.905, \"dissolved_oxygen_pct\": 36.828, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.933, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.601, \"our_mmol_l_h\": 6.953, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.441, \"viable_cell_density_million_ml\": 21.646}, {\"agitation_rpm\": 285.609, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 7.189, \"dissolved_oxygen_pct\": 35.751, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.622, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.503, \"our_mmol_l_h\": 6.618, \"ph\": 6.989, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 96.622, \"viable_cell_density_million_ml\": 21.913}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-723347a44bec85901615", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.95, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 5.993, \"dissolved_oxygen_pct\": 35.996, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.995, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.816, \"our_mmol_l_h\": 6.504, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.524, \"viable_cell_density_million_ml\": 19.148}, {\"agitation_rpm\": 284.931, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.432, \"dissolved_oxygen_pct\": 36.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.96, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.713, \"our_mmol_l_h\": 7.008, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.404, \"viable_cell_density_million_ml\": 20.472}, {\"agitation_rpm\": 285.741, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 29.182, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.918, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.637, \"our_mmol_l_h\": 7.479, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.448, \"viable_cell_density_million_ml\": 21.982}, {\"agitation_rpm\": 284.591, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.218, \"dissolved_oxygen_pct\": 18.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.782, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.482, \"our_mmol_l_h\": 7.803, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 96.623, \"viable_cell_density_million_ml\": 22.921}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-40de412097426285cb21", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.95, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 5.993, \"dissolved_oxygen_pct\": 35.996, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.995, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.816, \"our_mmol_l_h\": 6.504, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.524, \"viable_cell_density_million_ml\": 19.148}, {\"agitation_rpm\": 284.931, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.432, \"dissolved_oxygen_pct\": 36.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.96, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.713, \"our_mmol_l_h\": 7.008, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.404, \"viable_cell_density_million_ml\": 20.472}, {\"agitation_rpm\": 285.741, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 29.182, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.918, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.637, \"our_mmol_l_h\": 7.479, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.448, \"viable_cell_density_million_ml\": 21.982}, {\"agitation_rpm\": 284.591, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.218, \"dissolved_oxygen_pct\": 18.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.782, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.482, \"our_mmol_l_h\": 7.803, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 96.623, \"viable_cell_density_million_ml\": 22.921}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-681c9c843d03369ffd94", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.95, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 5.993, \"dissolved_oxygen_pct\": 35.996, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.995, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.816, \"our_mmol_l_h\": 6.504, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.524, \"viable_cell_density_million_ml\": 19.148}, {\"agitation_rpm\": 284.931, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.432, \"dissolved_oxygen_pct\": 36.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.96, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.713, \"our_mmol_l_h\": 7.008, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.404, \"viable_cell_density_million_ml\": 20.472}, {\"agitation_rpm\": 285.741, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 29.182, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.918, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.637, \"our_mmol_l_h\": 7.479, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.448, \"viable_cell_density_million_ml\": 21.982}, {\"agitation_rpm\": 284.591, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.218, \"dissolved_oxygen_pct\": 18.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.782, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.482, \"our_mmol_l_h\": 7.803, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 96.623, \"viable_cell_density_million_ml\": 22.921}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7f6e5b62b0f958ce1318", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.95, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 5.993, \"dissolved_oxygen_pct\": 35.996, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.995, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.816, \"our_mmol_l_h\": 6.504, \"ph\": 6.967, \"phase\": \"production\", \"temperature_c\": 35.776, \"viability_pct\": 98.524, \"viable_cell_density_million_ml\": 19.148}, {\"agitation_rpm\": 284.931, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.432, \"dissolved_oxygen_pct\": 36.745, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.96, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.713, \"our_mmol_l_h\": 7.008, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.404, \"viable_cell_density_million_ml\": 20.472}, {\"agitation_rpm\": 285.741, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 29.182, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.918, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.637, \"our_mmol_l_h\": 7.479, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.787, \"viability_pct\": 98.448, \"viable_cell_density_million_ml\": 21.982}, {\"agitation_rpm\": 284.591, \"airflow_vvm\": 0.183, \"cer_mmol_l_h\": 7.218, \"dissolved_oxygen_pct\": 18.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.782, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.482, \"our_mmol_l_h\": 7.803, \"ph\": 6.986, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 96.623, \"viable_cell_density_million_ml\": 22.921}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-872176219dd4bba91b71", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.443, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.727, \"dissolved_oxygen_pct\": 40.683, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.621, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.227, \"our_mmol_l_h\": 4.09, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.786, \"viable_cell_density_million_ml\": 11.975}, {\"agitation_rpm\": 237.739, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 4.621, \"dissolved_oxygen_pct\": 38.801, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.463, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.024, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.852, \"viable_cell_density_million_ml\": 14.739}, {\"agitation_rpm\": 279.108, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.058, \"dissolved_oxygen_pct\": 36.909, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.477, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.187, \"our_mmol_l_h\": 6.082, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.742, \"viable_cell_density_million_ml\": 19.1}, {\"agitation_rpm\": 284.292, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.997, \"dissolved_oxygen_pct\": 36.547, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.135, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.115, \"our_mmol_l_h\": 7.492, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 24.603}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1353c12e6960ae1e00de", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.443, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.727, \"dissolved_oxygen_pct\": 40.683, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.621, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.227, \"our_mmol_l_h\": 4.09, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.786, \"viable_cell_density_million_ml\": 11.975}, {\"agitation_rpm\": 237.739, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 4.621, \"dissolved_oxygen_pct\": 38.801, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.463, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.024, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.852, \"viable_cell_density_million_ml\": 14.739}, {\"agitation_rpm\": 279.108, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.058, \"dissolved_oxygen_pct\": 36.909, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.477, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.187, \"our_mmol_l_h\": 6.082, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.742, \"viable_cell_density_million_ml\": 19.1}, {\"agitation_rpm\": 284.292, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.997, \"dissolved_oxygen_pct\": 36.547, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.135, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.115, \"our_mmol_l_h\": 7.492, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 24.603}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-01f1f00639fb1266ce63", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.443, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.727, \"dissolved_oxygen_pct\": 40.683, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.621, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.227, \"our_mmol_l_h\": 4.09, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.786, \"viable_cell_density_million_ml\": 11.975}, {\"agitation_rpm\": 237.739, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 4.621, \"dissolved_oxygen_pct\": 38.801, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.463, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.024, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.852, \"viable_cell_density_million_ml\": 14.739}, {\"agitation_rpm\": 279.108, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.058, \"dissolved_oxygen_pct\": 36.909, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.477, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.187, \"our_mmol_l_h\": 6.082, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.742, \"viable_cell_density_million_ml\": 19.1}, {\"agitation_rpm\": 284.292, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.997, \"dissolved_oxygen_pct\": 36.547, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.135, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.115, \"our_mmol_l_h\": 7.492, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 24.603}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-aa7a2f18536511646bc0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.443, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.727, \"dissolved_oxygen_pct\": 40.683, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.621, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.227, \"our_mmol_l_h\": 4.09, \"ph\": 6.988, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.786, \"viable_cell_density_million_ml\": 11.975}, {\"agitation_rpm\": 237.739, \"airflow_vvm\": 0.241, \"cer_mmol_l_h\": 4.621, \"dissolved_oxygen_pct\": 38.801, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.463, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.232, \"our_mmol_l_h\": 5.024, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.852, \"viable_cell_density_million_ml\": 14.739}, {\"agitation_rpm\": 279.108, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.058, \"dissolved_oxygen_pct\": 36.909, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.477, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.187, \"our_mmol_l_h\": 6.082, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.796, \"viability_pct\": 98.742, \"viable_cell_density_million_ml\": 19.1}, {\"agitation_rpm\": 284.292, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.997, \"dissolved_oxygen_pct\": 36.547, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.135, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.115, \"our_mmol_l_h\": 7.492, \"ph\": 6.992, \"phase\": \"production\", \"temperature_c\": 35.788, \"viability_pct\": 98.657, \"viable_cell_density_million_ml\": 24.603}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e0ce2f3c379f4a28c5d8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.892, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.995, \"dissolved_oxygen_pct\": 35.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.374, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.709, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 25.587}, {\"agitation_rpm\": 286.001, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.821, \"dissolved_oxygen_pct\": 36.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.42, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.087, \"our_mmol_l_h\": 9.585, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.785, \"viable_cell_density_million_ml\": 28.158}, {\"agitation_rpm\": 285.912, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 9.87, \"dissolved_oxygen_pct\": 28.798, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.496, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.045, \"our_mmol_l_h\": 10.692, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.65, \"viable_cell_density_million_ml\": 31.465}, {\"agitation_rpm\": 284.451, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.776, \"dissolved_oxygen_pct\": 17.727, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.705, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.999, \"our_mmol_l_h\": 11.736, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.652, \"viable_cell_density_million_ml\": 34.572}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-01234b4a3b191981da20", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.892, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.995, \"dissolved_oxygen_pct\": 35.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.374, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.709, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 25.587}, {\"agitation_rpm\": 286.001, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.821, \"dissolved_oxygen_pct\": 36.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.42, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.087, \"our_mmol_l_h\": 9.585, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.785, \"viable_cell_density_million_ml\": 28.158}, {\"agitation_rpm\": 285.912, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 9.87, \"dissolved_oxygen_pct\": 28.798, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.496, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.045, \"our_mmol_l_h\": 10.692, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.65, \"viable_cell_density_million_ml\": 31.465}, {\"agitation_rpm\": 284.451, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.776, \"dissolved_oxygen_pct\": 17.727, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.705, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.999, \"our_mmol_l_h\": 11.736, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.652, \"viable_cell_density_million_ml\": 34.572}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d9a73bedebcd5c25ed26", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.892, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.995, \"dissolved_oxygen_pct\": 35.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.374, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.709, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 25.587}, {\"agitation_rpm\": 286.001, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.821, \"dissolved_oxygen_pct\": 36.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.42, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.087, \"our_mmol_l_h\": 9.585, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.785, \"viable_cell_density_million_ml\": 28.158}, {\"agitation_rpm\": 285.912, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 9.87, \"dissolved_oxygen_pct\": 28.798, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.496, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.045, \"our_mmol_l_h\": 10.692, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.65, \"viable_cell_density_million_ml\": 31.465}, {\"agitation_rpm\": 284.451, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.776, \"dissolved_oxygen_pct\": 17.727, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.705, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.999, \"our_mmol_l_h\": 11.736, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.652, \"viable_cell_density_million_ml\": 34.572}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0ee091c56b49c0abfd2e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.892, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 7.995, \"dissolved_oxygen_pct\": 35.633, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.374, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.159, \"our_mmol_l_h\": 8.709, \"ph\": 6.997, \"phase\": \"production\", \"temperature_c\": 35.79, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 25.587}, {\"agitation_rpm\": 286.001, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 8.821, \"dissolved_oxygen_pct\": 36.091, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.42, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.087, \"our_mmol_l_h\": 9.585, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.785, \"viability_pct\": 98.785, \"viable_cell_density_million_ml\": 28.158}, {\"agitation_rpm\": 285.912, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 9.87, \"dissolved_oxygen_pct\": 28.798, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.496, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.045, \"our_mmol_l_h\": 10.692, \"ph\": 7.0, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.65, \"viable_cell_density_million_ml\": 31.465}, {\"agitation_rpm\": 284.451, \"airflow_vvm\": 0.177, \"cer_mmol_l_h\": 10.776, \"dissolved_oxygen_pct\": 17.727, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.705, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.999, \"our_mmol_l_h\": 11.736, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.652, \"viable_cell_density_million_ml\": 34.572}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-30a05ed8df3ca5116492", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.796, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.237, \"dissolved_oxygen_pct\": 41.516, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.497, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 3.497, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.393, \"viable_cell_density_million_ml\": 10.217}, {\"agitation_rpm\": 216.022, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.875, \"dissolved_oxygen_pct\": 39.612, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.154, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.912, \"our_mmol_l_h\": 4.252, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.415, \"viable_cell_density_million_ml\": 12.45}, {\"agitation_rpm\": 245.784, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.88, \"dissolved_oxygen_pct\": 37.919, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.128, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.834, \"ph\": 6.965, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 15.273}, {\"agitation_rpm\": 278.754, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 5.995, \"dissolved_oxygen_pct\": 36.485, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.826, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 5.328, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.397, \"viable_cell_density_million_ml\": 18.202}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d2d7315fcf36ec6f8e14", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.796, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.237, \"dissolved_oxygen_pct\": 41.516, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.497, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 3.497, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.393, \"viable_cell_density_million_ml\": 10.217}, {\"agitation_rpm\": 216.022, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.875, \"dissolved_oxygen_pct\": 39.612, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.154, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.912, \"our_mmol_l_h\": 4.252, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.415, \"viable_cell_density_million_ml\": 12.45}, {\"agitation_rpm\": 245.784, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.88, \"dissolved_oxygen_pct\": 37.919, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.128, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.834, \"ph\": 6.965, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 15.273}, {\"agitation_rpm\": 278.754, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 5.995, \"dissolved_oxygen_pct\": 36.485, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.826, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 5.328, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.397, \"viable_cell_density_million_ml\": 18.202}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-483df75221b4af8da503", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.796, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.237, \"dissolved_oxygen_pct\": 41.516, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.497, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 3.497, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.393, \"viable_cell_density_million_ml\": 10.217}, {\"agitation_rpm\": 216.022, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.875, \"dissolved_oxygen_pct\": 39.612, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.154, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.912, \"our_mmol_l_h\": 4.252, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.415, \"viable_cell_density_million_ml\": 12.45}, {\"agitation_rpm\": 245.784, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.88, \"dissolved_oxygen_pct\": 37.919, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.128, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.834, \"ph\": 6.965, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 15.273}, {\"agitation_rpm\": 278.754, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 5.995, \"dissolved_oxygen_pct\": 36.485, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.826, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 5.328, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.397, \"viable_cell_density_million_ml\": 18.202}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-58064df6aeb663d7d71e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.796, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.237, \"dissolved_oxygen_pct\": 41.516, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.497, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.752, \"our_mmol_l_h\": 3.497, \"ph\": 6.969, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.393, \"viable_cell_density_million_ml\": 10.217}, {\"agitation_rpm\": 216.022, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.875, \"dissolved_oxygen_pct\": 39.612, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.154, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.912, \"our_mmol_l_h\": 4.252, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.415, \"viable_cell_density_million_ml\": 12.45}, {\"agitation_rpm\": 245.784, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.88, \"dissolved_oxygen_pct\": 37.919, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.128, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.834, \"ph\": 6.965, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.403, \"viable_cell_density_million_ml\": 15.273}, {\"agitation_rpm\": 278.754, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 5.995, \"dissolved_oxygen_pct\": 36.485, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.826, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.815, \"our_mmol_l_h\": 5.328, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.812, \"viability_pct\": 98.397, \"viable_cell_density_million_ml\": 18.202}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0888a13f6179e8a6d0d8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.401, \"airflow_vvm\": 0.196, \"cer_mmol_l_h\": 3.221, \"dissolved_oxygen_pct\": 41.647, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.453, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.762, \"our_mmol_l_h\": 3.525, \"ph\": 6.978, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.589, \"viable_cell_density_million_ml\": 10.249}, {\"agitation_rpm\": 218.181, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.916, \"dissolved_oxygen_pct\": 39.615, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.177, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.921, \"our_mmol_l_h\": 4.242, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.556, \"viable_cell_density_million_ml\": 12.372}, {\"agitation_rpm\": 246.601, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.865, \"dissolved_oxygen_pct\": 30.5, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.145, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.942, \"our_mmol_l_h\": 5.321, \"ph\": 6.961, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.608, \"viable_cell_density_million_ml\": 15.615}, {\"agitation_rpm\": 277.831, \"airflow_vvm\": 0.169, \"cer_mmol_l_h\": 6.024, \"dissolved_oxygen_pct\": 17.971, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.973, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.804, \"our_mmol_l_h\": 6.55, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.37, \"viable_cell_density_million_ml\": 19.264}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-338d17e678eac721ba4d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.401, \"airflow_vvm\": 0.196, \"cer_mmol_l_h\": 3.221, \"dissolved_oxygen_pct\": 41.647, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.453, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.762, \"our_mmol_l_h\": 3.525, \"ph\": 6.978, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.589, \"viable_cell_density_million_ml\": 10.249}, {\"agitation_rpm\": 218.181, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.916, \"dissolved_oxygen_pct\": 39.615, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.177, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.921, \"our_mmol_l_h\": 4.242, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.556, \"viable_cell_density_million_ml\": 12.372}, {\"agitation_rpm\": 246.601, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.865, \"dissolved_oxygen_pct\": 30.5, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.145, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.942, \"our_mmol_l_h\": 5.321, \"ph\": 6.961, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.608, \"viable_cell_density_million_ml\": 15.615}, {\"agitation_rpm\": 277.831, \"airflow_vvm\": 0.169, \"cer_mmol_l_h\": 6.024, \"dissolved_oxygen_pct\": 17.971, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.973, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.804, \"our_mmol_l_h\": 6.55, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.37, \"viable_cell_density_million_ml\": 19.264}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-8d2adf1cdca17167b7a6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.401, \"airflow_vvm\": 0.196, \"cer_mmol_l_h\": 3.221, \"dissolved_oxygen_pct\": 41.647, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.453, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.762, \"our_mmol_l_h\": 3.525, \"ph\": 6.978, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.589, \"viable_cell_density_million_ml\": 10.249}, {\"agitation_rpm\": 218.181, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.916, \"dissolved_oxygen_pct\": 39.615, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.177, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.921, \"our_mmol_l_h\": 4.242, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.556, \"viable_cell_density_million_ml\": 12.372}, {\"agitation_rpm\": 246.601, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.865, \"dissolved_oxygen_pct\": 30.5, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.145, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.942, \"our_mmol_l_h\": 5.321, \"ph\": 6.961, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.608, \"viable_cell_density_million_ml\": 15.615}, {\"agitation_rpm\": 277.831, \"airflow_vvm\": 0.169, \"cer_mmol_l_h\": 6.024, \"dissolved_oxygen_pct\": 17.971, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.973, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.804, \"our_mmol_l_h\": 6.55, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.37, \"viable_cell_density_million_ml\": 19.264}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6752403b0097f655e096", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.401, \"airflow_vvm\": 0.196, \"cer_mmol_l_h\": 3.221, \"dissolved_oxygen_pct\": 41.647, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.453, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.762, \"our_mmol_l_h\": 3.525, \"ph\": 6.978, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.589, \"viable_cell_density_million_ml\": 10.249}, {\"agitation_rpm\": 218.181, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.916, \"dissolved_oxygen_pct\": 39.615, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.177, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.921, \"our_mmol_l_h\": 4.242, \"ph\": 6.968, \"phase\": \"exponential\", \"temperature_c\": 36.779, \"viability_pct\": 98.556, \"viable_cell_density_million_ml\": 12.372}, {\"agitation_rpm\": 246.601, \"airflow_vvm\": 0.201, \"cer_mmol_l_h\": 4.865, \"dissolved_oxygen_pct\": 30.5, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.145, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.942, \"our_mmol_l_h\": 5.321, \"ph\": 6.961, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.608, \"viable_cell_density_million_ml\": 15.615}, {\"agitation_rpm\": 277.831, \"airflow_vvm\": 0.169, \"cer_mmol_l_h\": 6.024, \"dissolved_oxygen_pct\": 17.971, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.973, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.804, \"our_mmol_l_h\": 6.55, \"ph\": 6.973, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.37, \"viable_cell_density_million_ml\": 19.264}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7c1bd67fb734605bf997", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.914, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.729, \"dissolved_oxygen_pct\": 39.659, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.591, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.272, \"our_mmol_l_h\": 4.074, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.758, \"viable_cell_density_million_ml\": 11.894}, {\"agitation_rpm\": 237.161, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.643, \"dissolved_oxygen_pct\": 37.787, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.477, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.007, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.863, \"viable_cell_density_million_ml\": 14.876}, {\"agitation_rpm\": 280.775, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.101, \"dissolved_oxygen_pct\": 35.727, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.425, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.199, \"our_mmol_l_h\": 6.073, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.937, \"viable_cell_density_million_ml\": 18.985}, {\"agitation_rpm\": 285.862, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.965, \"dissolved_oxygen_pct\": 36.963, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.166, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.126, \"our_mmol_l_h\": 7.492, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.822, \"viable_cell_density_million_ml\": 24.522}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3dc87847b16e373078c3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.914, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.729, \"dissolved_oxygen_pct\": 39.659, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.591, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.272, \"our_mmol_l_h\": 4.074, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.758, \"viable_cell_density_million_ml\": 11.894}, {\"agitation_rpm\": 237.161, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.643, \"dissolved_oxygen_pct\": 37.787, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.477, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.007, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.863, \"viable_cell_density_million_ml\": 14.876}, {\"agitation_rpm\": 280.775, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.101, \"dissolved_oxygen_pct\": 35.727, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.425, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.199, \"our_mmol_l_h\": 6.073, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.937, \"viable_cell_density_million_ml\": 18.985}, {\"agitation_rpm\": 285.862, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.965, \"dissolved_oxygen_pct\": 36.963, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.166, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.126, \"our_mmol_l_h\": 7.492, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.822, \"viable_cell_density_million_ml\": 24.522}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b00dc2e47b1a7e53068d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.914, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.729, \"dissolved_oxygen_pct\": 39.659, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.591, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.272, \"our_mmol_l_h\": 4.074, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.758, \"viable_cell_density_million_ml\": 11.894}, {\"agitation_rpm\": 237.161, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.643, \"dissolved_oxygen_pct\": 37.787, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.477, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.007, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.863, \"viable_cell_density_million_ml\": 14.876}, {\"agitation_rpm\": 280.775, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.101, \"dissolved_oxygen_pct\": 35.727, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.425, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.199, \"our_mmol_l_h\": 6.073, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.937, \"viable_cell_density_million_ml\": 18.985}, {\"agitation_rpm\": 285.862, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.965, \"dissolved_oxygen_pct\": 36.963, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.166, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.126, \"our_mmol_l_h\": 7.492, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.822, \"viable_cell_density_million_ml\": 24.522}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9761582bb15f1fa2854a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 212.914, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.729, \"dissolved_oxygen_pct\": 39.659, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.591, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.272, \"our_mmol_l_h\": 4.074, \"ph\": 6.986, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.758, \"viable_cell_density_million_ml\": 11.894}, {\"agitation_rpm\": 237.161, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.643, \"dissolved_oxygen_pct\": 37.787, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.477, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.007, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.788, \"viability_pct\": 98.863, \"viable_cell_density_million_ml\": 14.876}, {\"agitation_rpm\": 280.775, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.101, \"dissolved_oxygen_pct\": 35.727, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.425, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.199, \"our_mmol_l_h\": 6.073, \"ph\": 6.999, \"phase\": \"exponential\", \"temperature_c\": 36.809, \"viability_pct\": 98.937, \"viable_cell_density_million_ml\": 18.985}, {\"agitation_rpm\": 285.862, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.965, \"dissolved_oxygen_pct\": 36.963, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.166, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.126, \"our_mmol_l_h\": 7.492, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.822, \"viable_cell_density_million_ml\": 24.522}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-90ac2f3a7af2bab4454a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.985, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.976, \"dissolved_oxygen_pct\": 36.931, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.137, \"our_mmol_l_h\": 8.701, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.653, \"viable_cell_density_million_ml\": 25.48}, {\"agitation_rpm\": 285.814, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.82, \"dissolved_oxygen_pct\": 36.998, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.109, \"our_mmol_l_h\": 9.576, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 28.181}, {\"agitation_rpm\": 284.67, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 9.874, \"dissolved_oxygen_pct\": 28.551, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.525, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.061, \"our_mmol_l_h\": 10.704, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.661, \"viable_cell_density_million_ml\": 31.536}, {\"agitation_rpm\": 285.895, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.8, \"dissolved_oxygen_pct\": 17.805, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.745, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.995, \"our_mmol_l_h\": 11.738, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.933, \"viable_cell_density_million_ml\": 34.506}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3cea502eeec10f71862a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.985, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.976, \"dissolved_oxygen_pct\": 36.931, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.137, \"our_mmol_l_h\": 8.701, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.653, \"viable_cell_density_million_ml\": 25.48}, {\"agitation_rpm\": 285.814, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.82, \"dissolved_oxygen_pct\": 36.998, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.109, \"our_mmol_l_h\": 9.576, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 28.181}, {\"agitation_rpm\": 284.67, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 9.874, \"dissolved_oxygen_pct\": 28.551, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.525, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.061, \"our_mmol_l_h\": 10.704, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.661, \"viable_cell_density_million_ml\": 31.536}, {\"agitation_rpm\": 285.895, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.8, \"dissolved_oxygen_pct\": 17.805, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.745, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.995, \"our_mmol_l_h\": 11.738, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.933, \"viable_cell_density_million_ml\": 34.506}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cea9eb2fdb34cd3bdc6c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.985, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.976, \"dissolved_oxygen_pct\": 36.931, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.137, \"our_mmol_l_h\": 8.701, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.653, \"viable_cell_density_million_ml\": 25.48}, {\"agitation_rpm\": 285.814, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.82, \"dissolved_oxygen_pct\": 36.998, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.109, \"our_mmol_l_h\": 9.576, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 28.181}, {\"agitation_rpm\": 284.67, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 9.874, \"dissolved_oxygen_pct\": 28.551, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.525, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.061, \"our_mmol_l_h\": 10.704, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.661, \"viable_cell_density_million_ml\": 31.536}, {\"agitation_rpm\": 285.895, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.8, \"dissolved_oxygen_pct\": 17.805, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.745, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.995, \"our_mmol_l_h\": 11.738, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.933, \"viable_cell_density_million_ml\": 34.506}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-43cdba4a7d1bcf7a6a93", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 285.985, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.976, \"dissolved_oxygen_pct\": 36.931, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.369, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.137, \"our_mmol_l_h\": 8.701, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.653, \"viable_cell_density_million_ml\": 25.48}, {\"agitation_rpm\": 285.814, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 8.82, \"dissolved_oxygen_pct\": 36.998, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.391, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.109, \"our_mmol_l_h\": 9.576, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.674, \"viable_cell_density_million_ml\": 28.181}, {\"agitation_rpm\": 284.67, \"airflow_vvm\": 0.248, \"cer_mmol_l_h\": 9.874, \"dissolved_oxygen_pct\": 28.551, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.525, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.061, \"our_mmol_l_h\": 10.704, \"ph\": 6.999, \"phase\": \"production\", \"temperature_c\": 35.817, \"viability_pct\": 98.661, \"viable_cell_density_million_ml\": 31.536}, {\"agitation_rpm\": 285.895, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 10.8, \"dissolved_oxygen_pct\": 17.805, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.745, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 0.995, \"our_mmol_l_h\": 11.738, \"ph\": 7.01, \"phase\": \"production\", \"temperature_c\": 35.825, \"viability_pct\": 98.933, \"viable_cell_density_million_ml\": 34.506}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-980c02a74d28ed944c2a", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.534, \"airflow_vvm\": 0.195, \"cer_mmol_l_h\": 3.207, \"dissolved_oxygen_pct\": 41.032, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.486, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.782, \"our_mmol_l_h\": 3.475, \"ph\": 6.972, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.608, \"viable_cell_density_million_ml\": 10.236}, {\"agitation_rpm\": 216.385, \"airflow_vvm\": 0.218, \"cer_mmol_l_h\": 3.903, \"dissolved_oxygen_pct\": 40.018, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.206, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.911, \"our_mmol_l_h\": 4.251, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.572, \"viable_cell_density_million_ml\": 12.398}, {\"agitation_rpm\": 245.301, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.912, \"dissolved_oxygen_pct\": 37.46, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.144, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.947, \"our_mmol_l_h\": 4.826, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 15.281}, {\"agitation_rpm\": 277.63, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.992, \"dissolved_oxygen_pct\": 35.609, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.781, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.79, \"our_mmol_l_h\": 5.335, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.621, \"viable_cell_density_million_ml\": 18.145}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7d68daca26d6d47bff1f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.534, \"airflow_vvm\": 0.195, \"cer_mmol_l_h\": 3.207, \"dissolved_oxygen_pct\": 41.032, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.486, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.782, \"our_mmol_l_h\": 3.475, \"ph\": 6.972, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.608, \"viable_cell_density_million_ml\": 10.236}, {\"agitation_rpm\": 216.385, \"airflow_vvm\": 0.218, \"cer_mmol_l_h\": 3.903, \"dissolved_oxygen_pct\": 40.018, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.206, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.911, \"our_mmol_l_h\": 4.251, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.572, \"viable_cell_density_million_ml\": 12.398}, {\"agitation_rpm\": 245.301, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.912, \"dissolved_oxygen_pct\": 37.46, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.144, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.947, \"our_mmol_l_h\": 4.826, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 15.281}, {\"agitation_rpm\": 277.63, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.992, \"dissolved_oxygen_pct\": 35.609, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.781, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.79, \"our_mmol_l_h\": 5.335, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.621, \"viable_cell_density_million_ml\": 18.145}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2e525086895955d3df76", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.534, \"airflow_vvm\": 0.195, \"cer_mmol_l_h\": 3.207, \"dissolved_oxygen_pct\": 41.032, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.486, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.782, \"our_mmol_l_h\": 3.475, \"ph\": 6.972, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.608, \"viable_cell_density_million_ml\": 10.236}, {\"agitation_rpm\": 216.385, \"airflow_vvm\": 0.218, \"cer_mmol_l_h\": 3.903, \"dissolved_oxygen_pct\": 40.018, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.206, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.911, \"our_mmol_l_h\": 4.251, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.572, \"viable_cell_density_million_ml\": 12.398}, {\"agitation_rpm\": 245.301, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.912, \"dissolved_oxygen_pct\": 37.46, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.144, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.947, \"our_mmol_l_h\": 4.826, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 15.281}, {\"agitation_rpm\": 277.63, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.992, \"dissolved_oxygen_pct\": 35.609, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.781, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.79, \"our_mmol_l_h\": 5.335, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.621, \"viable_cell_density_million_ml\": 18.145}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-edf3cc2222c71993c79f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.534, \"airflow_vvm\": 0.195, \"cer_mmol_l_h\": 3.207, \"dissolved_oxygen_pct\": 41.032, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.486, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.782, \"our_mmol_l_h\": 3.475, \"ph\": 6.972, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.608, \"viable_cell_density_million_ml\": 10.236}, {\"agitation_rpm\": 216.385, \"airflow_vvm\": 0.218, \"cer_mmol_l_h\": 3.903, \"dissolved_oxygen_pct\": 40.018, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.206, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.911, \"our_mmol_l_h\": 4.251, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.572, \"viable_cell_density_million_ml\": 12.398}, {\"agitation_rpm\": 245.301, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 4.912, \"dissolved_oxygen_pct\": 37.46, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.144, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.947, \"our_mmol_l_h\": 4.826, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.811, \"viability_pct\": 98.454, \"viable_cell_density_million_ml\": 15.281}, {\"agitation_rpm\": 277.63, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.992, \"dissolved_oxygen_pct\": 35.609, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.781, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.79, \"our_mmol_l_h\": 5.335, \"ph\": 6.964, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.621, \"viable_cell_density_million_ml\": 18.145}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d4f824bac0110a0ad823", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.678, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.013, \"dissolved_oxygen_pct\": 36.514, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.051, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.809, \"our_mmol_l_h\": 6.543, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.432, \"viable_cell_density_million_ml\": 19.267}, {\"agitation_rpm\": 283.997, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 36.451, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.939, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.729, \"our_mmol_l_h\": 6.962, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.626, \"viable_cell_density_million_ml\": 20.475}, {\"agitation_rpm\": 283.884, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.884, \"dissolved_oxygen_pct\": 28.347, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.927, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.621, \"our_mmol_l_h\": 7.51, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 22.007}, {\"agitation_rpm\": 286.089, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.182, \"dissolved_oxygen_pct\": 18.934, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.76, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.827, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 96.624, \"viable_cell_density_million_ml\": 22.978}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0f944e70d3e8b87b9d62", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.678, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.013, \"dissolved_oxygen_pct\": 36.514, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.051, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.809, \"our_mmol_l_h\": 6.543, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.432, \"viable_cell_density_million_ml\": 19.267}, {\"agitation_rpm\": 283.997, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 36.451, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.939, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.729, \"our_mmol_l_h\": 6.962, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.626, \"viable_cell_density_million_ml\": 20.475}, {\"agitation_rpm\": 283.884, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.884, \"dissolved_oxygen_pct\": 28.347, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.927, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.621, \"our_mmol_l_h\": 7.51, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 22.007}, {\"agitation_rpm\": 286.089, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.182, \"dissolved_oxygen_pct\": 18.934, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.76, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.827, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 96.624, \"viable_cell_density_million_ml\": 22.978}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-45e6752d2322a2fa66dc", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.678, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.013, \"dissolved_oxygen_pct\": 36.514, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.051, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.809, \"our_mmol_l_h\": 6.543, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.432, \"viable_cell_density_million_ml\": 19.267}, {\"agitation_rpm\": 283.997, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 36.451, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.939, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.729, \"our_mmol_l_h\": 6.962, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.626, \"viable_cell_density_million_ml\": 20.475}, {\"agitation_rpm\": 283.884, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.884, \"dissolved_oxygen_pct\": 28.347, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.927, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.621, \"our_mmol_l_h\": 7.51, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 22.007}, {\"agitation_rpm\": 286.089, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.182, \"dissolved_oxygen_pct\": 18.934, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.76, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.827, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 96.624, \"viable_cell_density_million_ml\": 22.978}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-e31c75d32d612493d6ec", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 278.678, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.013, \"dissolved_oxygen_pct\": 36.514, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.051, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.809, \"our_mmol_l_h\": 6.543, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.795, \"viability_pct\": 98.432, \"viable_cell_density_million_ml\": 19.267}, {\"agitation_rpm\": 283.997, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 6.412, \"dissolved_oxygen_pct\": 36.451, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.939, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.729, \"our_mmol_l_h\": 6.962, \"ph\": 6.97, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.626, \"viable_cell_density_million_ml\": 20.475}, {\"agitation_rpm\": 283.884, \"airflow_vvm\": 0.246, \"cer_mmol_l_h\": 6.884, \"dissolved_oxygen_pct\": 28.347, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.927, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.621, \"our_mmol_l_h\": 7.51, \"ph\": 6.975, \"phase\": \"production\", \"temperature_c\": 35.805, \"viability_pct\": 98.628, \"viable_cell_density_million_ml\": 22.007}, {\"agitation_rpm\": 286.089, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.182, \"dissolved_oxygen_pct\": 18.934, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.76, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.484, \"our_mmol_l_h\": 7.827, \"ph\": 6.984, \"phase\": \"production\", \"temperature_c\": 35.823, \"viability_pct\": 96.624, \"viable_cell_density_million_ml\": 22.978}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b8a067ac2702277b2ee5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.733, \"airflow_vvm\": 0.257, \"cer_mmol_l_h\": 5.097, \"dissolved_oxygen_pct\": 38.02, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.444, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 5.566, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.799, \"viability_pct\": 98.847, \"viable_cell_density_million_ml\": 16.375}, {\"agitation_rpm\": 279.043, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.114, \"dissolved_oxygen_pct\": 36.451, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.399, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 6.587, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.823, \"viability_pct\": 98.83, \"viable_cell_density_million_ml\": 19.504}, {\"agitation_rpm\": 283.864, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.524, \"dissolved_oxygen_pct\": 36.748, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.44, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 7.707, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 23.659}, {\"agitation_rpm\": 285.976, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.203, \"dissolved_oxygen_pct\": 36.002, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.241, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 8.818, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.935, \"viable_cell_density_million_ml\": 28.491}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a5b05c8c8aba411411e1", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.733, \"airflow_vvm\": 0.257, \"cer_mmol_l_h\": 5.097, \"dissolved_oxygen_pct\": 38.02, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.444, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 5.566, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.799, \"viability_pct\": 98.847, \"viable_cell_density_million_ml\": 16.375}, {\"agitation_rpm\": 279.043, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.114, \"dissolved_oxygen_pct\": 36.451, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.399, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 6.587, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.823, \"viability_pct\": 98.83, \"viable_cell_density_million_ml\": 19.504}, {\"agitation_rpm\": 283.864, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.524, \"dissolved_oxygen_pct\": 36.748, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.44, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 7.707, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 23.659}, {\"agitation_rpm\": 285.976, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.203, \"dissolved_oxygen_pct\": 36.002, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.241, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 8.818, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.935, \"viable_cell_density_million_ml\": 28.491}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2ede74eef1666c3a2dd2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.733, \"airflow_vvm\": 0.257, \"cer_mmol_l_h\": 5.097, \"dissolved_oxygen_pct\": 38.02, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.444, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 5.566, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.799, \"viability_pct\": 98.847, \"viable_cell_density_million_ml\": 16.375}, {\"agitation_rpm\": 279.043, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.114, \"dissolved_oxygen_pct\": 36.451, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.399, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 6.587, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.823, \"viability_pct\": 98.83, \"viable_cell_density_million_ml\": 19.504}, {\"agitation_rpm\": 283.864, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.524, \"dissolved_oxygen_pct\": 36.748, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.44, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 7.707, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 23.659}, {\"agitation_rpm\": 285.976, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.203, \"dissolved_oxygen_pct\": 36.002, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.241, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 8.818, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.935, \"viable_cell_density_million_ml\": 28.491}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-22ef9d72d6c771462ab4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.733, \"airflow_vvm\": 0.257, \"cer_mmol_l_h\": 5.097, \"dissolved_oxygen_pct\": 38.02, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.444, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.247, \"our_mmol_l_h\": 5.566, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.799, \"viability_pct\": 98.847, \"viable_cell_density_million_ml\": 16.375}, {\"agitation_rpm\": 279.043, \"airflow_vvm\": 0.291, \"cer_mmol_l_h\": 6.114, \"dissolved_oxygen_pct\": 36.451, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.399, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.184, \"our_mmol_l_h\": 6.587, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.823, \"viability_pct\": 98.83, \"viable_cell_density_million_ml\": 19.504}, {\"agitation_rpm\": 283.864, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.524, \"dissolved_oxygen_pct\": 36.748, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.44, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.156, \"our_mmol_l_h\": 7.707, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.851, \"viable_cell_density_million_ml\": 23.659}, {\"agitation_rpm\": 285.976, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.203, \"dissolved_oxygen_pct\": 36.002, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.241, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 8.818, \"ph\": 7.002, \"phase\": \"production\", \"temperature_c\": 35.798, \"viability_pct\": 98.935, \"viable_cell_density_million_ml\": 28.491}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-499620c2963bd2e54f4e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.505, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.727, \"dissolved_oxygen_pct\": 40.537, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.623, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.234, \"our_mmol_l_h\": 4.063, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.708, \"viable_cell_density_million_ml\": 12.014}, {\"agitation_rpm\": 237.523, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.623, \"dissolved_oxygen_pct\": 38.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.492, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.231, \"our_mmol_l_h\": 5.029, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.659, \"viable_cell_density_million_ml\": 14.806}, {\"agitation_rpm\": 279.699, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 6.081, \"dissolved_oxygen_pct\": 28.607, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.186, \"our_mmol_l_h\": 6.62, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.789, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 19.527}, {\"agitation_rpm\": 286.128, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.97, \"dissolved_oxygen_pct\": 17.702, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.379, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.123, \"our_mmol_l_h\": 8.693, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.768, \"viable_cell_density_million_ml\": 25.613}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-25dc6d4d0a9920f7a6be", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.505, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.727, \"dissolved_oxygen_pct\": 40.537, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.623, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.234, \"our_mmol_l_h\": 4.063, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.708, \"viable_cell_density_million_ml\": 12.014}, {\"agitation_rpm\": 237.523, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.623, \"dissolved_oxygen_pct\": 38.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.492, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.231, \"our_mmol_l_h\": 5.029, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.659, \"viable_cell_density_million_ml\": 14.806}, {\"agitation_rpm\": 279.699, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 6.081, \"dissolved_oxygen_pct\": 28.607, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.186, \"our_mmol_l_h\": 6.62, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.789, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 19.527}, {\"agitation_rpm\": 286.128, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.97, \"dissolved_oxygen_pct\": 17.702, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.379, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.123, \"our_mmol_l_h\": 8.693, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.768, \"viable_cell_density_million_ml\": 25.613}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3d23936ccf3905b658c5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.505, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.727, \"dissolved_oxygen_pct\": 40.537, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.623, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.234, \"our_mmol_l_h\": 4.063, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.708, \"viable_cell_density_million_ml\": 12.014}, {\"agitation_rpm\": 237.523, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.623, \"dissolved_oxygen_pct\": 38.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.492, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.231, \"our_mmol_l_h\": 5.029, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.659, \"viable_cell_density_million_ml\": 14.806}, {\"agitation_rpm\": 279.699, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 6.081, \"dissolved_oxygen_pct\": 28.607, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.186, \"our_mmol_l_h\": 6.62, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.789, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 19.527}, {\"agitation_rpm\": 286.128, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.97, \"dissolved_oxygen_pct\": 17.702, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.379, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.123, \"our_mmol_l_h\": 8.693, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.768, \"viable_cell_density_million_ml\": 25.613}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-65313b9e56bd13017120", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.505, \"airflow_vvm\": 0.209, \"cer_mmol_l_h\": 3.727, \"dissolved_oxygen_pct\": 40.537, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.623, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.234, \"our_mmol_l_h\": 4.063, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.777, \"viability_pct\": 98.708, \"viable_cell_density_million_ml\": 12.014}, {\"agitation_rpm\": 237.523, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 4.623, \"dissolved_oxygen_pct\": 38.504, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.492, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.231, \"our_mmol_l_h\": 5.029, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.659, \"viable_cell_density_million_ml\": 14.806}, {\"agitation_rpm\": 279.699, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 6.081, \"dissolved_oxygen_pct\": 28.607, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.186, \"our_mmol_l_h\": 6.62, \"ph\": 6.989, \"phase\": \"exponential\", \"temperature_c\": 36.789, \"viability_pct\": 98.69, \"viable_cell_density_million_ml\": 19.527}, {\"agitation_rpm\": 286.128, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 7.97, \"dissolved_oxygen_pct\": 17.702, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.379, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.123, \"our_mmol_l_h\": 8.693, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.768, \"viable_cell_density_million_ml\": 25.613}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5cb9b73e53a38f5942b3", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.745, \"airflow_vvm\": 0.194, \"cer_mmol_l_h\": 3.234, \"dissolved_oxygen_pct\": 41.392, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.465, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.753, \"our_mmol_l_h\": 3.516, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 10.261}, {\"agitation_rpm\": 217.533, \"airflow_vvm\": 0.216, \"cer_mmol_l_h\": 3.921, \"dissolved_oxygen_pct\": 39.788, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.168, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.935, \"our_mmol_l_h\": 4.205, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.551, \"viable_cell_density_million_ml\": 12.395}, {\"agitation_rpm\": 246.563, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.894, \"dissolved_oxygen_pct\": 38.03, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.191, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.794, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.354, \"viable_cell_density_million_ml\": 15.184}, {\"agitation_rpm\": 277.199, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 5.98, \"dissolved_oxygen_pct\": 36.811, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.807, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 5.31, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 18.218}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-055e9967d9cbb9d0229f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.745, \"airflow_vvm\": 0.194, \"cer_mmol_l_h\": 3.234, \"dissolved_oxygen_pct\": 41.392, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.465, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.753, \"our_mmol_l_h\": 3.516, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 10.261}, {\"agitation_rpm\": 217.533, \"airflow_vvm\": 0.216, \"cer_mmol_l_h\": 3.921, \"dissolved_oxygen_pct\": 39.788, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.168, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.935, \"our_mmol_l_h\": 4.205, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.551, \"viable_cell_density_million_ml\": 12.395}, {\"agitation_rpm\": 246.563, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.894, \"dissolved_oxygen_pct\": 38.03, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.191, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.794, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.354, \"viable_cell_density_million_ml\": 15.184}, {\"agitation_rpm\": 277.199, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 5.98, \"dissolved_oxygen_pct\": 36.811, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.807, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 5.31, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 18.218}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1db7b23686448632e9a5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.745, \"airflow_vvm\": 0.194, \"cer_mmol_l_h\": 3.234, \"dissolved_oxygen_pct\": 41.392, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.465, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.753, \"our_mmol_l_h\": 3.516, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 10.261}, {\"agitation_rpm\": 217.533, \"airflow_vvm\": 0.216, \"cer_mmol_l_h\": 3.921, \"dissolved_oxygen_pct\": 39.788, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.168, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.935, \"our_mmol_l_h\": 4.205, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.551, \"viable_cell_density_million_ml\": 12.395}, {\"agitation_rpm\": 246.563, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.894, \"dissolved_oxygen_pct\": 38.03, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.191, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.794, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.354, \"viable_cell_density_million_ml\": 15.184}, {\"agitation_rpm\": 277.199, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 5.98, \"dissolved_oxygen_pct\": 36.811, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.807, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 5.31, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 18.218}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dadf613b2cfac5abfccd", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 196.745, \"airflow_vvm\": 0.194, \"cer_mmol_l_h\": 3.234, \"dissolved_oxygen_pct\": 41.392, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.465, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.753, \"our_mmol_l_h\": 3.516, \"ph\": 6.964, \"phase\": \"exponential\", \"temperature_c\": 36.824, \"viability_pct\": 98.392, \"viable_cell_density_million_ml\": 10.261}, {\"agitation_rpm\": 217.533, \"airflow_vvm\": 0.216, \"cer_mmol_l_h\": 3.921, \"dissolved_oxygen_pct\": 39.788, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.168, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.935, \"our_mmol_l_h\": 4.205, \"ph\": 6.956, \"phase\": \"exponential\", \"temperature_c\": 36.785, \"viability_pct\": 98.551, \"viable_cell_density_million_ml\": 12.395}, {\"agitation_rpm\": 246.563, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 4.894, \"dissolved_oxygen_pct\": 38.03, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.191, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 4.794, \"ph\": 6.967, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.354, \"viable_cell_density_million_ml\": 15.184}, {\"agitation_rpm\": 277.199, \"airflow_vvm\": 0.29, \"cer_mmol_l_h\": 5.98, \"dissolved_oxygen_pct\": 36.811, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.807, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.81, \"our_mmol_l_h\": 5.31, \"ph\": 6.968, \"phase\": \"production\", \"temperature_c\": 35.811, \"viability_pct\": 98.355, \"viable_cell_density_million_ml\": 18.218}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-26f01d4b493be48f9b69", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.212, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.228, \"dissolved_oxygen_pct\": 37.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.051, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.892, \"our_mmol_l_h\": 5.676, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.81, \"viability_pct\": 98.564, \"viable_cell_density_million_ml\": 16.556}, {\"agitation_rpm\": 270.813, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.745, \"dissolved_oxygen_pct\": 36.065, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.865, \"our_mmol_l_h\": 6.246, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.619, \"viable_cell_density_million_ml\": 18.468}, {\"agitation_rpm\": 284.591, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.415, \"dissolved_oxygen_pct\": 29.208, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.973, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.72, \"our_mmol_l_h\": 6.987, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.416, \"viable_cell_density_million_ml\": 20.475}, {\"agitation_rpm\": 284.497, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.023, \"dissolved_oxygen_pct\": 18.905, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.887, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.587, \"our_mmol_l_h\": 7.634, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 97.955, \"viable_cell_density_million_ml\": 22.453}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-1d9acc98e0d6d87e653d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.212, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.228, \"dissolved_oxygen_pct\": 37.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.051, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.892, \"our_mmol_l_h\": 5.676, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.81, \"viability_pct\": 98.564, \"viable_cell_density_million_ml\": 16.556}, {\"agitation_rpm\": 270.813, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.745, \"dissolved_oxygen_pct\": 36.065, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.865, \"our_mmol_l_h\": 6.246, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.619, \"viable_cell_density_million_ml\": 18.468}, {\"agitation_rpm\": 284.591, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.415, \"dissolved_oxygen_pct\": 29.208, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.973, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.72, \"our_mmol_l_h\": 6.987, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.416, \"viable_cell_density_million_ml\": 20.475}, {\"agitation_rpm\": 284.497, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.023, \"dissolved_oxygen_pct\": 18.905, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.887, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.587, \"our_mmol_l_h\": 7.634, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 97.955, \"viable_cell_density_million_ml\": 22.453}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ffaec8ea5a8ec3a18981", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.212, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.228, \"dissolved_oxygen_pct\": 37.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.051, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.892, \"our_mmol_l_h\": 5.676, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.81, \"viability_pct\": 98.564, \"viable_cell_density_million_ml\": 16.556}, {\"agitation_rpm\": 270.813, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.745, \"dissolved_oxygen_pct\": 36.065, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.865, \"our_mmol_l_h\": 6.246, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.619, \"viable_cell_density_million_ml\": 18.468}, {\"agitation_rpm\": 284.591, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.415, \"dissolved_oxygen_pct\": 29.208, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.973, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.72, \"our_mmol_l_h\": 6.987, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.416, \"viable_cell_density_million_ml\": 20.475}, {\"agitation_rpm\": 284.497, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.023, \"dissolved_oxygen_pct\": 18.905, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.887, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.587, \"our_mmol_l_h\": 7.634, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 97.955, \"viable_cell_density_million_ml\": 22.453}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4ae6989e5549e4bd0c7e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.212, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.228, \"dissolved_oxygen_pct\": 37.673, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.051, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.892, \"our_mmol_l_h\": 5.676, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.81, \"viability_pct\": 98.564, \"viable_cell_density_million_ml\": 16.556}, {\"agitation_rpm\": 270.813, \"airflow_vvm\": 0.284, \"cer_mmol_l_h\": 5.745, \"dissolved_oxygen_pct\": 36.065, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.023, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.865, \"our_mmol_l_h\": 6.246, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.793, \"viability_pct\": 98.619, \"viable_cell_density_million_ml\": 18.468}, {\"agitation_rpm\": 284.591, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.415, \"dissolved_oxygen_pct\": 29.208, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.973, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.72, \"our_mmol_l_h\": 6.987, \"ph\": 6.969, \"phase\": \"production\", \"temperature_c\": 35.819, \"viability_pct\": 98.416, \"viable_cell_density_million_ml\": 20.475}, {\"agitation_rpm\": 284.497, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 7.023, \"dissolved_oxygen_pct\": 18.905, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.887, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.587, \"our_mmol_l_h\": 7.634, \"ph\": 6.974, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 97.955, \"viable_cell_density_million_ml\": 22.453}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-79d92a1cc0336e037f8b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.153, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.1, \"dissolved_oxygen_pct\": 37.854, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.429, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.574, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.783, \"viability_pct\": 98.794, \"viable_cell_density_million_ml\": 16.359}, {\"agitation_rpm\": 279.348, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.104, \"dissolved_oxygen_pct\": 36.887, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.411, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.199, \"our_mmol_l_h\": 6.602, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.884, \"viable_cell_density_million_ml\": 19.372}, {\"agitation_rpm\": 285.276, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.553, \"dissolved_oxygen_pct\": 35.706, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.439, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.141, \"our_mmol_l_h\": 7.709, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.858, \"viable_cell_density_million_ml\": 23.62}, {\"agitation_rpm\": 285.648, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.184, \"dissolved_oxygen_pct\": 36.948, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.276, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.108, \"our_mmol_l_h\": 8.812, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.735, \"viable_cell_density_million_ml\": 28.434}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-69be14021af63156cb0c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.153, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.1, \"dissolved_oxygen_pct\": 37.854, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.429, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.574, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.783, \"viability_pct\": 98.794, \"viable_cell_density_million_ml\": 16.359}, {\"agitation_rpm\": 279.348, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.104, \"dissolved_oxygen_pct\": 36.887, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.411, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.199, \"our_mmol_l_h\": 6.602, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.884, \"viable_cell_density_million_ml\": 19.372}, {\"agitation_rpm\": 285.276, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.553, \"dissolved_oxygen_pct\": 35.706, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.439, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.141, \"our_mmol_l_h\": 7.709, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.858, \"viable_cell_density_million_ml\": 23.62}, {\"agitation_rpm\": 285.648, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.184, \"dissolved_oxygen_pct\": 36.948, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.276, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.108, \"our_mmol_l_h\": 8.812, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.735, \"viable_cell_density_million_ml\": 28.434}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0abc98794a36aabe59ea", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.153, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.1, \"dissolved_oxygen_pct\": 37.854, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.429, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.574, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.783, \"viability_pct\": 98.794, \"viable_cell_density_million_ml\": 16.359}, {\"agitation_rpm\": 279.348, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.104, \"dissolved_oxygen_pct\": 36.887, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.411, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.199, \"our_mmol_l_h\": 6.602, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.884, \"viable_cell_density_million_ml\": 19.372}, {\"agitation_rpm\": 285.276, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.553, \"dissolved_oxygen_pct\": 35.706, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.439, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.141, \"our_mmol_l_h\": 7.709, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.858, \"viable_cell_density_million_ml\": 23.62}, {\"agitation_rpm\": 285.648, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.184, \"dissolved_oxygen_pct\": 36.948, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.276, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.108, \"our_mmol_l_h\": 8.812, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.735, \"viable_cell_density_million_ml\": 28.434}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f6aaec07205c804a7648", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.153, \"airflow_vvm\": 0.26, \"cer_mmol_l_h\": 5.1, \"dissolved_oxygen_pct\": 37.854, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.429, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.574, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.783, \"viability_pct\": 98.794, \"viable_cell_density_million_ml\": 16.359}, {\"agitation_rpm\": 279.348, \"airflow_vvm\": 0.295, \"cer_mmol_l_h\": 6.104, \"dissolved_oxygen_pct\": 36.887, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.411, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.199, \"our_mmol_l_h\": 6.602, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.806, \"viability_pct\": 98.884, \"viable_cell_density_million_ml\": 19.372}, {\"agitation_rpm\": 285.276, \"airflow_vvm\": 0.303, \"cer_mmol_l_h\": 7.553, \"dissolved_oxygen_pct\": 35.706, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.439, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.141, \"our_mmol_l_h\": 7.709, \"ph\": 6.994, \"phase\": \"production\", \"temperature_c\": 35.778, \"viability_pct\": 98.858, \"viable_cell_density_million_ml\": 23.62}, {\"agitation_rpm\": 285.648, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 9.184, \"dissolved_oxygen_pct\": 36.948, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.276, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.108, \"our_mmol_l_h\": 8.812, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.735, \"viable_cell_density_million_ml\": 28.434}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5ce42d9b68ea9f11ec4b", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.091, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.108, \"dissolved_oxygen_pct\": 37.463, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.445, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.25, \"our_mmol_l_h\": 5.566, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.868, \"viable_cell_density_million_ml\": 16.307}, {\"agitation_rpm\": 278.878, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.088, \"dissolved_oxygen_pct\": 36.234, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.421, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.215, \"our_mmol_l_h\": 6.618, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.822, \"viable_cell_density_million_ml\": 19.39}, {\"agitation_rpm\": 285.664, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 7.525, \"dissolved_oxygen_pct\": 28.769, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.139, \"our_mmol_l_h\": 8.166, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.871, \"viable_cell_density_million_ml\": 24.173}, {\"agitation_rpm\": 285.01, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 9.189, \"dissolved_oxygen_pct\": 18.05, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.472, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.081, \"our_mmol_l_h\": 10.008, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 29.455}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-13d203809de8e6ecf7b8", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.091, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.108, \"dissolved_oxygen_pct\": 37.463, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.445, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.25, \"our_mmol_l_h\": 5.566, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.868, \"viable_cell_density_million_ml\": 16.307}, {\"agitation_rpm\": 278.878, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.088, \"dissolved_oxygen_pct\": 36.234, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.421, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.215, \"our_mmol_l_h\": 6.618, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.822, \"viable_cell_density_million_ml\": 19.39}, {\"agitation_rpm\": 285.664, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 7.525, \"dissolved_oxygen_pct\": 28.769, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.139, \"our_mmol_l_h\": 8.166, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.871, \"viable_cell_density_million_ml\": 24.173}, {\"agitation_rpm\": 285.01, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 9.189, \"dissolved_oxygen_pct\": 18.05, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.472, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.081, \"our_mmol_l_h\": 10.008, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 29.455}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-964da82708cfb8bb6440", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.091, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.108, \"dissolved_oxygen_pct\": 37.463, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.445, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.25, \"our_mmol_l_h\": 5.566, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.868, \"viable_cell_density_million_ml\": 16.307}, {\"agitation_rpm\": 278.878, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.088, \"dissolved_oxygen_pct\": 36.234, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.421, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.215, \"our_mmol_l_h\": 6.618, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.822, \"viable_cell_density_million_ml\": 19.39}, {\"agitation_rpm\": 285.664, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 7.525, \"dissolved_oxygen_pct\": 28.769, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.139, \"our_mmol_l_h\": 8.166, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.871, \"viable_cell_density_million_ml\": 24.173}, {\"agitation_rpm\": 285.01, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 9.189, \"dissolved_oxygen_pct\": 18.05, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.472, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.081, \"our_mmol_l_h\": 10.008, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 29.455}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5004decf15ddec84f8bb", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.091, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.108, \"dissolved_oxygen_pct\": 37.463, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.445, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.25, \"our_mmol_l_h\": 5.566, \"ph\": 6.993, \"phase\": \"exponential\", \"temperature_c\": 36.775, \"viability_pct\": 98.868, \"viable_cell_density_million_ml\": 16.307}, {\"agitation_rpm\": 278.878, \"airflow_vvm\": 0.292, \"cer_mmol_l_h\": 6.088, \"dissolved_oxygen_pct\": 36.234, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.421, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.215, \"our_mmol_l_h\": 6.618, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.795, \"viability_pct\": 98.822, \"viable_cell_density_million_ml\": 19.39}, {\"agitation_rpm\": 285.664, \"airflow_vvm\": 0.252, \"cer_mmol_l_h\": 7.525, \"dissolved_oxygen_pct\": 28.769, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.374, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.139, \"our_mmol_l_h\": 8.166, \"ph\": 6.991, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.871, \"viable_cell_density_million_ml\": 24.173}, {\"agitation_rpm\": 285.01, \"airflow_vvm\": 0.179, \"cer_mmol_l_h\": 9.189, \"dissolved_oxygen_pct\": 18.05, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.472, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.081, \"our_mmol_l_h\": 10.008, \"ph\": 7.007, \"phase\": \"production\", \"temperature_c\": 35.779, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 29.455}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-9b6eff9320e765dc57ab", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.971, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.03, \"dissolved_oxygen_pct\": 36.228, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.018, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.534, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.365, \"viable_cell_density_million_ml\": 19.19}, {\"agitation_rpm\": 284.093, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.43, \"dissolved_oxygen_pct\": 35.635, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.973, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.744, \"our_mmol_l_h\": 6.983, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.421, \"viable_cell_density_million_ml\": 20.494}, {\"agitation_rpm\": 285.899, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 36.545, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.925, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.607, \"our_mmol_l_h\": 6.976, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.384, \"viable_cell_density_million_ml\": 21.586}, {\"agitation_rpm\": 284.418, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.218, \"dissolved_oxygen_pct\": 36.082, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.573, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.474, \"our_mmol_l_h\": 6.631, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 96.538, \"viable_cell_density_million_ml\": 21.94}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7bc763283d5ba08950f5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.971, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.03, \"dissolved_oxygen_pct\": 36.228, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.018, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.534, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.365, \"viable_cell_density_million_ml\": 19.19}, {\"agitation_rpm\": 284.093, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.43, \"dissolved_oxygen_pct\": 35.635, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.973, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.744, \"our_mmol_l_h\": 6.983, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.421, \"viable_cell_density_million_ml\": 20.494}, {\"agitation_rpm\": 285.899, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 36.545, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.925, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.607, \"our_mmol_l_h\": 6.976, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.384, \"viable_cell_density_million_ml\": 21.586}, {\"agitation_rpm\": 284.418, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.218, \"dissolved_oxygen_pct\": 36.082, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.573, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.474, \"our_mmol_l_h\": 6.631, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 96.538, \"viable_cell_density_million_ml\": 21.94}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c66ad5427cbe06c2e116", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.971, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.03, \"dissolved_oxygen_pct\": 36.228, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.018, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.534, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.365, \"viable_cell_density_million_ml\": 19.19}, {\"agitation_rpm\": 284.093, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.43, \"dissolved_oxygen_pct\": 35.635, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.973, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.744, \"our_mmol_l_h\": 6.983, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.421, \"viable_cell_density_million_ml\": 20.494}, {\"agitation_rpm\": 285.899, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 36.545, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.925, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.607, \"our_mmol_l_h\": 6.976, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.384, \"viable_cell_density_million_ml\": 21.586}, {\"agitation_rpm\": 284.418, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.218, \"dissolved_oxygen_pct\": 36.082, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.573, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.474, \"our_mmol_l_h\": 6.631, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 96.538, \"viable_cell_density_million_ml\": 21.94}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6d6fe840ba51e5491957", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 277.971, \"airflow_vvm\": 0.293, \"cer_mmol_l_h\": 6.03, \"dissolved_oxygen_pct\": 36.228, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.018, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.831, \"our_mmol_l_h\": 6.534, \"ph\": 6.976, \"phase\": \"production\", \"temperature_c\": 35.808, \"viability_pct\": 98.365, \"viable_cell_density_million_ml\": 19.19}, {\"agitation_rpm\": 284.093, \"airflow_vvm\": 0.299, \"cer_mmol_l_h\": 6.43, \"dissolved_oxygen_pct\": 35.635, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.973, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.744, \"our_mmol_l_h\": 6.983, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.799, \"viability_pct\": 98.421, \"viable_cell_density_million_ml\": 20.494}, {\"agitation_rpm\": 285.899, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.881, \"dissolved_oxygen_pct\": 36.545, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.925, \"hour\": 120.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.607, \"our_mmol_l_h\": 6.976, \"ph\": 6.971, \"phase\": \"production\", \"temperature_c\": 35.801, \"viability_pct\": 98.384, \"viable_cell_density_million_ml\": 21.586}, {\"agitation_rpm\": 284.418, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 7.218, \"dissolved_oxygen_pct\": 36.082, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.573, \"hour\": 136.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.474, \"our_mmol_l_h\": 6.631, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.784, \"viability_pct\": 96.538, \"viable_cell_density_million_ml\": 21.94}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3a3fde4a060c96aeccec", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.442, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 4.257, \"dissolved_oxygen_pct\": 38.587, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.15, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.027, \"our_mmol_l_h\": 4.585, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.606, \"viable_cell_density_million_ml\": 13.494}, {\"agitation_rpm\": 246.283, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.906, \"dissolved_oxygen_pct\": 38.408, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.124, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 5.307, \"ph\": 6.966, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.497, \"viable_cell_density_million_ml\": 15.691}, {\"agitation_rpm\": 269.822, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.774, \"dissolved_oxygen_pct\": 29.186, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.005, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.863, \"our_mmol_l_h\": 6.269, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.421, \"viable_cell_density_million_ml\": 18.439}, {\"agitation_rpm\": 284.205, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 6.624, \"dissolved_oxygen_pct\": 18.927, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.976, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.688, \"our_mmol_l_h\": 7.158, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.406, \"viable_cell_density_million_ml\": 21.138}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c816bffbcb6da0b65ec2", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.442, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 4.257, \"dissolved_oxygen_pct\": 38.587, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.15, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.027, \"our_mmol_l_h\": 4.585, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.606, \"viable_cell_density_million_ml\": 13.494}, {\"agitation_rpm\": 246.283, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.906, \"dissolved_oxygen_pct\": 38.408, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.124, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 5.307, \"ph\": 6.966, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.497, \"viable_cell_density_million_ml\": 15.691}, {\"agitation_rpm\": 269.822, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.774, \"dissolved_oxygen_pct\": 29.186, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.005, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.863, \"our_mmol_l_h\": 6.269, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.421, \"viable_cell_density_million_ml\": 18.439}, {\"agitation_rpm\": 284.205, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 6.624, \"dissolved_oxygen_pct\": 18.927, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.976, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.688, \"our_mmol_l_h\": 7.158, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.406, \"viable_cell_density_million_ml\": 21.138}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4fa372c5d720f17d00a4", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.442, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 4.257, \"dissolved_oxygen_pct\": 38.587, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.15, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.027, \"our_mmol_l_h\": 4.585, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.606, \"viable_cell_density_million_ml\": 13.494}, {\"agitation_rpm\": 246.283, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.906, \"dissolved_oxygen_pct\": 38.408, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.124, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 5.307, \"ph\": 6.966, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.497, \"viable_cell_density_million_ml\": 15.691}, {\"agitation_rpm\": 269.822, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.774, \"dissolved_oxygen_pct\": 29.186, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.005, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.863, \"our_mmol_l_h\": 6.269, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.421, \"viable_cell_density_million_ml\": 18.439}, {\"agitation_rpm\": 284.205, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 6.624, \"dissolved_oxygen_pct\": 18.927, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.976, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.688, \"our_mmol_l_h\": 7.158, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.406, \"viable_cell_density_million_ml\": 21.138}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-d69d8ef424b7c74b1251", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 226.442, \"airflow_vvm\": 0.232, \"cer_mmol_l_h\": 4.257, \"dissolved_oxygen_pct\": 38.587, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.15, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 2.027, \"our_mmol_l_h\": 4.585, \"ph\": 6.957, \"phase\": \"exponential\", \"temperature_c\": 36.787, \"viability_pct\": 98.606, \"viable_cell_density_million_ml\": 13.494}, {\"agitation_rpm\": 246.283, \"airflow_vvm\": 0.25, \"cer_mmol_l_h\": 4.906, \"dissolved_oxygen_pct\": 38.408, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.124, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.956, \"our_mmol_l_h\": 5.307, \"ph\": 6.966, \"phase\": \"exponential\", \"temperature_c\": 36.807, \"viability_pct\": 98.497, \"viable_cell_density_million_ml\": 15.691}, {\"agitation_rpm\": 269.822, \"airflow_vvm\": 0.231, \"cer_mmol_l_h\": 5.774, \"dissolved_oxygen_pct\": 29.186, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 4.005, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.863, \"our_mmol_l_h\": 6.269, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.82, \"viability_pct\": 98.421, \"viable_cell_density_million_ml\": 18.439}, {\"agitation_rpm\": 284.205, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 6.624, \"dissolved_oxygen_pct\": 18.927, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.976, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.688, \"our_mmol_l_h\": 7.158, \"ph\": 6.978, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.406, \"viable_cell_density_million_ml\": 21.138}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-c7433e703f78d779edf0", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.335, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.096, \"dissolved_oxygen_pct\": 38.071, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.474, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.543, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 16.389}, {\"agitation_rpm\": 281.231, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.066, \"dissolved_oxygen_pct\": 36.421, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.394, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.218, \"our_mmol_l_h\": 6.59, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 19.524}, {\"agitation_rpm\": 284.176, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.539, \"dissolved_oxygen_pct\": 36.794, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.41, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.144, \"our_mmol_l_h\": 7.666, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.785, \"viable_cell_density_million_ml\": 23.596}, {\"agitation_rpm\": 285.969, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 9.214, \"dissolved_oxygen_pct\": 36.112, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.232, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.086, \"our_mmol_l_h\": 8.815, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.773, \"viable_cell_density_million_ml\": 28.478}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-26c5ab36f5128fa39eef", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.335, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.096, \"dissolved_oxygen_pct\": 38.071, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.474, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.543, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 16.389}, {\"agitation_rpm\": 281.231, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.066, \"dissolved_oxygen_pct\": 36.421, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.394, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.218, \"our_mmol_l_h\": 6.59, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 19.524}, {\"agitation_rpm\": 284.176, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.539, \"dissolved_oxygen_pct\": 36.794, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.41, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.144, \"our_mmol_l_h\": 7.666, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.785, \"viable_cell_density_million_ml\": 23.596}, {\"agitation_rpm\": 285.969, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 9.214, \"dissolved_oxygen_pct\": 36.112, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.232, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.086, \"our_mmol_l_h\": 8.815, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.773, \"viable_cell_density_million_ml\": 28.478}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-a7461f74c04c2857917d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.335, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.096, \"dissolved_oxygen_pct\": 38.071, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.474, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.543, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 16.389}, {\"agitation_rpm\": 281.231, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.066, \"dissolved_oxygen_pct\": 36.421, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.394, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.218, \"our_mmol_l_h\": 6.59, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 19.524}, {\"agitation_rpm\": 284.176, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.539, \"dissolved_oxygen_pct\": 36.794, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.41, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.144, \"our_mmol_l_h\": 7.666, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.785, \"viable_cell_density_million_ml\": 23.596}, {\"agitation_rpm\": 285.969, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 9.214, \"dissolved_oxygen_pct\": 36.112, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.232, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.086, \"our_mmol_l_h\": 8.815, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.773, \"viable_cell_density_million_ml\": 28.478}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-fd2c4e87a46254fa2d05", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.335, \"airflow_vvm\": 0.261, \"cer_mmol_l_h\": 5.096, \"dissolved_oxygen_pct\": 38.071, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.474, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.252, \"our_mmol_l_h\": 5.543, \"ph\": 6.987, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.807, \"viable_cell_density_million_ml\": 16.389}, {\"agitation_rpm\": 281.231, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.066, \"dissolved_oxygen_pct\": 36.421, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.394, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.218, \"our_mmol_l_h\": 6.59, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.816, \"viability_pct\": 98.752, \"viable_cell_density_million_ml\": 19.524}, {\"agitation_rpm\": 284.176, \"airflow_vvm\": 0.298, \"cer_mmol_l_h\": 7.539, \"dissolved_oxygen_pct\": 36.794, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.41, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.144, \"our_mmol_l_h\": 7.666, \"ph\": 7.004, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.785, \"viable_cell_density_million_ml\": 23.596}, {\"agitation_rpm\": 285.969, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 9.214, \"dissolved_oxygen_pct\": 36.112, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.232, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.086, \"our_mmol_l_h\": 8.815, \"ph\": 7.003, \"phase\": \"production\", \"temperature_c\": 35.777, \"viability_pct\": 98.773, \"viable_cell_density_million_ml\": 28.478}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-16d058916808b52c067e", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.106, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.768, \"dissolved_oxygen_pct\": 40.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.562, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.242, \"our_mmol_l_h\": 4.076, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.67, \"viable_cell_density_million_ml\": 11.935}, {\"agitation_rpm\": 237.431, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.656, \"dissolved_oxygen_pct\": 38.335, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.505, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.037, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.65, \"viable_cell_density_million_ml\": 14.863}, {\"agitation_rpm\": 279.488, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.103, \"dissolved_oxygen_pct\": 29.283, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.412, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.224, \"our_mmol_l_h\": 6.59, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.919, \"viable_cell_density_million_ml\": 19.511}, {\"agitation_rpm\": 284.496, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 17.928, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.359, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.68, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.745, \"viable_cell_density_million_ml\": 25.598}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-7957a75fcd4435784099", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.106, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.768, \"dissolved_oxygen_pct\": 40.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.562, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.242, \"our_mmol_l_h\": 4.076, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.67, \"viable_cell_density_million_ml\": 11.935}, {\"agitation_rpm\": 237.431, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.656, \"dissolved_oxygen_pct\": 38.335, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.505, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.037, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.65, \"viable_cell_density_million_ml\": 14.863}, {\"agitation_rpm\": 279.488, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.103, \"dissolved_oxygen_pct\": 29.283, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.412, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.224, \"our_mmol_l_h\": 6.59, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.919, \"viable_cell_density_million_ml\": 19.511}, {\"agitation_rpm\": 284.496, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 17.928, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.359, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.68, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.745, \"viable_cell_density_million_ml\": 25.598}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f29ba6ffa82256ec9cc5", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.106, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.768, \"dissolved_oxygen_pct\": 40.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.562, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.242, \"our_mmol_l_h\": 4.076, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.67, \"viable_cell_density_million_ml\": 11.935}, {\"agitation_rpm\": 237.431, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.656, \"dissolved_oxygen_pct\": 38.335, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.505, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.037, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.65, \"viable_cell_density_million_ml\": 14.863}, {\"agitation_rpm\": 279.488, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.103, \"dissolved_oxygen_pct\": 29.283, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.412, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.224, \"our_mmol_l_h\": 6.59, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.919, \"viable_cell_density_million_ml\": 19.511}, {\"agitation_rpm\": 284.496, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 17.928, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.359, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.68, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.745, \"viable_cell_density_million_ml\": 25.598}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-036f99a219f69db7620c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 213.106, \"airflow_vvm\": 0.214, \"cer_mmol_l_h\": 3.768, \"dissolved_oxygen_pct\": 40.479, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.562, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.242, \"our_mmol_l_h\": 4.076, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.786, \"viability_pct\": 98.67, \"viable_cell_density_million_ml\": 11.935}, {\"agitation_rpm\": 237.431, \"airflow_vvm\": 0.245, \"cer_mmol_l_h\": 4.656, \"dissolved_oxygen_pct\": 38.335, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.505, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.228, \"our_mmol_l_h\": 5.037, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.822, \"viability_pct\": 98.65, \"viable_cell_density_million_ml\": 14.863}, {\"agitation_rpm\": 279.488, \"airflow_vvm\": 0.242, \"cer_mmol_l_h\": 6.103, \"dissolved_oxygen_pct\": 29.283, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.412, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.224, \"our_mmol_l_h\": 6.59, \"ph\": 6.995, \"phase\": \"exponential\", \"temperature_c\": 36.819, \"viability_pct\": 98.919, \"viable_cell_density_million_ml\": 19.511}, {\"agitation_rpm\": 284.496, \"airflow_vvm\": 0.182, \"cer_mmol_l_h\": 8.009, \"dissolved_oxygen_pct\": 17.928, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.359, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.15, \"our_mmol_l_h\": 8.68, \"ph\": 6.996, \"phase\": \"production\", \"temperature_c\": 35.797, \"viability_pct\": 98.745, \"viable_cell_density_million_ml\": 25.598}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-270334b6d9b7a13a4dde", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.774, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.183, \"dissolved_oxygen_pct\": 37.877, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.055, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.925, \"our_mmol_l_h\": 5.647, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.493, \"viable_cell_density_million_ml\": 16.651}, {\"agitation_rpm\": 271.819, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.759, \"dissolved_oxygen_pct\": 36.347, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.009, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.852, \"our_mmol_l_h\": 6.25, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.503, \"viable_cell_density_million_ml\": 18.451}, {\"agitation_rpm\": 285.918, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 36.098, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.013, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.741, \"our_mmol_l_h\": 6.439, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.554, \"viable_cell_density_million_ml\": 20.062}, {\"agitation_rpm\": 285.662, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.985, \"dissolved_oxygen_pct\": 36.88, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.663, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.578, \"our_mmol_l_h\": 6.414, \"ph\": 6.981, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.131, \"viable_cell_density_million_ml\": 21.335}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-bf844a11753be6f2ca01", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.774, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.183, \"dissolved_oxygen_pct\": 37.877, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.055, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.925, \"our_mmol_l_h\": 5.647, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.493, \"viable_cell_density_million_ml\": 16.651}, {\"agitation_rpm\": 271.819, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.759, \"dissolved_oxygen_pct\": 36.347, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.009, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.852, \"our_mmol_l_h\": 6.25, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.503, \"viable_cell_density_million_ml\": 18.451}, {\"agitation_rpm\": 285.918, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 36.098, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.013, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.741, \"our_mmol_l_h\": 6.439, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.554, \"viable_cell_density_million_ml\": 20.062}, {\"agitation_rpm\": 285.662, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.985, \"dissolved_oxygen_pct\": 36.88, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.663, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.578, \"our_mmol_l_h\": 6.414, \"ph\": 6.981, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.131, \"viable_cell_density_million_ml\": 21.335}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b7b63015066fa7b69683", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.774, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.183, \"dissolved_oxygen_pct\": 37.877, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.055, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.925, \"our_mmol_l_h\": 5.647, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.493, \"viable_cell_density_million_ml\": 16.651}, {\"agitation_rpm\": 271.819, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.759, \"dissolved_oxygen_pct\": 36.347, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.009, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.852, \"our_mmol_l_h\": 6.25, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.503, \"viable_cell_density_million_ml\": 18.451}, {\"agitation_rpm\": 285.918, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 36.098, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.013, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.741, \"our_mmol_l_h\": 6.439, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.554, \"viable_cell_density_million_ml\": 20.062}, {\"agitation_rpm\": 285.662, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.985, \"dissolved_oxygen_pct\": 36.88, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.663, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.578, \"our_mmol_l_h\": 6.414, \"ph\": 6.981, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.131, \"viable_cell_density_million_ml\": 21.335}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-cf623ca79b685594b41d", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 255.774, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.183, \"dissolved_oxygen_pct\": 37.877, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.055, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.925, \"our_mmol_l_h\": 5.647, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.825, \"viability_pct\": 98.493, \"viable_cell_density_million_ml\": 16.651}, {\"agitation_rpm\": 271.819, \"airflow_vvm\": 0.283, \"cer_mmol_l_h\": 5.759, \"dissolved_oxygen_pct\": 36.347, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.009, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.852, \"our_mmol_l_h\": 6.25, \"ph\": 6.972, \"phase\": \"production\", \"temperature_c\": 35.792, \"viability_pct\": 98.503, \"viable_cell_density_million_ml\": 18.451}, {\"agitation_rpm\": 285.918, \"airflow_vvm\": 0.301, \"cer_mmol_l_h\": 6.425, \"dissolved_oxygen_pct\": 36.098, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.013, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.741, \"our_mmol_l_h\": 6.439, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.783, \"viability_pct\": 98.554, \"viable_cell_density_million_ml\": 20.062}, {\"agitation_rpm\": 285.662, \"airflow_vvm\": 0.302, \"cer_mmol_l_h\": 6.985, \"dissolved_oxygen_pct\": 36.88, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.663, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.578, \"our_mmol_l_h\": 6.414, \"ph\": 6.981, \"phase\": \"production\", \"temperature_c\": 35.782, \"viability_pct\": 98.131, \"viable_cell_density_million_ml\": 21.335}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-6f4ff28f72342fff3262", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.48, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.187, \"dissolved_oxygen_pct\": 36.903, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.085, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.921, \"our_mmol_l_h\": 5.679, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.798, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 16.561}, {\"agitation_rpm\": 269.739, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.747, \"dissolved_oxygen_pct\": 35.992, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.004, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.866, \"our_mmol_l_h\": 6.287, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.531, \"viable_cell_density_million_ml\": 18.435}, {\"agitation_rpm\": 285.469, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.437, \"dissolved_oxygen_pct\": 28.101, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.936, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.71, \"our_mmol_l_h\": 7.012, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.546, \"viable_cell_density_million_ml\": 20.606}, {\"agitation_rpm\": 284.284, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.027, \"dissolved_oxygen_pct\": 17.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.834, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.576, \"our_mmol_l_h\": 7.587, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.057, \"viable_cell_density_million_ml\": 22.452}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-180841ee2192db1d3099", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.48, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.187, \"dissolved_oxygen_pct\": 36.903, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.085, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.921, \"our_mmol_l_h\": 5.679, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.798, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 16.561}, {\"agitation_rpm\": 269.739, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.747, \"dissolved_oxygen_pct\": 35.992, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.004, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.866, \"our_mmol_l_h\": 6.287, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.531, \"viable_cell_density_million_ml\": 18.435}, {\"agitation_rpm\": 285.469, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.437, \"dissolved_oxygen_pct\": 28.101, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.936, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.71, \"our_mmol_l_h\": 7.012, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.546, \"viable_cell_density_million_ml\": 20.606}, {\"agitation_rpm\": 284.284, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.027, \"dissolved_oxygen_pct\": 17.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.834, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.576, \"our_mmol_l_h\": 7.587, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.057, \"viable_cell_density_million_ml\": 22.452}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-ede4834be18101d8b7cf", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.48, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.187, \"dissolved_oxygen_pct\": 36.903, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.085, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.921, \"our_mmol_l_h\": 5.679, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.798, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 16.561}, {\"agitation_rpm\": 269.739, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.747, \"dissolved_oxygen_pct\": 35.992, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.004, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.866, \"our_mmol_l_h\": 6.287, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.531, \"viable_cell_density_million_ml\": 18.435}, {\"agitation_rpm\": 285.469, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.437, \"dissolved_oxygen_pct\": 28.101, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.936, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.71, \"our_mmol_l_h\": 7.012, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.546, \"viable_cell_density_million_ml\": 20.606}, {\"agitation_rpm\": 284.284, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.027, \"dissolved_oxygen_pct\": 17.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.834, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.576, \"our_mmol_l_h\": 7.587, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.057, \"viable_cell_density_million_ml\": 22.452}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-34337762e913a1e03387", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 254.48, \"airflow_vvm\": 0.265, \"cer_mmol_l_h\": 5.187, \"dissolved_oxygen_pct\": 36.903, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.085, \"hour\": 88.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.921, \"our_mmol_l_h\": 5.679, \"ph\": 6.971, \"phase\": \"exponential\", \"temperature_c\": 36.798, \"viability_pct\": 98.431, \"viable_cell_density_million_ml\": 16.561}, {\"agitation_rpm\": 269.739, \"airflow_vvm\": 0.285, \"cer_mmol_l_h\": 5.747, \"dissolved_oxygen_pct\": 35.992, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.004, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.866, \"our_mmol_l_h\": 6.287, \"ph\": 6.966, \"phase\": \"production\", \"temperature_c\": 35.813, \"viability_pct\": 98.531, \"viable_cell_density_million_ml\": 18.435}, {\"agitation_rpm\": 285.469, \"airflow_vvm\": 0.251, \"cer_mmol_l_h\": 6.437, \"dissolved_oxygen_pct\": 28.101, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.936, \"hour\": 108.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.71, \"our_mmol_l_h\": 7.012, \"ph\": 6.965, \"phase\": \"production\", \"temperature_c\": 35.802, \"viability_pct\": 98.546, \"viable_cell_density_million_ml\": 20.606}, {\"agitation_rpm\": 284.284, \"airflow_vvm\": 0.18, \"cer_mmol_l_h\": 7.027, \"dissolved_oxygen_pct\": 17.742, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.834, \"hour\": 124.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.576, \"our_mmol_l_h\": 7.587, \"ph\": 6.98, \"phase\": \"production\", \"temperature_c\": 35.81, \"viability_pct\": 98.057, \"viable_cell_density_million_ml\": 22.452}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-40267dad3a64d596d109", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.397, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.079, \"dissolved_oxygen_pct\": 37.379, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.484, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.575, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.91, \"viable_cell_density_million_ml\": 16.349}, {\"agitation_rpm\": 280.232, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.062, \"dissolved_oxygen_pct\": 36.178, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.349, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.215, \"our_mmol_l_h\": 6.631, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.667, \"viable_cell_density_million_ml\": 19.521}, {\"agitation_rpm\": 284.19, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.527, \"dissolved_oxygen_pct\": 35.795, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.37, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.174, \"our_mmol_l_h\": 7.697, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.911, \"viable_cell_density_million_ml\": 23.717}, {\"agitation_rpm\": 285.478, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.196, \"dissolved_oxygen_pct\": 36.897, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.202, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.112, \"our_mmol_l_h\": 8.818, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.733, \"viable_cell_density_million_ml\": 28.456}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2788f3be7330cf5d9928", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.397, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.079, \"dissolved_oxygen_pct\": 37.379, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.484, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.575, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.91, \"viable_cell_density_million_ml\": 16.349}, {\"agitation_rpm\": 280.232, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.062, \"dissolved_oxygen_pct\": 36.178, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.349, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.215, \"our_mmol_l_h\": 6.631, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.667, \"viable_cell_density_million_ml\": 19.521}, {\"agitation_rpm\": 284.19, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.527, \"dissolved_oxygen_pct\": 35.795, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.37, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.174, \"our_mmol_l_h\": 7.697, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.911, \"viable_cell_density_million_ml\": 23.717}, {\"agitation_rpm\": 285.478, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.196, \"dissolved_oxygen_pct\": 36.897, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.202, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.112, \"our_mmol_l_h\": 8.818, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.733, \"viable_cell_density_million_ml\": 28.456}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-42fe9830c12f416c565c", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.397, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.079, \"dissolved_oxygen_pct\": 37.379, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.484, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.575, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.91, \"viable_cell_density_million_ml\": 16.349}, {\"agitation_rpm\": 280.232, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.062, \"dissolved_oxygen_pct\": 36.178, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.349, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.215, \"our_mmol_l_h\": 6.631, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.667, \"viable_cell_density_million_ml\": 19.521}, {\"agitation_rpm\": 284.19, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.527, \"dissolved_oxygen_pct\": 35.795, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.37, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.174, \"our_mmol_l_h\": 7.697, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.911, \"viable_cell_density_million_ml\": 23.717}, {\"agitation_rpm\": 285.478, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.196, \"dissolved_oxygen_pct\": 36.897, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.202, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.112, \"our_mmol_l_h\": 8.818, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.733, \"viable_cell_density_million_ml\": 28.456}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-df630e8556cb00cf97d7", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 252.397, \"airflow_vvm\": 0.262, \"cer_mmol_l_h\": 5.079, \"dissolved_oxygen_pct\": 37.379, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.484, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.241, \"our_mmol_l_h\": 5.575, \"ph\": 6.996, \"phase\": \"exponential\", \"temperature_c\": 36.813, \"viability_pct\": 98.91, \"viable_cell_density_million_ml\": 16.349}, {\"agitation_rpm\": 280.232, \"airflow_vvm\": 0.296, \"cer_mmol_l_h\": 6.062, \"dissolved_oxygen_pct\": 36.178, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.349, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.215, \"our_mmol_l_h\": 6.631, \"ph\": 6.992, \"phase\": \"exponential\", \"temperature_c\": 36.797, \"viability_pct\": 98.667, \"viable_cell_density_million_ml\": 19.521}, {\"agitation_rpm\": 284.19, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 7.527, \"dissolved_oxygen_pct\": 35.795, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.37, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.174, \"our_mmol_l_h\": 7.697, \"ph\": 6.998, \"phase\": \"production\", \"temperature_c\": 35.814, \"viability_pct\": 98.911, \"viable_cell_density_million_ml\": 23.717}, {\"agitation_rpm\": 285.478, \"airflow_vvm\": 0.3, \"cer_mmol_l_h\": 9.196, \"dissolved_oxygen_pct\": 36.897, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.202, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.112, \"our_mmol_l_h\": 8.818, \"ph\": 7.001, \"phase\": \"production\", \"temperature_c\": 35.789, \"viability_pct\": 98.733, \"viable_cell_density_million_ml\": 28.456}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-5e69f3346493c57bfb9f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.679, \"airflow_vvm\": 0.258, \"cer_mmol_l_h\": 5.132, \"dissolved_oxygen_pct\": 36.895, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.462, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.212, \"our_mmol_l_h\": 5.523, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.88, \"viable_cell_density_million_ml\": 16.283}, {\"agitation_rpm\": 279.95, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.072, \"dissolved_oxygen_pct\": 36.665, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.345, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.193, \"our_mmol_l_h\": 6.625, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.744, \"viable_cell_density_million_ml\": 19.422}, {\"agitation_rpm\": 283.955, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 7.529, \"dissolved_oxygen_pct\": 28.02, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.384, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.132, \"our_mmol_l_h\": 8.218, \"ph\": 6.99, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 24.138}, {\"agitation_rpm\": 285.962, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 9.232, \"dissolved_oxygen_pct\": 18.261, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.457, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 10.034, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 29.486}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-14d95646c852d4d38449", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.679, \"airflow_vvm\": 0.258, \"cer_mmol_l_h\": 5.132, \"dissolved_oxygen_pct\": 36.895, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.462, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.212, \"our_mmol_l_h\": 5.523, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.88, \"viable_cell_density_million_ml\": 16.283}, {\"agitation_rpm\": 279.95, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.072, \"dissolved_oxygen_pct\": 36.665, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.345, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.193, \"our_mmol_l_h\": 6.625, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.744, \"viable_cell_density_million_ml\": 19.422}, {\"agitation_rpm\": 283.955, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 7.529, \"dissolved_oxygen_pct\": 28.02, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.384, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.132, \"our_mmol_l_h\": 8.218, \"ph\": 6.99, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 24.138}, {\"agitation_rpm\": 285.962, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 9.232, \"dissolved_oxygen_pct\": 18.261, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.457, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 10.034, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 29.486}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-dae766e6d92daf00882f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.679, \"airflow_vvm\": 0.258, \"cer_mmol_l_h\": 5.132, \"dissolved_oxygen_pct\": 36.895, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.462, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.212, \"our_mmol_l_h\": 5.523, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.88, \"viable_cell_density_million_ml\": 16.283}, {\"agitation_rpm\": 279.95, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.072, \"dissolved_oxygen_pct\": 36.665, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.345, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.193, \"our_mmol_l_h\": 6.625, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.744, \"viable_cell_density_million_ml\": 19.422}, {\"agitation_rpm\": 283.955, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 7.529, \"dissolved_oxygen_pct\": 28.02, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.384, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.132, \"our_mmol_l_h\": 8.218, \"ph\": 6.99, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 24.138}, {\"agitation_rpm\": 285.962, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 9.232, \"dissolved_oxygen_pct\": 18.261, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.457, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 10.034, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 29.486}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-3dc38645c14d59f918a9", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 251.679, \"airflow_vvm\": 0.258, \"cer_mmol_l_h\": 5.132, \"dissolved_oxygen_pct\": 36.895, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.462, \"hour\": 76.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.212, \"our_mmol_l_h\": 5.523, \"ph\": 6.998, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.88, \"viable_cell_density_million_ml\": 16.283}, {\"agitation_rpm\": 279.95, \"airflow_vvm\": 0.297, \"cer_mmol_l_h\": 6.072, \"dissolved_oxygen_pct\": 36.665, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.345, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.193, \"our_mmol_l_h\": 6.625, \"ph\": 7.0, \"phase\": \"exponential\", \"temperature_c\": 36.814, \"viability_pct\": 98.744, \"viable_cell_density_million_ml\": 19.422}, {\"agitation_rpm\": 283.955, \"airflow_vvm\": 0.247, \"cer_mmol_l_h\": 7.529, \"dissolved_oxygen_pct\": 28.02, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 74.286, \"glucose_g_l\": 3.384, \"hour\": 96.0, \"inlet_filter_delta_p_kpa\": 12.429, \"lactate_g_l\": 1.132, \"our_mmol_l_h\": 8.218, \"ph\": 6.99, \"phase\": \"production\", \"temperature_c\": 35.818, \"viability_pct\": 98.797, \"viable_cell_density_million_ml\": 24.138}, {\"agitation_rpm\": 285.962, \"airflow_vvm\": 0.178, \"cer_mmol_l_h\": 9.232, \"dissolved_oxygen_pct\": 18.261, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 100.0, \"glucose_g_l\": 3.457, \"hour\": 112.0, \"inlet_filter_delta_p_kpa\": 25.0, \"lactate_g_l\": 1.097, \"our_mmol_l_h\": 10.034, \"ph\": 6.995, \"phase\": \"production\", \"temperature_c\": 35.803, \"viability_pct\": 98.931, \"viable_cell_density_million_ml\": 29.486}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-f28b97eb3a2054a9f3c6", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the primary equipment diagnostic from: high_filter_pressure_drop, high_jacket_temperature, low_feed_delivery, low_offline_ph, low_oxygen_transfer, unexpected_antifoam_addition. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.251, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.245, \"dissolved_oxygen_pct\": 41.082, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.442, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.763, \"our_mmol_l_h\": 3.519, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.509, \"viable_cell_density_million_ml\": 10.346}, {\"agitation_rpm\": 215.991, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.916, \"dissolved_oxygen_pct\": 39.651, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.166, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.959, \"our_mmol_l_h\": 4.246, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.509, \"viable_cell_density_million_ml\": 12.409}, {\"agitation_rpm\": 245.258, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.887, \"dissolved_oxygen_pct\": 37.341, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.166, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 4.819, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.563, \"viable_cell_density_million_ml\": 15.144}, {\"agitation_rpm\": 278.201, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.996, \"dissolved_oxygen_pct\": 35.604, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.777, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.823, \"our_mmol_l_h\": 5.309, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.55, \"viable_cell_density_million_ml\": 18.143}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-b28f1e39a6dc2e07bf1f", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Diagnose the cause and intervention. Choose cause from antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion and intervention from compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return JSON with exactly the keys cause and intervention. JSON whitespace is ignored.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.251, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.245, \"dissolved_oxygen_pct\": 41.082, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.442, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.763, \"our_mmol_l_h\": 3.519, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.509, \"viable_cell_density_million_ml\": 10.346}, {\"agitation_rpm\": 215.991, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.916, \"dissolved_oxygen_pct\": 39.651, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.166, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.959, \"our_mmol_l_h\": 4.246, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.509, \"viable_cell_density_million_ml\": 12.409}, {\"agitation_rpm\": 245.258, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.887, \"dissolved_oxygen_pct\": 37.341, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.166, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 4.819, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.563, \"viable_cell_density_million_ml\": 15.144}, {\"agitation_rpm\": 278.201, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.996, \"dissolved_oxygen_pct\": 35.604, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.777, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.823, \"our_mmol_l_h\": 5.309, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.55, \"viable_cell_density_million_ml\": 18.143}]", "input_type": "text", "image": "", "scorer": "json_semantic", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-4e192c9f5383efbb1d08", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the causal label from: antifoam_overdose, feed_interruption, feed_line_occlusion, gas_filter_restriction, ph_probe_bias, temperature_excursion. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.251, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.245, \"dissolved_oxygen_pct\": 41.082, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.442, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.763, \"our_mmol_l_h\": 3.519, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.509, \"viable_cell_density_million_ml\": 10.346}, {\"agitation_rpm\": 215.991, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.916, \"dissolved_oxygen_pct\": 39.651, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.166, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.959, \"our_mmol_l_h\": 4.246, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.509, \"viable_cell_density_million_ml\": 12.409}, {\"agitation_rpm\": 245.258, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.887, \"dissolved_oxygen_pct\": 37.341, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.166, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 4.819, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.563, \"viable_cell_density_million_ml\": 15.144}, {\"agitation_rpm\": 278.201, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.996, \"dissolved_oxygen_pct\": 35.604, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.777, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.823, \"our_mmol_l_h\": 5.309, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.55, \"viable_cell_density_million_ml\": 18.143}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-0f62001788e28b49fb98", "task": "process_trajectory_reasoning", "prompt": "This is a deterministic synthetic process trajectory, not a validated digital twin. Choose the intervention label from: compare_at_line_ph, replace_feed_line, restore_feed_profile, restore_temperature_control, stop_antifoam_and_verify_transfer, switch_to_redundant_gas_path. Return exactly one listed label.\n\nTrajectory excerpt:\n[{\"agitation_rpm\": 198.251, \"airflow_vvm\": 0.193, \"cer_mmol_l_h\": 3.245, \"dissolved_oxygen_pct\": 41.082, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 2.442, \"hour\": 64.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.763, \"our_mmol_l_h\": 3.519, \"ph\": 6.97, \"phase\": \"exponential\", \"temperature_c\": 36.778, \"viability_pct\": 98.509, \"viable_cell_density_million_ml\": 10.346}, {\"agitation_rpm\": 215.991, \"airflow_vvm\": 0.217, \"cer_mmol_l_h\": 3.916, \"dissolved_oxygen_pct\": 39.651, \"feed_flow_ratio\": 1.0, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 4.166, \"hour\": 72.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.959, \"our_mmol_l_h\": 4.246, \"ph\": 6.962, \"phase\": \"exponential\", \"temperature_c\": 36.781, \"viability_pct\": 98.509, \"viable_cell_density_million_ml\": 12.409}, {\"agitation_rpm\": 245.258, \"airflow_vvm\": 0.254, \"cer_mmol_l_h\": 4.887, \"dissolved_oxygen_pct\": 37.341, \"feed_flow_ratio\": 0.734, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 3.166, \"hour\": 84.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.972, \"our_mmol_l_h\": 4.819, \"ph\": 6.959, \"phase\": \"exponential\", \"temperature_c\": 36.776, \"viability_pct\": 98.563, \"viable_cell_density_million_ml\": 15.144}, {\"agitation_rpm\": 278.201, \"airflow_vvm\": 0.294, \"cer_mmol_l_h\": 5.996, \"dissolved_oxygen_pct\": 35.604, \"feed_flow_ratio\": 0.38, \"feed_pump_command_pct\": 100.0, \"gas_valve_command_pct\": 55.0, \"glucose_g_l\": 1.777, \"hour\": 100.0, \"inlet_filter_delta_p_kpa\": 3.0, \"lactate_g_l\": 1.823, \"our_mmol_l_h\": 5.309, \"ph\": 6.963, \"phase\": \"production\", \"temperature_c\": 35.806, \"viability_pct\": 98.55, \"viable_cell_density_million_ml\": 18.143}]", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "deterministic-process-trajectory-generator", "source_url": "", "license": "", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/00071198d059ba7f5914a526d124d28e6d010c92466da21d4a04cd5413362552/images/00071198d059ba7f5914a526d124d28e6d010c92466da21d4a04cd5413362552.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/003cee89357d9fe13516167fd67b609a164651b21934585648c740d2c3d86dc1/images/003cee89357d9fe13516167fd67b609a164651b21934585648c740d2c3d86dc1.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/00ae65c1c6631ae6f2be1a449902976e6eb8483bf6b0740d00530220832c6d3e/images/00ae65c1c6631ae6f2be1a449902976e6eb8483bf6b0740d00530220832c6d3e.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/0121d6759c5adb290c8e828fc882f37dfaf3663ec885c663859948c154a443ed/images/0121d6759c5adb290c8e828fc882f37dfaf3663ec885c663859948c154a443ed.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/01d44a26f6680c42ba94c9bc6339228579a95d0e2695b149b7cc0c9592b21baf/images/01d44a26f6680c42ba94c9bc6339228579a95d0e2695b149b7cc0c9592b21baf.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/0280fa8f60f6bcae0f97d93c28f60be194f9309ff610dc5845e60455b0f87c21/images/0280fa8f60f6bcae0f97d93c28f60be194f9309ff610dc5845e60455b0f87c21.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/0287e7ee5b007c91ae2bd7628d09735e70496bc6127ecb7f3dd043e04ce37426/images/0287e7ee5b007c91ae2bd7628d09735e70496bc6127ecb7f3dd043e04ce37426.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/02903040e19ddf92f452907644ad3822918f54af41dd85e5a3fe3e1b6d6f9339/images/02903040e19ddf92f452907644ad3822918f54af41dd85e5a3fe3e1b6d6f9339.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/03398329ced0c23b9ac3fac84dd53a87d9ffe4d9d10f1b5fe8df8fac12380776/images/03398329ced0c23b9ac3fac84dd53a87d9ffe4d9d10f1b5fe8df8fac12380776.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/03b9306f44e9b8951461623dcbd615550cdcf36ea93b203f2c8fa58ed1dffcbe/images/03b9306f44e9b8951461623dcbd615550cdcf36ea93b203f2c8fa58ed1dffcbe.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/03f583ec5018739f4abb9b3b4a580ac43bd933c4337ad8877aa18b1dfb59fc9a/images/03f583ec5018739f4abb9b3b4a580ac43bd933c4337ad8877aa18b1dfb59fc9a.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/0402a81e75262469925ea893b6706183832e85324f7b1e08e634129f5d522cdd/images/0402a81e75262469925ea893b6706183832e85324f7b1e08e634129f5d522cdd.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/04acab7636c4cf61d288a5962f15fa456b7bde31a021e5deedfbf51288e4001e/images/04acab7636c4cf61d288a5962f15fa456b7bde31a021e5deedfbf51288e4001e.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/05040e2e959c3f5632558fc9683fec88f0010026c555b499066346f67fdd0e13/images/05040e2e959c3f5632558fc9683fec88f0010026c555b499066346f67fdd0e13.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/0532c64c2fd0c4d3188cc751cdfd566b1cfba3d269358717295bab1504c7c275/images/0532c64c2fd0c4d3188cc751cdfd566b1cfba3d269358717295bab1504c7c275.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/05a8f65ebd0b30d3b210f30b4d640c847c2e710d0d135e0aeeaccbe1988e3b6e/images/05a8f65ebd0b30d3b210f30b4d640c847c2e710d0d135e0aeeaccbe1988e3b6e.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/06350c7cc618be442c15706db7a68e91f313758d224de4608f9b960106d4f9ca/images/06350c7cc618be442c15706db7a68e91f313758d224de4608f9b960106d4f9ca.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/06c779330d6d3447be21df2b9f05d1088f5b3b50dc48724fc130b1fd2896a68c/images/06c779330d6d3447be21df2b9f05d1088f5b3b50dc48724fc130b1fd2896a68c.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/072ff14c1d3245bf49ad6f1d4c71cdb18f1cb78a8e06fd2f53767e28f727cb81/images/072ff14c1d3245bf49ad6f1d4c71cdb18f1cb78a8e06fd2f53767e28f727cb81.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/07761fa39f60dc37022dbbe8d8694595fd5b77ceb2af2a2724768c8e524d6770/images/07761fa39f60dc37022dbbe8d8694595fd5b77ceb2af2a2724768c8e524d6770.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/077f026f4ab0f0bcc0856644d99cbf639e443ec4f067d7b708bc6cecac609424/images/077f026f4ab0f0bcc0856644d99cbf639e443ec4f067d7b708bc6cecac609424.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/07fb37aafa6626608af90c1e18f6a743f29b6b233d2e427dcd1102df6a916cf5/images/07fb37aafa6626608af90c1e18f6a743f29b6b233d2e427dcd1102df6a916cf5.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
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{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/52a4ac5a875be7a6c886035d54fb63f5f397dc43508c4831898f6b2f8debc7f3/images/52a4ac5a875be7a6c886035d54fb63f5f397dc43508c4831898f6b2f8debc7f3.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/52a6b8ae4c8e0a8a07a31b8e3f401d8811bf1942969c198e51dfcbd98520aa60/images/52a6b8ae4c8e0a8a07a31b8e3f401d8811bf1942969c198e51dfcbd98520aa60.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
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{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/53ad09e4348767bece0165884bf40c10b72ae18444e3f414a850442f02385efc/images/53ad09e4348767bece0165884bf40c10b72ae18444e3f414a850442f02385efc.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
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{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8e507d58f4c27cd2a82bee79fe27b069befd62a46fdaed20970a95a2ba819c7b/images/8e507d58f4c27cd2a82bee79fe27b069befd62a46fdaed20970a95a2ba819c7b.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8e8a7a14749d0b2e48de3d10e2e80063f17b165ad921c8afc0623f08500f3259/images/8e8a7a14749d0b2e48de3d10e2e80063f17b165ad921c8afc0623f08500f3259.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8ecdb93582b2d5270457b36651b62776256ade3aaa2d7432ae65c14f07432d49/images/8ecdb93582b2d5270457b36651b62776256ade3aaa2d7432ae65c14f07432d49.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8efed2e62c919e6d70a2ab548b1a33014877fe8a23f177ef25a9dee25ffe8842/images/8efed2e62c919e6d70a2ab548b1a33014877fe8a23f177ef25a9dee25ffe8842.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8f27ebc74164eddfe989a98a754dcf5a9c85ef599a1321de24bcf097df1814ca/images/8f27ebc74164eddfe989a98a754dcf5a9c85ef599a1321de24bcf097df1814ca.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8f6597cd978c060378177df76e554d0578b97eab471e237dbe0adc0dd0d93d63/images/8f6597cd978c060378177df76e554d0578b97eab471e237dbe0adc0dd0d93d63.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8f6e49e474ebb649a1e99662243d51a46cc9ba0c9c8f1efe2e2b662a81b48de1/images/8f6e49e474ebb649a1e99662243d51a46cc9ba0c9c8f1efe2e2b662a81b48de1.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8f94a80b95a881d0efdec36affc915dca9609f4cba8134c4a91b219d418778aa/images/8f94a80b95a881d0efdec36affc915dca9609f4cba8134c4a91b219d418778aa.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/8fdc34509a0c3721f7b5e235c8a93e1f553343aa17ad103a1e89e3509a3e1570/images/8fdc34509a0c3721f7b5e235c8a93e1f553343aa17ad103a1e89e3509a3e1570.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/91cc2e0d4d6e2c1ad59a8d63bcbe3e2ea8bc7f8e642e942a0113450181e73379/images/91cc2e0d4d6e2c1ad59a8d63bcbe3e2ea8bc7f8e642e942a0113450181e73379.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/92e7e86e765e05ce331c07a6d14f0a696eac7ee40058699243900f40b696d7aa/images/92e7e86e765e05ce331c07a6d14f0a696eac7ee40058699243900f40b696d7aa.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/92f31f591929a30e4309ab75185c96ff4314ce0a7ead2ed2c2171897ad1da0c7/images/92f31f591929a30e4309ab75185c96ff4314ce0a7ead2ed2c2171897ad1da0c7.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/930f246a8e4ff273a72a6e4b3cf8e8caff94fca4eaf1dbe6f93ba37b8195c0a0/images/930f246a8e4ff273a72a6e4b3cf8e8caff94fca4eaf1dbe6f93ba37b8195c0a0.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/93c5638e7e6433b5c9cc87c152bcbe28873d2f9d6a392cca0642520807542a77/images/93c5638e7e6433b5c9cc87c152bcbe28873d2f9d6a392cca0642520807542a77.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/93cfd412c7de5210bbd262ec3a602cfea65072e9272e9fce9b5339a5b9436eb7/images/93cfd412c7de5210bbd262ec3a602cfea65072e9272e9fce9b5339a5b9436eb7.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/942d56861fc83e195e9c559a000bb86627d8682f8dcc2300818458e5b6850dd0/images/942d56861fc83e195e9c559a000bb86627d8682f8dcc2300818458e5b6850dd0.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/94519eb45cbe1573252623b7ea06a8b43c19c930f5c9b685edb639d0db719ab0/images/94519eb45cbe1573252623b7ea06a8b43c19c930f5c9b685edb639d0db719ab0.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/947c0d94c8213ac7aaa41c4efc95d854246550298259cf1bb489654d0e969050/images/947c0d94c8213ac7aaa41c4efc95d854246550298259cf1bb489654d0e969050.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/94a5a37c3b1153d5c5aef2eca53c960b9f21f2ef1758209d7ec502ec324b03a3/images/94a5a37c3b1153d5c5aef2eca53c960b9f21f2ef1758209d7ec502ec324b03a3.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/9520aff4efe87bd8f3901652fa2dde9b4bc9c679325966145ce00c1ca33f35de/images/9520aff4efe87bd8f3901652fa2dde9b4bc9c679325966145ce00c1ca33f35de.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/953211bcc0192e2298087d30e708dba68def9e0c13a3ff3326a18b0962c63adc/images/953211bcc0192e2298087d30e708dba68def9e0c13a3ff3326a18b0962c63adc.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/958114e5f37d5e1420b410bd716753b3e874b175f2b6958ebf1ec2bdf776e41f/images/958114e5f37d5e1420b410bd716753b3e874b175f2b6958ebf1ec2bdf776e41f.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/9586e48a9a4353f11898a6a4b7475a91574e8af82e99c4b7a5e1f1b18f345f7a/images/9586e48a9a4353f11898a6a4b7475a91574e8af82e99c4b7a5e1f1b18f345f7a.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/9620c33d8ef2772dbc5bd152429f507bd7fafb27e12109003292b671e556b089/images/9620c33d8ef2772dbc5bd152429f507bd7fafb27e12109003292b671e556b089.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/97126a9791f0c1176e4563ad679a301dac27c59011f579e808bbd6e9f4cd1034/images/97126a9791f0c1176e4563ad679a301dac27c59011f579e808bbd6e9f4cd1034.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/97158b2fe38783d88d4e44ba1b7bc6c84f225f8b35fcccc2f9265c65f14e7c8b/images/97158b2fe38783d88d4e44ba1b7bc6c84f225f8b35fcccc2f9265c65f14e7c8b.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/9774c82396327929fea05e40ae153cabf0107178b2ae3e40a5709b409793887e/images/9774c82396327929fea05e40ae153cabf0107178b2ae3e40a5709b409793887e.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-2659d956a0a11ce16b72", "task": "microscopy_label_reasoning", "prompt": "Estimate the visible-nuclei count range in the supplied microscopy image. Choose exactly one label: 0-10, 11-25, 26-50, 51-100, 101-200, 201+. Return only the label.", "input_type": "image_text", "image": "s3://capicu-research/raw/bbbc038/raw/extracted/stage1_train/98a463483fe3a56deacc8bc00ab8aa62668bd40ad0c70bbe7deb10d3e4aeb0c0/images/98a463483fe3a56deacc8bc00ab8aa62668bd40ad0c70bbe7deb10d3e4aeb0c0.png", "scorer": "normalized_exact_match", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "cc0-1.0", "evaluation_role": "discrimination", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3f10d1f217e73d08cece", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b6c67e626ca515523a2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-953b6479857b83b30137", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db5873ddd2414c97da17", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-303598af6a617f9f6199", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5df58a7a8711bf7aed8c", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-57bdcafe58f53887ad8b", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-35b74c2ea5f27e1b1d2e", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=fluorescence\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c7b2ceaf7fa121d61d05", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-263d11cc44a936f60929", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a3a4c2396cce08f54d32", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-032e6f82ece02c12efce", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7b60e8bedabca84718d1", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4413f4bf57758bf6ec74", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-53de0d622dd25864bf0d", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0b29b43b92eafa6c9b45", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=phase-contrast\nsource_id=nist-ipsc", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "nist-ipsc", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a2ca342b262f2cf8fa8f", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4089afa99dbf4dab29f3", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-b724dda72765abf94fa0", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-8abed092529aff482ff2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-87302d8497a2f2766a43", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7dafd30ab0fe9bdfb7a6", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5363976fb8339449100a", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2e65d77a7836260ce4e5", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a2ca342b262f2cf8fa8f", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4089afa99dbf4dab29f3", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-b724dda72765abf94fa0", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-8abed092529aff482ff2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-87302d8497a2f2766a43", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7dafd30ab0fe9bdfb7a6", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5363976fb8339449100a", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2e65d77a7836260ce4e5", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a2ca342b262f2cf8fa8f", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4089afa99dbf4dab29f3", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-b724dda72765abf94fa0", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-8abed092529aff482ff2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-87302d8497a2f2766a43", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7dafd30ab0fe9bdfb7a6", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5363976fb8339449100a", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2e65d77a7836260ce4e5", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a2ca342b262f2cf8fa8f", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4089afa99dbf4dab29f3", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-b724dda72765abf94fa0", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-8abed092529aff482ff2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-87302d8497a2f2766a43", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7dafd30ab0fe9bdfb7a6", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5363976fb8339449100a", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2e65d77a7836260ce4e5", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a2ca342b262f2cf8fa8f", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly two keys: \"fields\" and \"values\". fields must equal [\"modality\",\"source_id\"]; values must follow that order. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4089afa99dbf4dab29f3", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with exactly is_bench_reserved=true, modality, and source_id, with keys alphabetized. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-b724dda72765abf94fa0", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with source_id_length as the exact character count and source_id_upper as the uppercase source_id. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-8abed092529aff482ff2", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with modality_reversed and the final six source_id characters as source_id_suffix. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-87302d8497a2f2766a43", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one key \"record\". record must contain modality and source_tokens. Split source_id on -, _, /, ., or :, remove empty and duplicate tokens, and sort tokens alphabetically. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7dafd30ab0fe9bdfb7a6", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with record equal to modality|source_id and token_count equal to the number of alphanumeric source_id tokens. Alphabetize keys. No other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5363976fb8339449100a", "task": "verifiable_instruction_following", "prompt": "Return exactly one minified JSON object with keys modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2e65d77a7836260ce4e5", "task": "verifiable_instruction_following", "prompt": "Return minified JSON with one top-level key \"record\". Its value must contain only modality and source_id in alphabetical order. Include no other text.\n\nmodality=nuclei-microscopy\nsource_id=bbbc038-bench-reserved", "input_type": "text", "image": "", "scorer": "json_exact", "source_id": "bbbc038-bench-reserved", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-94d7e1f680cc0c140cf5", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nAncestral sequence reconstruction (ASR) offers a revolutionary approach to resurrect functional proteins, yet its potential in transporter engineering remains underexplored. Here, we pioneered the application of ASR to reconstructing ancestral xylose transporters, addressing the persistent challenge of glucose-mediated inhibition of xylose uptake in Saccharomyces cerevisiae during xylose co-fermentation. Through rigorous ASR analysis, we reconstructed ancestral xylose transporters (Xt) and selected two candidates—Xt3 (approximately 140 million years old) and Xt7 (approximately 40 million years old)—based on their phylogenetic positioning, degree of sequence divergence from extant homologs, and predicted structural integrity. Functional characterization demonstrated that both Xt3 and Xt7 significantly enhance xylose uptake efficiency and mitigate glucose-induced repression. In fermentation experiments with mixed sugars (40 g/L xylose and 40 g/L glucose) within 72 h, recombinant S. cerevisiae expressing Xt3 achieved 22.75 g/L xylose consumption, surpassing the benchmark N326F Xltr1p (16.22 g/L) by 40.27% and outperforming Xt7 (21.36 g/L) by 6.51%, highlighting Xt3 as the most efficient transporter. Molecular docking suggested a potentially more favorable binding mode for xylose in the ancestral transporters (binding affinity: −3.68 kcal/mol for Xt3 vs. −3.15 kcal/mol for N326F Xltr1p ). Molecular dynamics simulations further demonstrated that the ancestral transporters formed complexes with xylose that exhibited faster convergence to a stable state and maintained significantly greater conformational stability throughout the simulation compared to the N326F Xltr1p complex. These computational insights provide a plausible structural basis for their enhanced performance. This work contributes to the advancement of lignocellulosic biorefinery technology and provides a practical reference for resurrecting other valuable proteins using ASR’. The online version contains supplementary material available at 10.1186/s40643-025-00995-1. Keywords: Ancestral sequence reconstruction, Xylose transporter, Glucose/xylose co-utilization, Saccharomyces cerevisiae\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00000.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12770209", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-d98573955303618c1acb", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nNatural products (NPs) and their analogues have long underpinned therapies in humans, animals, and plants health, yet, discovering truly novel scaffolds remains a formidable challenge, even with the enormous diversity offered. Over the last two decades, breakthroughs in bioinformatics, cheminformatics, advanced analytical methods, synthetic biology toolkits, and optimized microbial culture have surmounted many of the bottlenecks that stalled NP research in the 1990s and 2000s. Researchers now deploy innovative extraction and purification protocols alongside high-throughput dereplication tools to fish trace metabolites out of complex matrices. These combined approaches not only enable the discovery and rigorous characterization of biosynthesized metabolites, bio-transformed analogues and new chemical entities but also allow precise tuning of biosynthetic gene clusters (BGCs) and culture conditions- modulation and optimization, dramatically improving yield, scalability, and cost-efficiency. Several of these newly unearthed compounds exhibit unique bioactivities that directly inspire drug-development programs against metabolic disorders, cancer drug resistance, and infectious diseases. In this review, we present an up-to-date, concise roadmap of natural product discovery (NPD), majorly covering strategies for awakening silent BGCs, genome mining, and late-stage diversification systems, and we discuss the current limitations and perspectives of rational NPD. Keywords: Natural products, Culturing modulation, Unexplored reservoirs, Genome mining, Natural product diversification\n\nSynthetic evaluation record: lot number LOT-00001. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12775259", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-f0cf168de4e60db0c1c3", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nHigh-yield influenza virus production is essential for efficient vaccine manufacturing to support global demands. Using Madin-Darby canine kidney (MDCK) cells to produce influenza viruses is an attractive alternative to the conventional method of manufacturing vaccines using embryonated eggs. MDCK cells exhibit heterogeneity which can impact viral yields. However, the factors driving the variation between MDCK cells are not fully understood. Utilizing an untargeted liquid chromatography-mass spectrometry lipidomic approach, we investigated two proprietary MDCK clones (C59 and C113) provided by Sartorius (Germany) that differ in biochemical and viral production properties and examined their lipid profiles and dynamics upon influenza A virus (IAV) infection between 24 and 72 h. C113, a high-yield clone, displayed elevated levels across all lipid classes, aside from ether lipids compared to C59, a clone with superior growth properties. IAV infection in clone C59 and C113 displayed key differences, specifically triacylglycerols. Analysis of progeny virions from C59 and C113 clones revealed subtle differences with a positive correlation in lipid profile ( R 2 = 0.77), suggesting similar lipid raft domains between clones. Overall, these findings highlight specific cellular lipid signatures associated with high-yield production and demonstrate the value of integrating lipidomics methods into biomanufacturing pipelines, providing complimentary quality assurance markers. The online version contains supplementary material available at 10.1038/s41598-025-33499-1. Subject terms: Biochemistry, Biological techniques, Biotechnology, Microbiology\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00002, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12783198", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e1953be5c2b7923bbe3b", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\n2-Hydroxy-4-Methoxybenzaldehyde (2H4MB) is a valuable aromatic compound with applications in flavour, fragrance, and pharmaceuticals. Because of its endangered status and root-specific accumulation, its production in native plants is restricted. In order to increase 2H4MB yield, this study emphasises recent developments in metabolic engineering, synthetic biology, in vitro culture methods, and AI-assisted route prediction. This review discussed about how CRISPR-based genome editing can be used to modify important biosynthetic genes and regulatory components, as well as how predictive machine learning techniques can be used to improve production conditions. Inadequate genetic resources, poorly understood biosynthetic pathways, and a dearth of reliable transformation systems are among the present constraints. The work highlights the importance of using integrative plant biotechnology techniques to fully realise the industrial and medicinal potential of this underutilised chemical.\n\nSynthetic evaluation record: commercial lot number=LOT-00003. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12786920", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-99f2f15efab9ce86db9e", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe exploration and multiscale manufacturing in outer space hold vital significance Chemical and biological nano/micro/meso-scale manufacturing offer strategies to address challenges Emerging advances encompass novel manufacturing technologies and resource utilization strategies across orbital space stations, the Moon, Mars, and asteroids Emerging technologies like synthetic biology and artificial intelligence are discussed Key innovations, cross-disciplinary applications, and limitations are highlighted Keywords: In-space manufacturing, Biomanufacturing, Chemical manufacturing, Long-term space mission, In-situ resource utilization\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00004.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12791114", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5eaa59602e0676189054", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nBone fractures represent a significant global healthcare burden. Although fractures typically heal on their own, some fail to regenerate properly, leading to nonunion, a condition that causes prolonged disability, morbidity, and mortality. The challenge of treating nonunion fractures is further complicated in patients with underlying bone disorders where systemic and local factors impair bone healing. Traditional treatment approaches, including autografts, allografts, xenografts, and synthetic biomaterials, face limitations such as donor site pain, immune rejection, and insufficient mechanical strength, underscoring the need for alternative strategies. Biologic therapies have emerged as promising tools to enhance bone regeneration by leveraging the body’s natural healing processes. This review explores the critical role of conventional and emerging biologics in fracture healing. We categorize biologic therapies into protein-based treatments, gene and transcript therapies, small molecules, peptides, and cell-based therapies, highlighting their mechanisms of action, advantages, and clinical relevance. Finally, we examine the potential applications of biologics in treating fractures associated with bone disorders such as osteoporosis, osteogenesis imperfecta, rickets, osteomalacia, Paget’s disease, and bone tumors. By integrating biologic therapies with existing biomaterial-based strategies, these innovative approaches have the potential to transform clinical management and improve outcomes for patients with difficult-to-heal fractures. Subject terms: Bone, Pathogenesis\n\nSynthetic evaluation record: lot number LOT-00005. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12791149", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-d4b24470453167a532e7", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe production of odd-chain fatty acids (OCFAs) is gaining increasing importance due to their diverse applications in food, chemical, and biofuel industries. These fatty acids, which are relatively rare in nature, can be produced from renewable carbon sources through microbial fermentation processes. This review covers the significance of OCFAs in the market and their occurrence, followed by a detailed exploration of their production in mixed and single strain cultures. Specifically, the anaerobic fermentation (AF) conditions and feedstocks used to produce short OCFAs (SOCFAs), such as propionic, valeric, and heptanoic acids are discussed. Additionally, the production of long OCFAs (LOCFAs) by single strains is focusing on yeast, bacteria, and microalgae. Novel approaches for LOCFAs generation from waste carbon sources are also reviewed. This work delves both into the manipulation of microbial communities covering bioaugmentation and process optimization for bioenrichment in open mixed cultures and genetic manipulation in single-strain systems. Finally, the potential for scalable and sustainable production of OCFAs through microbial processes is discussed, as well as the technological advances needed to optimize these pathways.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00006, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12799634", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-1b61c335f8a6e2e3e6fe", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nPalm oil is the world’s most widely used vegetable oil, with a sizeable impact on the environment. As an alternative, microalgae are considered oil producers since they produce a variety of fatty acids (FA) depending on growth conditions. A collection of ten microalgae strains naturally producing oils similar in composition to palm oil was selected, and the effects of cultivation regime and varying light intensity on their growth and FA production and composition were analysed. To achieve high biomass density as well as total fatty acid (TFA) content, the optimum irradiance of 400 µmol photons m −2 s −1 in a photoautotrophic regime was determined for most of the strains. The growth rates of Scenedesmus and Desmodesmus strains in general were approximately twice as high as Chlamydomonas . The highest TFA content was found in S. obliquus CCALA 455 and D. subspicatus CCALA 467, grown photoautotrophically, reaching the values of about 66% and 58% of their dry weight, respectively. Moreover, the content of palmitic (PA), oleic (OA) and linoleic acid (LA) of about 39%, 30% and 14% of TFA, respectively, determined in D. subspicatus CCALA 467 was closest to that in palm oil (44% of PA, 39% of OA and 10% of LA). Eight of the ten microalgae strains were capable of heterotrophic growth, although their production under this regime has not been considered suitable in terms of TFA and individual FA content. • The optimum irradiance of 400 µmol photons m −2 s −1 was determined • CCALA 467 produces selected FAs in amounts close to those in palm oil • TFA content (% of dry weight) in CCALA 467 is 1.6-fold higher than in the palm The online version contains supplementary material available at 10.1007/s00253-025-13682-0. Keywords: Microalga, Biomass, Photoautotrophic and heterotrophic cultivation, Fatty acid, Palm oil\n\nSynthetic evaluation record: commercial lot number=LOT-00007. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12799715", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5f64f10157d0f5fe8fdc", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nToxoplasmosis is a widespread zoonotic disease that poses risks to pregnant women and immunocompromised individuals. Despite considerable efforts, no licensed vaccines are currently available for humans or animals. Rational vaccine design increasingly relies on immunoinformatics approaches to identify immunodominant epitopes and key immunological features. This study aimed to characterise the Toxoplasma gondii ‐secreted protein with an altered thrombospondin repeat (TgSPATR) using immunoinformatics tools to evaluate its suitability as a vaccine candidate. A comprehensive panel of bioinformatics servers was used to predict allergenicity, solubility, antigenicity, secondary and tertiary structures, post‐translational modification (PTM) regions, and B‐ and T‐cell epitopes, followed by in silico immune simulation. TgSPATR consists of 534 amino acids with an estimated molecular weight of ∼57 kDa. The aliphatic index (65.71) and GRAVY score (–0.507) indicate moderate thermostability and an overall hydrophilic nature. Additionally, a total of 120 PTM sites were predicted, including phosphorylation, O ‐ and N ‐glycosylation, and palmitoylation sites. Secondary structure analysis (GOR IV, SOPMA, and NetSurfP‐3.0) revealed a predominance of random coils. Moreover, multiple servers (BcePred, SVMTriP, ABCpred, IEDB, ElliPro, and CTLpred) identified several high‐scoring B‐ and T‐cell epitopes capable of binding MHC class I and II molecules. According to SAVES v6.1, 74.1% and 93.8% of the residues of the initial and refined 3D models were located in favoured regions, indicating improved structural quality after refinement. In line with this, the ERRAT score also increased from 89.557 to 95.046. TgSPATR was predicted to be immunogenic and non‐allergenic. Finally, virtual immune simulation using the C‐ImmSim server showed that TgSPATR can elicit both humoral and cellular immune responses following three injections. This study provides foundational evidence that TgSPATR possesses key immunogenic properties and may serve as a promising vaccine candidate against acute and chronic toxoplasmosis. Nonetheless, wet‐lab experiments are required to validate these computational findings. Keywords: bioinformatics, in silico, secreted protein with an altered thrombospondin repeat (SPATR), Toxoplasma gondii , vaccine\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00008.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12800914", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-9c41adf62132c6a819c6", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nTo guarantee consistent quality of therapeutic proteins, the relationship between manufacturing process parameters and glycosylation profiles must be investigated and understood. The most important manufacturing step to investigate is the cell culture unit operation, where glycoprotein structure is highly dependent on raw materials, cell line genetics, and process control ranges. Because of the critical role glycosylation plays in certain drug mechanisms of action, the relationship between specific process inputs and glycosylation have been documented extensively. However, despite the extensive body of published work, general relationships between different cell culture conditions and glycosylation profiles remain fragmented across diverse studies, hindering systematic analysis and data-driven decision-making. To better elucidate these general relationships from published research, we introduce an innovative framework that leverages text mining and knowledge graph technologies to automatically extract, integrate, and visualize complex relationships from scientific literature, enabling actionable insights for biopharmaceutical process (bioprocess) development. Our methodology centers on the design and development of a specialized text-mining pipeline to extract and quantify relationships between cell culture conditions (raw materials, cell line genetics, and process control ranges) and glycosylation profiles from unstructured scientific literature. To enhance precision, we implement a dual normalization strategy: 1) dictionary-based concept standardization to reconcile term variants, and 2) ontological classification to organize entities into hierarchically structured categories. These curated relationships are then systematically integrated into a knowledge graph, which not only captures direct parameter-outcome associations but also reveals higher-order indirect connection through graph, providing a comprehensive view of bioprocess interactions. We present an intuitive web-based interface that enables researchers to dynamically explore and visualize complex bioprocess relationships through interactive queries. The system demonstrates robust performance with an 88% F1-score in relation extraction, effectively revealing hidden relationships between process parameters and glycan attributes. By combining scalable knowledge graph technology with interpretable analytics, our solution empowers pharmaceutical researchers to optimize therapeutic glycan profiles and accelerate manufacturing process development. This advancement represents a significant step forward in data-driven bioprocess optimization.\n\nSynthetic evaluation record: lot number LOT-00009. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12803468", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e28662f288a96909fa8e", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nSecretory protein production by microbial hosts simplifies product recovery and is therefore preferred over intracellular production. Efficient secretion of heterologous proteins by bacteria requires the identification of optimal signal peptides (SPs), a step that often limits process development. Using Corynebacterium glutamicum as a model host, we established a modular cloning system enabling rapid assembly of expression plasmids for secretory protein production. Screening a library of 30 individually cloned endogenous SPs with a fungal cutinase as target protein demonstrated that several native SPs achieved substantially higher secretion levels than the widely used Bacillus subtilis NprE reference SP. To accelerate SP discovery, we developed a one‐pot approach in which C. glutamicum was directly transformed with a single modular cloning mixture containing all 30 SPs. Combined with the AutoBioTech high‐throughput platform for cultivation, harvesting, and protein quantification, this strategy enabled screening of several hundred clones in parallel. Superior SPs were rapidly identified not only for cutinase but also for four polyethylene terephthalate hydrolases (PETases). This streamlined workflow significantly reduces time and cost for selecting effective SPs and provides a versatile platform for advancing secretory protein production in C. glutamicum .\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00010, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12810402", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-89ec70617a61f28fa49e", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nAntimicrobial resistance is a growing global health crisis, responsible for nearly five million deaths annually and projected to double by 2050 as conventional antibiotics fail against multidrug-resistant pathogens. AMR is aggravated by antibiotic misuse, weak regulations, inadequate prevention, high treatment costs, and the limited discovery of new antimicrobials. In this context, antimicrobial peptides, including natural, synthetic, and computationally designed variants, have emerged as promising alternatives. AMPs display broad-spectrum antibacterial, antifungal, antiviral, antiparasitic, antibiofilm, and immunomodulatory activities, with a lower tendency to induce resistance. Their mechanisms include membrane disruption, intracellular targeting, immune modulation, and selective binding to negatively charged microbial membranes. Structural features such as α-helices, β-sheets, cyclic motifs, and post-translational modifications enhance potency and specificity. Recent advances in chemical modification, recombinant expression systems, nanotechnology, and AI-driven computational approaches have improved AMP stability, bioavailability, and therapeutic efficacy. Synthetic derivatives like innate defense regulators and conjugated AMPs further enhance immunomodulatory properties and reduce toxicity, while combination therapies increase effectiveness. Challenges remain, including degradation, short half-life, production costs, and microbial defenses such as biofilms and efflux pumps. Nevertheless, high-throughput sequencing and screening, structural biology, and structure–activity relationship studies continue to accelerate AMP development, positioning them as vital next-generation therapeutics against AMR.\n\nSynthetic evaluation record: commercial lot number=LOT-00011. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12813028", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5c93e18b487a3295710b", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\n\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00012.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12813140", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e46354bb9d6616e27c8a", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nHemophilia is an inherited disorder characterized by impaired blood clotting caused by mutations in the genes responsible for producing coagulation factor (F) VIII (hemophilia A, HA) or FIX (hemophilia B, HB). Current treatment primarily relies on replacement therapy, involving frequent and costly infusions of FVIII or FIX concentrates. While effective, these treatments come with the risk of developing neutralizing antibodies (inhibitors) against the infused factor. In recent years, non‐factor replacement therapies have emerged as innovative treatment options, offering enhanced efficacy especially for patients with inhibitors. Despite their advantages, these approaches still fall short of providing a definitive, long‐term cure. Since hemophilia is a monogenic disease, it presents an excellent opportunity for cell and gene therapy approaches aimed at achieving durable treatment and potentially a cure. Over the past three decades, remarkable advancements have been made in hemophilia gene therapy, culminating in the approval of Valoctocogene roxaparvovec (ROCTAVIAN, AAV‐FVIII) and Etranacogene dezaparvovec (HEMGENIX, AAV‐FIX) for patients with severe HA and HB, respectively. Nevertheless, gene therapy poses questions regarding its long‐term efficacy and safety. This review synthesizes findings from clinical trials, addresses persistent challenges in hemophilia gene therapy, and underscores the biological constraints and limitations inherent to viral vector‐based approaches.\n\nSynthetic evaluation record: lot number LOT-00013. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12813738", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e472128136a0e939e67f", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe A-Cell Case Study published by the “Alliance of Regenerative Medicine” illustrates how Quality-by Design can be applied to the manufacturing of Advanced Therapeutical Medicinal Products (ATMPs), using Chimeric Antigen Receptor (CAR)-T cell therapy as a ‘model’ process. However, no emphasis is given to different degrees of automation in this study. CAR-T cell therapies have been developed for various forms of leukemia, such as Acute Lymphoblastic Leukemia (ALL) or Non Hodgkin-Lymphoma (NHL). As more CAR-T cell therapies reach market approval and are being considered as first- or second line treatments, the economic efficiency and scalability of the chosen production modality become increasingly critical. Currently, academic and industrial manufacturers employ a range of approaches, from fully manual and open processing to closed and automated systems. New technologies, investments and cleanroom space requirements must be considered to assess economic and spatial efficiency in cell therapy manufacturing. This study analyses the costs and space requirements of different production modalities for autologous CAR-T cell production. The analysis shows that a higher degree of automation can reduce manufacturing costs by lowering personnel costs, cleanroom grade requirements and spatial footprint. It emphasizes the importance of maximizing cleanroom efficiency to support the scalable production of cell therapies as clinical demand grows. These results underscore the need for both industry and academia to consider automated production as a strategic approach to optimize resource use in CAR-T cell manufacturing.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00014, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12816348", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4b0f0bfa4e87b335aae9", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nEscherichia coli ( E. coli ) has long served as a versatile workhorse for recombinant protein production. As synthetic biology expands the demand for coordinated expression of multiple genes, co-expression systems in E. coli have evolved from basic dual-gene constructs to programmable, polygenic expression platforms. This review critically examines the major strategies enabling multigene co-expression in E. coli, including internal ribosome entry sites (IRES), 2A self-cleaving peptides, dual-promoter cassettes, multicistronic operons, and multi-plasmid configurations. We highlight the mechanistic principles, design trade-offs, and regulatory bottlenecks associated with each approach, such as translational imbalance, inclusion body formation, and plasmid compatibility. Real-world applications in metabolic engineering, complex protein assembly, and biomanufacturing are analyzed to demonstrate the functional advantages of these systems. Finally, we explore emerging programmable toolkits that integrate modular architecture, expression modeling, and AI-assisted design, paving the way for next-generation synthetic expression control in microbial chassis. This review offers a comprehensive and strategic roadmap for researchers engineering multi-gene systems in E. coli and beyond.\n\nSynthetic evaluation record: commercial lot number=LOT-00015. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12819055", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-282ccc6aca7151ce0267", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nWhey, a by‐product of the cheese manufacturing industry, represents one of the most abundant and polluting effluents in the global food industry. Despite traditionally being underutilized and often discarded, its rich nutrient profile, particularly protein and lactose, has increasingly sparked an interest in its value within biotechnological processes. This review analyses the potential of whey as a sustainable substrate for the microbial production of value‐added bioproducts, focussing on L‐threonine production as a strategic case study, while addressing the environmental impact of inadequate disposal and current utilization strategies. A comparative analysis with other agroindustrial waste demonstrates whey’s competitive advantages in terms of composition, cost‐effectiveness and sustainability metrics. Furthermore, L‐threonine biological and industrial importance, and the most relevant advances in metabolic engineering, optimized fermentation and emerging tools such as optogenetics and machine learning are discussed, as they facilitate enhanced L‐threonine yields through the creation of robust, high‐producing strains. Technoeconomic analysis at pilot scale (33.8 tons/year) indicates that whey‐based production offers a comparative cost advantage of 7.4% over glucose‐based processes (20.55 USD/kg vs. 22.20 USD/kg). While absolute costs at pilot scale exceed current industrial market prices (1.31–1.66 USD/kg)—reflecting typical scale effects—the demonstrated comparative advantage and substantial environmental benefits (waste valorization, elimination of disposal costs and circular economy alignment) position whey‐based L‐threonine production as a strategic biorefinery opportunity with significant potential for industrial‐scale implementation. This cost benefit is primarily driven by the lower market price of whey compared to commercial glucose substrates, which compensates for the slightly higher downstream processing costs (5.90 vs. 5.40 USD/kg) required for complex matrices. Downstream processing considerations, including recovery, purity requirements and economic viability, are comprehensively addressed. This review concludes that whey, far from being merely a pollutant, has the characteristics required to become an asset for biotechnology. Utilizing whey as a culture medium for L‐threonine production by E. coli in bioreactors not only offers a solution to mitigate a significant environmental issue but also opens a path for the cost‐effective, sustainable production of a globally high‐demand amino acid. Whey represents a strategic biorefinery platform with potential for industrial‐scale implementation. Continued research and development in this area are fundamental to fully realizing this potential.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00016.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12828667", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5ef021057d77682ab73f", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nYarrowia lipolytica, a versatile and oleaginous yeast, has garnered significant attention as a promising microbial chassis for producing a vast array of important metabolic products, including trace minerals, vitamins, amino acids, protein and peptides, carbohydrates, and single-cell oil (SCO), primarily in the form of saturated high-value lipids like cocoa-butter equivalents and mono-unsaturated fatty acids (MUFAs). The US FDA has designated Y. lipolytica as a “safe-to-use organism” and given it GRAS (generally regarded as safe) classification for the synthesis of EPA, citric acid, and erythritol. Co-culturing multiple interspecies microorganisms together has proven to be a feasible and comparatively more efficient strategy than monoculture to target the degradation of waste components as a substrate and boost the production of significant metabolites. In recent years, a great deal of research has been devoted to exploring the potential of this host for the biosynthesis of valuable compounds from a wide variety of strategies. Despite ongoing efforts to improve our understanding of xylose metabolism in this yeast, there has been a notable lack of research focused specifically on the biosynthesis of natural products using xylose as a precursor, which is the second most abundant sugar in lignocellulosic biomass. This review also explores recent advances in the genetic modification of Y. lipolytica to enhance its ability to assimilate xylose and produce various secondary metabolites by using xylose as a substrate.\n\nSynthetic evaluation record: lot number LOT-00017. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12833036", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-ca9a8da80b86a6674059", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe emergence of allogeneic, universal chimeric antigen receptor (CAR) T cell therapies requires intensified and scalable manufacturing workflows supported by representative scale-down models (SDMs) to enable efficient process development and future large-scale production of off-the-shelf therapies. Here, we present a 7-day CAR-T cell expansion process intensified via perfusion of serum-free medium in a 2 L Univessel® Single-Use stirred-tank bioreactor (STR), consistently achieving 30 × 10 6 cells/mL, corresponding to 113 ± 7 anti-CD19 CAR-T doses per batch. Parallel runs in 250 mL Ambr® 250 STRs conducted at equivalent volumetric power input ( P/V ) of ∼8.78 W/m 3 demonstrated comparable process performance and final product quality, with univariate and multivariate analyses of cell growth, phenotype, cytotoxicity, and cytokine secretion validating the Ambr® 250 as a predictive SDM for the 2 L process. Integrating capacitance sensing in the 2 L STR enabled robust monitoring of viable cell concentrations in real-time, with strong correlation to offline measurements (R 2 = 0.98). For downstream processing, the Ksep® 400 was used to automate CAR-T cell harvesting, concentration, and washing at the 2 L scale, achieving >90% product recovery and nine-fold volume reduction without impacting product quality attributes compared to manual methods. This study establishes a scalable CAR-T manufacturing workflow supported by a predictive SDM, providing an efficient platform for process development and scale-up to enable future large-scale production of allogeneic CAR-T cell therapies.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00018, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12833271", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-f6ca76a3ea09046e1fed", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMicroalgae and cyanobacteria are emerging as sustainable alternatives to chemical fertilizers and pesticides, offering nutrient recycling, stress mitigation, and environmental restoration within the framework of circular bioeconomy. This review synthesizes recent advances in the utilization of cyanobacteria and green microalgae as biofertilizers, biostimulants, and biopesticides, emphasizing their physiological mechanisms and agronomic potential. Microalgae and cyanobacteria can fix atmospheric nitrogen, solubilize phosphorus, and supply essential micronutrients through exopolysaccharides, organic acids, and siderophores, thereby improving soil fertility and structure. Their metabolites, including phytohormones, amino acids, and antioxidants, stimulate seed germination, root growth, nutrient uptake, and tolerance to abiotic stresses such as drought and salinity. Moreover, allelochemicals and antimicrobial compounds from microalgae can suppress plant pathogens and reduce pesticide dependence. Integrating microalgae cultivation with wastewater and flue gas utilization promotes nutrient recycling and CO 2 sequestration, further enhancing environmental sustainability. However, large-scale application remains limited by biomass production costs, inconsistent performance under field conditions, and regulatory uncertainty. Overall, microalgae-based fertilizers and biostimulants hold great promise for sustainable crop production and soil health improvement. Future research should focus on low-cost cultivation and harvesting technologies, field scale validation, and standardized product formulations to accelerate the transition toward climate smart and resource sustainable agriculture.\n\nSynthetic evaluation record: commercial lot number=LOT-00019. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12833470", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e64659a28b5bbc531121", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nL-( +)-Tartaric acid is a valuable organic acid with broad applications in the food, pharmaceutical, and chemical industries. Its eco-friendly synthesis typically relies on the enzymatic hydrolysis of cis -epoxysuccinate (CES) catalyzed by cis -epoxysuccinate hydrolases (CESHs), but conventional single-batch processes suffer from low space–time yields and poor continuity. To address these challenges, we devised two complementary fed-batch strategies to simplify the enzyme–product separation by exploiting differences in their solubilities. Strategy A employs carrier-free cross-linking immobilization of whole cells using 0.02% glutaraldehyde and 0.1% polyethylenimine. In this system, both the substrate sodium cis -epoxysuccinate (CESNa) and the product sodium L-( +)-tartrate remain soluble, while the enzyme is retained in the insoluble cell matrix. Under fed-batch operation, this configuration achieves a space–time yield of 150 g L −1 h −1 . Strategy B uses cell-free extract of CESH to hydrolyze calcium cis -epoxysuccinate (CESCa) with inherently low solubility. Here, the enzyme is fully soluble but the L-( +)-tartrate formed precipitates as an insoluble calcium salt, allowing easy separation of the product from the reaction mixture. This approach overcomes potential substrate inhibition and minimizes sodium-ion discharge, delivering a space–time yield of 136 g L −1 h −1 and a specific productivity of 484 g product /g catalyst . Both the soluble-product/insoluble-enzyme system (A) and the insoluble-product/soluble-enzyme system (B) represent effective strategies to streamline downstream processing and markedly enhance productivity. Together, they offer a viable route to scalable and cost-effective industrial production of L-( +)-tartaric acid.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00020.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12834850", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-076eb2414915faddbbbb", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nD-Pantothenic acid (DPA), also known as vitamin B 5 , is a water-soluble organic acid, widely applied in foods, feeds, cosmetics, and medicines. Although numerous and rapidly developing cell factories have been established for DPA biosynthesis, there has been no report of any attempts to engineer Yarrowia lipolytica to synthesize DPA. To explore further possibilities in DPA biosynthesis, we tried to employ systematic metabolic engineering strategies to identify and break the potential bottlenecks in DPA biosynthesis by Y. lipolytica . By improving the rate-limiting steps of the DPA biosynthesis pathway, weakening the strongly competitive pathways, and enhancing the multiple cofactor supplies, a robust Y. lipolytica cell factory for DPA biosynthesis was successfully constructed. Consequently, the resulting strain DPA34 produced 2.18 g/L DPA in a 5-L bioreactor, representing the first report of DPA production to date in Y. lipolytica. This work is believed to facilitate the development of Y. lipolytica for sustainable manufacturing of vitamin B 5 and its derivatives. The online version contains supplementary material available at 10.1186/s40643-026-01009-4.\n\nSynthetic evaluation record: lot number LOT-00021. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12834876", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7bd62474904d5a0cc680", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nHeparin, mainly used as an anticoagulant, has also shown potential in the treatment of diseases such as inflammation and cancer. Currently, heparin is mainly extracted from the intestinal mucosa of pigs. However, due to concerns about disease transmission and contamination associated with animal-derived products, biomanufacturing techniques have been explored as alternative production methods. Through enzyme engineering, metabolic engineering, and synthetic biology approaches, the heparin biosynthetic pathways have been systematically optimized. The main biomanufacturing techniques include in vivo/in vitro combination strategy (microbial heparosan fermentation followed by chemoenzymatic modification) and de novo biosynthesis. This article comprehensively discusses the latest advancements, challenges, and future perspectives of these heparin biomanufacturing techniques. Keywords: Bioengineered heparin, Heparosan fermentation, Chemoenzymatic modification, De novo biosynthesis, Synthetic biology\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00022, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12834895", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2e5182f105c4f9abede7", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nUmbilical cord blood (UCB) is an attractive source of natural killer (NK) cells for the development of allogeneic ‘off-the-shelf’ cancer immunotherapies. This is due to the relatively high proportion of highly proliferative NK cells compared to adult peripheral blood (APB), a low risk of graft-versus-host disease and ease of procurement. However, due to the limited starting volume of UCB and naïve phenotype of isolated cells, ex vivo NK cell expansion and activation is essential to generate clinically relevant doses of cells with potent anti-tumor activity. Furthermore, intrinsic variability in both in vitro and clinical performance of NK cells from different UCB units (CBUs) has been reported. To better characterize this variability, we measured UCB NK cell ex vivo fold expansion, phenotype and cytotoxic potential using a basic expansion system. We then used these results to identify characteristics related to superior performance, enabling the optimization, selection and processing of CBUs for the manufacture of NK cells as therapies at a larger scale. Our results revealed that despite wide inter-donor variability in performance between CBUs, a priori selection could be used to identify units likely to show high expansion and/or cytotoxicity. We observed that decreased time between UCB collection and CD3 - UCB mononuclear cell (CBMC) isolation was associated with significantly higher NK fold expansion (n=13; p<0.05). Furthermore, a cryopreservation step following early isolation and prior to expansion, significantly increased the expansion potential of the isolated NK cells (p<0.05), thus providing an opportunity for pre-selection and parallel culture of multiple optimal units. Finally, the NK cells from CBUs collected from caesarean sections had statistically significantly increased proliferative potential compared to those from vaginal deliveries (n=13; p<0.05). In conclusion, early isolation and cryopreservation of CD3 - CBMCs from caesarean section CBUs offer an optimal starting material for use in UCB-derived NK cell immunotherapies, providing superior ex vivo performance and enabling batch testing to selectively expand cells from CBUs with the greatest potential.\n\nSynthetic evaluation record: commercial lot number=LOT-00023. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12835214", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e36f04f623427d8ba80b", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nCodon optimization is widely used to improve heterologous gene expression in Escherichia coli . However, many existing methods focus primarily on maximizing the codon adaptation index (CAI) and neglect broader aspects of biological context. In this study, we present ColiFormer, a transformer-based codon optimization framework fine-tuned on 3676 high-expression E. coli genes curated from the NCBI database. Built on the CodonTransformer BigBird architecture, ColiFormer employs self-attention mechanisms and a mathematical optimization method (the augmented Lagrangian approach) to balance multiple biological objectives simultaneously, including CAI, GC content, tRNA adaptation index (tAI), RNA stability, and minimization of negative cis-regulatory elements. Based on in silico evaluations on 37,053 native E. coli genes and 80 recombinant protein targets commonly used in industrial studies, ColiFormer demonstrated significant improvements in CAI and tAI values, maintained GC content within biologically optimal ranges, and reduced inhibitory cis-regulatory motifs compared with established codon optimization approaches, while maintaining competitive runtime performance. These results represent computational predictions derived from standard in silico metrics; future experimental work is anticipated to validate these computational predictions in vivo. ColiFormer has been released as an open-source tool alongside the benchmark datasets used in this study.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00024.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12838208", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-b58848535a857b2864fa", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nSyringic acid (SA) is a natural derivative of syringaldehyde (SD), derived from lignin depolymerization. Its application in the food industry focuses on the properties of natural functional ingredients; it is mainly used as a food antioxidant and food preservative, but can also be used as an ingredient to enhance food flavor and functional foods. This compound exhibits a remarkable spectrum of biological activities, including potent antioxidant, anti-inflammatory, neuroprotective, hypoglycemic, detoxifying, and anti-cancer effects, positioning it as a highly promising candidate for pharmaceutical and nutraceutical applications. In this study, suitable sites were first screened through homologous sequence alignment, and a variant of aryl-alcohol oxidase (CgAAO) with high efficiency in catalyzing the conversion of SD to SA was obtained via site-directed mutagenesis. A deep eutectic solvent (DES) system based on choline chloride/urea (ChCl/UR) in water was developed to enhance SA production. Additionally, key parameters of the biological reaction were optimized, including temperature, pH, metal ions, as well as the type and dosage of DES. The optimal performance was achieved using recombinant E. coli pRSFDuet-CgAAO-Y335F whole-cell biocatalysts, yielding 75% and producing 0.75 g/L SA in 100 mM KPB buffer (pH 7.0) containing 5 wt% ChCl/UR and 1 mM Fe 3+ . This study established a novel biosynthetic pathway for SA that was efficient, mild, green, and environmentally friendly.\n\nSynthetic evaluation record: lot number LOT-00025. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12839953", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-8ac866491578c573969b", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nEnzyme technology, characterized by high efficiency, environmental compatibility, and precise controllability, has become a pivotal biocatalytic approach for quality enhancement and nutritional improvement in modern food industries. This review summarizes recent advances and underlying mechanisms of enzyme applications in food processing optimization, nutritional enhancement, and functional food development. In terms of process optimization, enzymes such as transglutaminase, laccase, and peroxidase enhance protein crosslinking, thereby markedly improving the texture and stability of dairy products, meat products, and plant-based protein systems. Proteases and lipases play essential roles in flavor development, maturation, and modulation of sensory attributes. From a nutritional perspective, enzymatic hydrolysis significantly improves the bioavailability of proteins, minerals, and dietary fibers, while simultaneously degrading antinutritional factors and harmful compounds, including phytic acid, tannins, food allergens, and acrylamide, thus contributing to improved food safety and nutritional balance. With respect to functional innovation, enzyme-directed production of bioactive peptides has demonstrated notable antihypertensive, antioxidant, and immunomodulatory activities. In addition, enzymatic synthesis of functional oligosaccharides and rare sugars, glycosylation-based modification of polyphenols, and enzyme-assisted extraction of plant bioactive compounds provide novel strategies and technological support for the development of functional foods. Owing to their high specificity and eco-friendly nature, enzyme technologies are driving food and nutrition sciences toward more precise, personalized, and sustainable development pathways. Despite these advances, critical research gaps remain, particularly in the limited mechanistic understanding of enzyme behavior in complex food matrices, the insufficient integration of multi-omics data with enzymatic process design, and the challenges associated with translating laboratory-scale enzymatic strategies into robust, data-driven, and scalable industrial applications.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00026, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12841018", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-bbba75c45cf7e733c2b8", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe metal-binding periplasmic protein CusF has been proposed as a bifunctional tag that enhances the solubility of recombinant proteins and enables purification using Cu affinity chromatography. However, evidence for its performance remains limited to a few model proteins. Here, we evaluated CusF as a solubility tag for two heterologous proteins: a putative poly(A)-polymerase from Enterococcus faecalis (Efa PAP) and the red fluorescent protein mCherry. The proteins were fused to CusF, expressed in E. coli BL21 (DE3) pLysS and Rosetta 2 (DE3) strains, and assessed for solubility and IMAC binding. Native Efa PAP was completely insoluble under all tested conditions, and fusion to CusF did not improve its solubility. Similarly, CusF–mCherry accumulated predominantly in the insoluble fraction, with only trace amounts detectable in soluble lysates. Soluble CusF–mCherry did not bind Cu 2+ -charged IMAC resin, while moderate binding to Ni 2+ -charged resin was attributable to the vector-encoded His tag rather than CusF. These results indicate that CusF does not universally enhance protein solubility and may not consistently bind Cu-based IMAC resin. Our findings expand empirical knowledge of solubility tag performance and emphasize the necessity of testing multiple tags to identify optimal strategies for recombinant protein production.\n\nSynthetic evaluation record: commercial lot number=LOT-00027. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12842202", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e80528dbb0cdb931765c", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nPhycobiliproteins are recognized as potential bioactive compounds and described as highly valued natural products for industrial and biotechnological applications. Moreover, they have been observed to possess antioxidant, anticancer/antineoplastic, and anti-inflammatory activities. Therefore, the search for new methods of their extraction and isolation is still ongoing. Foam fractionation, a bubble separation technique that allows amphiphilic molecules to be separated from their aqueous solutions, is a promising but understudied method. The process may be carried out both under mild conditions that are suitable for proteins and also for diluted solutions. This paper presents the results of applying the foam fractionation process to concentrate and separate phycobiliproteins. Allo- and C-phycocyanin from a thermophilic Synechococcus PCC 6715 strain were used in extract form after biomass cultivation and disintegration. Two ways of running the process were investigated: batch mode and continuous mode, the latter of which has not been reported in the literature previously. The results indicate that the method can be applied on a larger scale, as the outcomes of the continuous mode processes were comparable to those of the batch mode. Moreover, the results indicate that the process provides, to a certain extent, the opportunity of separating phycobiliproteins from each other.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00028.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12842750", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-9191fbbeca1e4e8771fe", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nXylanases (EC 3.2.1.8) are value-added enzymes essential for biomass deconstruction and are widely used in the pulp and paper, food, feed, and biofuel sectors. This review provides a comprehensive analysis of the current state and future prospects of xylanase research and application. It begins by examining the structural diversity of xylan substrates and the corresponding classification of xylanase enzymes, their catalytic mechanisms, and methods for their functional study, such as inhibitor analysis. The discussion then covers the challenges and methods involved in the purification of xylanases from complex biological mixtures. While natural microbial sources (fungi and bacteria) remain important, the limitations of wild-type (WT) strains for industrial production are highlighted. The review assesses the most common recombinant production systems, including Escherichia coli , Bacillus subtilis , and Komagataella phaffii , comparing their advantages for high-yield enzyme production. Finally, the paper focuses on protein engineering strategies as powerful tools for enhancing key enzyme properties (thermostability, specific activity, and pH tolerance). By integrating fundamental knowledge with applied technological approaches, this review underscores the critical role of xylanases in industrial biotechnology and identifies future research directions for their optimization.\n\nSynthetic evaluation record: lot number LOT-00029. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12843773", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-f13001bd59648f5b6e5b", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nAstaxanthin, derived from Haematococcus pluvialis , is a potent antioxidant with significant therapeutic potential. However, its large-scale commercialization is hindered by the “thick-wall challenge”, a phenomenon where the stress conditions required for astaxanthin accumulation also trigger the formation of resistant secondary cell walls. This challenge complicates extraction and reduces bioaccessibility, thereby increasing production costs. Recent advancements have focused on uncoupling astaxanthin biosynthesis from cell wall reinforcement, utilizing metabolic engineering and strain selection to reduce wall formation while maintaining high yields. Furthermore, green extraction techniques, such as electrotechnologies and ionic liquids, are being explored to improve efficiency and environmental sustainability. This review synthesizes these innovations, including biorefinery systems that maximize biomass valorization, and discusses emerging clinical applications. We highlight the challenges in bridging the gap between laboratory successes and clinical translation, and suggest future directions for resolving the thick-wall challenge, advancing astaxanthin production, and expanding its therapeutic uses in nutraceuticals and pharmaceuticals.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00030, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12843829", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-b42ff804a917fbcb21b3", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe growing concern over plastic pollution and the widespread presence of micro- and nanoplastics has renewed interest in polyhydroxybutyrate (PHB) as a biodegradable alternative; however, its industrial deployment remains constrained by costly recovery operations with a high environmental burden. This study examines how PHB biosynthesis and intracellular organization, physicochemical properties, and the characteristics of the producing microorganism influence the performance of conventional recovery routes, including extraction with organic solvents, alkaline/oxidative chemical digestion, and enzymatic–physical schemes coupled with mechanical disruption. Based on this foundation, quantitative data are analyzed for PHB content in bacteria, mixed microbial cultures, cyanobacteria, and microalgae, along with extraction yields, polymer purity, and solvent recyclability in processes employing chlorine-free solvents, green solvents, and hydrophobic natural deep eutectic solvents (NaDESs) formulated with terpenes and organic acids. The analysis integrates mechanistic perspectives on NaDES–cell and NaDES–PHB interactions with solvent design criteria, biorefinery configurations, and preliminary evidence from technoeconomic and life cycle assessments. The findings identify NaDES as an up-and-coming platform capable of reconciling biopolymer quality with the principles of green chemistry while delineating critical gaps in recovery efficiency, viscosity management, solvent recycling, and pilot-scale validation.\n\nSynthetic evaluation record: commercial lot number=LOT-00031. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12845502", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-f46acb8529098e4c5e04", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nToday, PHB and its copolymers—potential plastic substitutes—are produced by fermenting sugar, which is not scalable to the volumes of plastic consumption. PHB from CH 4 can offer a sustainable process route, with CH 4 potentially produced from a variety of waste biomass streams through anaerobic digestion, gasification, and methanation. The high molar mass (M w ) of PHB is a key determinant of its mechanical properties, and strain, culture conditions and downstream processing influence it. In this work, the strain Methylocystis sp. GB 25 (DSMZ 7674) was grown on natural gas as the sole carbon and energy source and air (1:1) in a loop reactor with 350 L active fermentation volume, at 35 °C and ambient pressure. After two days of continuous growth, the bacteria were limited in P and N for 1, 2, and 2.5 days to determine the optimal conditions for PHB accumulation and the highest Mw as the target. The biomass was then centrifuged and spray-dried. For downstream processing, chloroform solvent extraction and selected enzymatic treatment were deployed, yielding ~40% PHB from the biomass. The PHB obtained by solvent extraction exhibited high average weight molar masses of M w ~1.1–1.5 × 10 6 g mol −1 . The highest M w was obtained after one day of limitation, whereas enzyme treatment resulted in partially degraded PHB. Cold chloroform maceration, interesting due to energy savings, did not achieve sufficient extraction efficiency because it was unable to extract high-molar-mass PHB fractions. The extracted PHB has a high molar mass, more than double that of standard commercial PHB, and was characterized by DSC, which showed a high degree of crystallinity of up to 70% with a melting temperature of close to 180 °C. Mechanical tensile properties measurements, as well as dynamic mechanical thermal analysis (DMTA), were performed. Degradation of the PHB by enzymes was also determined. Methanotrophic PHB is a promising bioplastics material. The high M w can limit and delay polymer degradation in practical processing steps, making the material more versatile and robust.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00032.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12846098", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-f6ff0f2113f19eeeda64", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nScalable moss bioreactors enable the production of high-quality recombinant prolyl-hydroxylated human collagen without heterologous P4H expression, offering a sustainable and vegan alternative to conventional collagens derived from animals. Collagens are structural proteins of the extracellular matrix essential for skin elasticity and integrity. They are widely used in dietary supplements and cosmetics. Conventional collagens of animal origin raise concerns regarding ethics, safety, and sustainability. As a vegan alternative, we report on the production of a 30 kDa prolyl-hydroxylated human collagen polypeptide from Physcomitrella moss plants. For secretion-based production and formulation compatibility, a hydrophilic region encompassing 334 amino acids from human type III collagen was selected, which includes four protein domains involved in cell adhesion, collagen binding, integrin recognition and wound healing. Transgenic moss lines were generated via protoplast transformation. Immunodetection identified collagen-producing lines, and mass spectrometry validated the product and detected prolyl-hydroxylation on 23 sites. The presence of this important post-translational modification underscores the high biomimetic quality of the product. To enable industrial-scale production, the transformants were quantitatively analysed at the genomic, transcript, and protein levels. The most productive lines were forwarded to process development, where culture conditions, including CO 2 supplementation, pH, and light intensity, were optimized. Upscaling to 5 L photobioreactors established a robust, light- and biomass-dependent production regime that yielded nearly 1 mg/L of secreted collagen polypeptide in the culture supernatant after 11 days of cultivation. Taken together, this study presents the first scalable moss-based production of a post-translationally modified human collagen and offers a sustainable and vegan alternative to conventional collagens for cosmetic formulations. This highlights the versatility of Physcomitrella as a production host for high-quality proteins with industrial applicability that also meet consumer requirements. The online version contains supplementary material available at 10.1007/s00299-026-03727-7.\n\nSynthetic evaluation record: lot number LOT-00033. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12852254", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-b1d9a23e6071faa6cf02", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nCarotenoids and apocarotenoids constitute a structurally and functionally sundry class of isoprenoids whose significance extends from photosynthetic light capture and photoprotection to phytohormone signaling, flavor and aroma formation, and emerging biomedical applications. While recent appraisals have emphasized quantitative advances in microbial production, this mini-review adopts a pathway module-centric perspective. We examine each biosynthetic stage from precursor supply, condensation to geranylgeranyl diphosphate (GGPP), phytoene synthesis, desaturation/isomerization, cyclization, hydroxylation, ketolation, epoxidation, and oxidative cleavage, highlighting novel enzymatic variants, mutagenesis studies, fusion strategies, and compartmentalization approaches that impart metabolic control. Special emphasis is placed on recently discovered and engineered enzymes, as well as synthetic biology tools. This review integrates diverse enzyme sources, host ranges across plants, fungi, algae, yeasts, and bacteria, as well as pathway modularity, to provide an updated review of recent literature. We conclude by outlining future directions that highlight gaps and potential areas for future work. This focused synthesis aims to equip researchers with a hierarchical understanding of the pathways and strategies to advance carotenoid and apocarotenoid biosynthesis.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00034, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12852478", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-40ef2f35d89fc026ea54", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\n\n\nSynthetic evaluation record: commercial lot number=LOT-00035. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12854881", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-42ba1289b1429a1d5147", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nDifferent yeast species, including Ogataea polymorpha , are often used as hosts for recombinant protein production. One of the most important factors limiting such applications is yeast-specific modifications of glycoside chains attached to secretory proteins. This problem can potentially be solved by the identification and inactivation of genes responsible for these modifications. Previously we demonstrated that the exceptional resistance of O. polymorpha to vanadate depends on the ABV1 gene responsible for the mannosylphosphorylation of protein glycoside chain in the Golgi apparatus. Here we show that mutations altering protein glycosylation in the secretory pathway can be selected in the abv1Δ mutant by screening for vanadate resistance. For one such mutant, we identified the responsible gene, which encodes a putative α-1,2-mannosyltransferase. To ensure the absence of phosphomannosylation, both O. polymorpha genes, ABV1 and MNN4 , which encode mannosylphosphate transferase homologs, were inactivated. Some vanadate resistant mutants generated in this strain showed defects in N -glycosylation of a recombinant glycoprotein. This demonstrates that the effects of N -glycosylation on vanadate resistance in O. polymorpha are not mediated by phosphomannosylation per se and that identification of certain genes responsible for N -glycosylation in this yeast can be performed via selection of vanadate resistant clones.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00036.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12855064", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7731be582c29bb847262", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nCAR-T cell therapy is leading the way in the field of cancer cell immunotherapies due to its high success rates. However, the manufacturing of CAR-T cells remains complex and expensive. T-cell enrichment from patient apheresis starting material is a key step in the manufacture but cellular impurities interfere with the ex vivo transduction of T-cells and their proliferation. Current enrichment methods including magnetic bead selection suffer from various limitations. We report here a bead-less T-cell enrichment process through a two-stage procedure based on inertial microfluidics. Using apheresis like starting material samples from healthy donors, the dual-stage process showed an efficient 87% (SD ± 6%) enrichment and 80% (SD ± 30%) recovery of T-cells. Validation of the process with ovarian cancer samples resulted in a T-cell purity 70% (SD ± 10%) from a starting purity of 48% (SD ± 6%) at a 64% (SD ± 4%) T-cell recovery. The two-stage inertial microfluidic process was also shown to have no detectable effect on the proliferation of the cells.\n\nSynthetic evaluation record: lot number LOT-00037. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12855359", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-3ef0a3abe87250eb0f9a", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nRecombinant protein expression can be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications. We introduce RP3Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli . RP3Net utilizes the most recent protein and genomic foundational models. A curated dataset of internal experimental results from AstraZeneca and publicly available data from the Structural Genomics Consortium was used for training, validation and testing of RP3Net. RP3Net achieves an increase in area under the receiver operator curve (AUROC) of 0.15, compared to a baseline model. When experimentally validated on an independent, prospective, manually selected set of 97 constructs, RP3Net outperformed currently available models, with an AUROC of 0.83, delivering accurate predictions in 77% of the cases, and correctly identifying successfully expressing constructs in 92% of cases. The model, along with installation and running instructions, is available under an MIT licence at https://github.com/RP3Net/RP3Net , DOI 10.5281/zenodo.17243498.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00038, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12857573", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e2c73c9099e98cc8c393", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nSaccharomyces cerevisiae is an established production host for therapeutic proteins; many of those are small proteins such as insulin or glucagon-like peptide-1 (GLP-1) analogs. Contrastingly, proteins of higher molecular weight, foremost antibodies, did not reach the market due, among other factors, to limiting productivity. Here we addressed the loss of product to protein degradation through a combination of genetic engineering of the host and medium optimization. We screened target genes that either directly or indirectly can lead to proteolytic degradation. We identified four deletions that are beneficial for expression: PEP1 and VPS30 , which both can channel proteins to the vacuole for degradation; MON2 , which can lead to the re-uptake of secreted proteins; and ALG3 , which can affect the permeability of the cell wall. In parallel, we developed a small-scale fed-batch cultivation system for 24-well deep well plate cultivations and using an amino acid-rich medium. To stabilize secreted proteins, we screened chemical chaperones and osmolytes. We fortified the medium with arginine, 4-phenylbutyrate (4-PBA), and Tween-20. Using the engineered yeast strain, which features VPS30 , PEP1 , and ALG3 deletions, and the small-scale fed-batch system, we obtained 2.5 µg/mL of a secreted chimeric fusion of a nanobody to the crystallizable fragment (Fc) of a human immunoglobulin. Instrumental to the increase in the final titer were the reduced losses. This was achieved by a combination of complementary measures: improving diffusion through the cell wall, achieved through genetic engineering, and reducing losses to proteolytic degradation through medium optimization and genetic engineering. Moreover, we showed that the engineered strain and cultivation set-up are suitable for the production of different antibodies. • Chemical chaperones and amino acid-rich medium increased secreted protein titers. • Medium and host engineering are instrumental for improving productivity. • Small-scale cultivation system enables production levels suitable for characterization. The online version contains supplementary material available at 10.1007/s00253-025-13700-1. Keywords: Saccharomyces cerevisiae , Chemical chaperones, Small-scale production system, Medium optimization, Recombinant antibody production, Chimeric nanobody-Fc fusion protein\n\nSynthetic evaluation record: commercial lot number=LOT-00039. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12858493", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-1961ce86d593f54db152", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nGas fermentation offers a sustainable alternative for valorizing climate-active gases and industrial off-gases. Currently, these gases require energy-intensive purification steps before they can be used in chemical processes such as Fischer–Tropsch synthesis. In gas fermentation, anaerobic bacteria produce acetate from industrial off-gases. Compared to chemical processes, the anaerobic bacteria offer greater tolerance to varying gas concentrations and impurities. One major product of these anaerobic valorization processes is acetate, which can be used as a co-substrate in a variety of biological processes. This study evaluates Corynebacterium glutamicum and Ustilago maydis in benchtop cultivations using 10–20% (v/v) sterile-filtered acetate-rich supernatants from Acetobacterium woodii fermentation to produce L-lysine and triglycerides. Partial substitution of glucose with these supernatants supported robust growth and required no additional purification beyond sterile filtration. C. glutamicum achieved a L-lysine concentration of 3.5 ± 0.27 g∙L −1 and exhibited a diauxic growth pattern on glucose and acetate. In U. maydis , supernatant addition shortened the lag phase by approximately 2 h but reduced triglyceride yields modestly due to higher nitrogen availability. Optimizing the nitrogen-to-carbon ratio in benchtop fermentations resulted in a triglyceride concentration of 12.75 ± 1.17 g∙L −1 , demonstrating the feasibility of this approach. Collectively, the results demonstrate a viable method for replacing a portion of refined glucose with acetate-rich supernatants, thereby enabling a cost-efficient integration of anaerobic gas valorization with aerobic biomanufacturing. The online version contains supplementary material available at 10.1186/s13068-025-02732-4.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00040.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12859875", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c8ed16990dc41076e6e0", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nKomagataella pastoris is extensively used as a microbial cell factory for the production of recombinant proteins and high‐value compounds. However, tightly controlled promoter systems responsive to safe and economical inducers are required for precise metabolic and pathway engineering in this yeast species. Cumate‐inducible promoters are an ideal choice due to the safety and low cost of cumate. In this study, we systematically optimised the insertion sites of the CuO operator sequence within the strong promoter P GCW14 to isolate a high‐activity variant that we designated as P GCWCuO03 . To fine‐tune the expression of the repressor protein CymR, we developed a truncated promoter of P GAP , designated as P GAP200 . Based on the optimal promoter P GCWCuO03 and the CymR expression unit, we constructed a robust CymR/CuO‐mediated cumate‐inducible promoter, designated as P gc , in K. pastoris . P gc demonstrated outstanding induction properties, resulting in an approximately 11‐fold increase in target protein production following induction. Promoter substitution assays validated the effectiveness of P gc in temporal gene expression control, highlighting the significant potential of this promoter for both basic research and industrial bioprocessing applications in synthetic biology and biotechnology in K. pastoris .\n\nSynthetic evaluation record: lot number LOT-00041. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12868391", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2f0280446eb56e4c963b", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nPrecise quantification of recombinant proteins is essential for assessing and comparing expression efficiency and optimizing production processes. Fluorescent proteins have emerged as powerful tools for real-time monitoring of gene expression and protein tracking. However, standardized and validated methods for their quantification, particularly for the widely used green fluorescent protein, remain limited. To date, no universally adopted protocol has emerged. This study presents a high-throughput method for the quantification of recombinantly produced Emerald Green Fluorescent Protein (EmGFP) based on direct fluorescence measurements of the cell suspension while quantifying and integrating potential effects of signal attenuation. The workflow uses solely standard laboratory equipment, ensuring broad accessibility and easy implementation. Moreover, in-house EmGFP standard preparation and quantification is described. The method was validated according to FDA guidelines “Analytical Procedures and Methods Validation for Drugs and Biologics,” addressing the requirements of linearity, limit of detection (LOD), limit of quantification (LOQ), precision, accuracy, and recovery rate. Investigation was conducted using Escherichia coli BL21 cells expressing EmGFP, widely available sodium fluorescein as a chemical standard, commercial GFP, and an in-house EmGFP standard. A robust correlation (linear fitting, R 2 0.96) of the EmGFP concentration and relative fluorescence units (RFU) was established, enabling efficient and high-throughput fluorescence quantification using a standardized workflow in a microtiter-based format suitable for the application in comparative studies across different expression constructs, conditions, and scales. By enabling absolute quantification of fluorescent proteins, this method supports both real-time bioprocess optimization and broader applications in protein production research. The online version contains supplementary material available at 10.1007/s00253-026-13734-z.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00042, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12876113", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-577c05a0d4472bf8d244", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMicrobial fermentation is an established technology that is becoming increasingly used to produce key food components. Among the various microorganisms used, yeasts play crucial roles due to their efficiency in synthesizing a wide range of industrially important compounds. The growing demand for sustainable, locally sourced, and animal-free food ingredients has increased the focus on yeast biomass and its derivatives. These yeast-based products, such as food emulsifiers, are a promising next-generation of food components, offering advantages like a low risk of allergenicity. Yeast biomass-based fractions have been effectively used as emulsifiers in various food products including in dairy, meat, bakery, meat alternatives, mayonnaises and salad dressing, with effective properties demonstrated in a range of oil-in-water, water-in-oil, and Pickering emulsion models. Both whole cell biomass and yeast cell fractions such as the yeast cell wall, mannoproteins, glucans, exopolysaccharides and other yeast-derived compounds have been demonstrated to function as effective emulsifiers. An increasingly large number of yeasts, beyond just Saccharomyces cerevisiae , have been studied as potential sources of these emulsifiers with the extraction and purification methods employed depending on the specific emulsifier targeted, the required purity, and the intended application. Efficient, cost-effective, and sustainable processes are key to enabling industrial-scale production of these emulsifiers, as such this article reviews the potential yeast-derived food emulsifiers, lists the various yeast species investigated to date, examines the extraction and purification methods, and highlights the potential food applications of these yeast-derived emulsifiers.\n\nSynthetic evaluation record: commercial lot number=LOT-00043. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12876142", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-f8298c5e28c39e4b4027", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nBillions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics. We introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications. The framework is validated on 37,191 ECG images with 1596 collected at Akershus University Hospital, where the algorithm obtains a mean signal-to-noise ratio of 19.65 dB on scanned papers with common artifacts. It is further evaluated on the Emory Paper Digitization ECG Dataset, comprising 35,595 images, including images with perspective distortion, wrinkles, and stains. The model improves on the state-of-the-art in all subcategories. The full software is released as open-source, promoting reproducibility and further development. We hope the software will contribute to unlocking retrospective ECG archives and democratize access to AI-driven diagnostics.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00044.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12891466", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-fcf4a8bef735114b6501", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\n\n\nSynthetic evaluation record: lot number LOT-00045. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12896639", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-06d40cbe4b5f309811a7", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nBovine mastitis is a major disease affecting dairy cow health and milk production, and current treatment options still have important limitations. Previous studies have shown that a protein called cyclophilin A is closely associated with inflammatory responses during mastitis, but further research requires a stable and reliable source of this protein. In this study, we established a method for the stable expression of bovine cyclophilin A in mammalian cells and demonstrated that these cells can continuously express the target protein. This work provides a solid technical foundation for future studies on the role of this protein in bovine mastitis and for the development of related diagnostic or intervention approaches, and it may contribute to improving the prevention and control of bovine mastitis.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00046, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12896659", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-cb11cae012e851c4d9fd", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMicrobial biosurfactants have emerged as natural and sustainable alternatives to synthetic surfactants used in the food industry, due to the growing demand for biodegradable and safe ingredients. Produced by bacteria, fungi, and yeasts, these compounds exhibit important physicochemical properties, such as emulsifying capacity, surface tension reduction, foam stabilization, and favorable interaction with different food matrices. In addition to their technological function, they exhibit relevant biological activities, including antioxidant and antimicrobial action, which contribute to the control of lipid oxidation and microbiological deterioration. These characteristics make biosurfactants attractive for applications in emulsions, fermented beverages, aerated products, probiotic systems, and bioactive packaging. The objective of this work is to provide a narrative literature review that integrates recent advances in the production, functionality, safety, sustainability, and application perspectives of biosurfactants in the food sector. In the field of production, biotechnological advances have made it possible to overcome historical limitations such as high cost and low yield. Strategies such as the use of agro-industrial waste, metabolic engineering, microbial co-cultures, continuous fermentations, and in situ removal techniques have increased efficiency and reduced environmental impacts. Despite the advances, significant challenges remain. Future prospects and advances tend to facilitate industrial adoption and consolidate biosurfactants as strategic ingredients for the development of more sustainable, functional, and technologically advanced foods.\n\nSynthetic evaluation record: commercial lot number=LOT-00047. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12896828", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-884bdad112dbaad09d7e", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nCentella asiatica has emerged as a strategic biomass for the sustainable production of high-value biochemicals at the interface of traditional medicine and modern biotechnology. This review consolidates the current knowledge on its phytochemical diversity, emphasizing triterpenoid saponins—asiaticoside, madecassoside, asiatic acid, and madecassic acid—as core bioactive molecules relevant to pharmaceutical, dermatological, nutraceutical, and functional-ingredient applications. Advances in green extraction technologies, including ultrasound-assisted, microwave-assisted, ohmic-heating, and supercritical CO 2 systems, have demonstrated superior efficiency in recovering high-purity biochemicals while significantly reducing solvent use, energy demand, and environmental impact compared with conventional methods. Complementary analytical and standardization platforms, such as HPLC, UPLC, and GC–MS, enable rigorous quality control across the entire value chain, supporting the development of reproducible and regulatory-compliant biochemical extracts. From a biomass valorization and biorefinery perspective, C. asiatica offers multiple metabolite streams that align with circular economy and field-to-market sustainability principles. Key challenges remain, including agronomic variability, scaling up green extraction, and supply chain resilience. However, emerging solutions, such as Good Agricultural and Collection Practices (GACP) guided cultivation, plant tissue culture, metabolic engineering, and integrated biorefinery frameworks, show strong potential for establishing a reliable and environmentally responsible production system. Collectively, C. asiatica represents a model species for sustainable biochemical production, combining scientific efficacy with industrial, economic, and ecological relevance.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00048.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12899466", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-d61267fea0a32c6ce3b5", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMonoacylglycerols (MAGs) are significant intermediate byproducts in the hydrolysis of oils and fats. The accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes. Therefore, the development of efficient biocatalysts for the specific MAG conversion is of great industrial importance. The lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application. In this study, a combinatorial protein engineering strategy was employed to overcome these limitations. We integrated fusion protein technology with rational design to enhance both the functional expression and catalytic efficiency of AOL. Firstly, the superfolder green fluorescent protein (sfGFP) was fused to the N-terminus of AOL. The results indicated that the sfGFP fusion tag significantly improved the solubility and stability of the enzyme, preventing the formation of inclusion bodies. The fusion protein sfGFP-AOL exhibited a MAG conversion rate of approximately 65%, confirming the positive impact of the fusion tag on enzyme developability. To further boost catalytic performance, site-directed mutagenesis was performed based on structural analysis. Among the variants, the mutant sfGFP-Y92Q emerged as the most potent candidate. In the MAG conversion, sfGFP-Y92Q achieved a conversion rate of 98%, which was not only significantly higher than that of sfGFP-AOL but also outperformed the widely used commercial immobilized lipase, Novozym 435 (~54%). Structural modeling and docking analysis revealed that the Y92Q mutation optimized the geometry of the active site. The substitution of Tyrosine with Glutamine at position 92 likely enlarged the substrate-binding pocket and altered the local electrostatic environment, thereby relieving steric hindrance and facilitating the access of the bulky MAG substrate to the catalytic center. In conclusion, this work demonstrates that the synergistic application of sfGFP fusion and rational point mutation (Y92Q) can dramatically transform the catalytic properties of AOL. The engineered sfGFP-Y92Q variant serves as a robust and highly efficient biocatalyst for MAG degradation. Its superior performance compared to commercial standards suggests immense potential for cost-effective applications in the bio-manufacturing of high-purity fatty acids and biodiesel, offering a greener alternative to traditional chemical processes.\n\nSynthetic evaluation record: lot number LOT-00049. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12899640", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0703364f5a20cca83a1a", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nDetergent-compatible lipases are increasingly valued for their ability to remove stains under low-temperature and environmentally friendly washing conditions. Their industrial applicability depends on achieving high enzyme production, cost-effective purification, and stability within detergent formulations. Here, we report the purification and characterization of a highly active extracellular lipase from Streptomyces sp. AU-153 (1543 U/mL, p -NPP assay). A simplified aqueous two-phase system (ATPS) of poly­(ethylene glycol) and sodium chloride achieved 8-fold purification with a recovery of 272.7%. The purified enzyme exhibited optimal activity at pH 8.0 and 40 °C, maintained stability across pH 7–11, and retained substantial activity up to 60 °C. Activity was enhanced by Ca 2+ , Mg 2+ , and β-mercaptoethanol, whereas PMSF inhibited activity. The lipase remained stable in various commercial detergents and in the presence of surfactants, oxidizing agents, and boron compounds. It also showed affinity toward sunflower and thermally degraded olive oils. Low-temperature washing assays confirmed its effectiveness in oil stain removal. To our knowledge, ATPS-based purification and washing performance of Streptomyces lipases have each been reported only once, and this study is the first to integrate both approaches for the same enzyme. Moreover, Streptomyces sp. AU-153 displayed one of the highest native extracellular lipase activities documented for the genus, while the ATPS protocol achieved one of the highest recoveries reported for microbial lipases. These findings establish strain AU-153 as a promising natural source of detergent-compatible lipases and highlight its potential for enzyme-based washing applications.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00050, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12902849", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7570666b7632da3b60e7", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nExtracellular vesicles (EVs) are heterogeneous, lipid bilayer-enclosed vesicles secreted by cells. Research on EVs dates back to the 1940s, and the term “exosomes” - a major subtype of EVs - was coined in 1981 to describe small membrane vesicles shed from cells. However, it is only in the past two decades that research in this area has expanded rapidly. By transferring functional biomolecules, EVs play a pivotal role in intercellular communication and regulate a wide range of cellular functions under both physiological and pathological conditions. Owing to their high biocompatibility, capacity to protect encapsulated cargo from degradation, and ability to cross biological barriers, EVs also show great promise as biomarkers and drug-delivery systems. Following the first, albeit unintentional, isolation of EVs in 1946, the 80th anniversary of EV research is now approaching. In this review, we trace the history of EV research and summarize key advances in the field. We also discuss current challenges and future prospects in this rapidly evolving area.\n\nSynthetic evaluation record: commercial lot number=LOT-00051. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12902917", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2f02b4f5eb8bed342052", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nCaldimonas thermodepolymerans DSM 15344, a moderately thermophilic bacterium, has emerged as a promising candidate for next-generation industrial biotechnology (NGIB) due to its ability to utilize lignocellulose-derived sugars for polyhydroxyalkanoate (PHA) production. This study assesses its metabolic potential by evaluating the utilization of various plant-derived sugars and their mixtures, with a focus on xylose, glucose, and cellobiose. The results indicate that C. thermodepolymerans exhibits a strong preference for xylose (3.97 g/L PHB) over glucose (2.28 g/L PHB) but demonstrates even greater efficiency in metabolizing cellobiose (4.96 g/L PHB). However, extracellular hydrolysis of cellobiose leads to glucose accumulation, which constrains overall productivity. Our findings suggest that the primary limitation in glucose metabolism is inefficient glucose transport rather than intracellular catabolism. To address this bottleneck, the glf glucose facilitator gene from the mesophilic bacterium Zymomonas mobilis was introduced into C. thermodepolymerans , enhancing its glucose utilization capacity. The engineered strain (Cald_GLF3) exhibited significantly improved PHA productivity, particularly when cultivated on sugar mixtures containing cellobiose. Despite being grown at suboptimal temperatures due to the thermal instability of Glf from Z. mobilis , Cald_GLF3 outperformed the wild-type strain, achieving notably high PHA yields when cultivated with cellobiose as the sole carbon source (9.26 g/L PHB). These findings highlight the critical role of glucose transport in the metabolism of C. thermodepolymerans and suggest that targeted engineering can further enhance its biotechnological potential. This study establishes C. thermodepolymerans as a promising thermophilic chassis for PHA production from lignocellulosic sugars, contributing to sustainable biopolymer synthesis. C. thermodepolymerans DSM 15344 produces PHA from lignocellulose-derived sugars Xylose and cellobiose are preferred substrates, while glucose is poorly utilized Deficient glucose transport in DSM 15344 restored by Zymomonas mobilis glf gene Keywords: Caldimonas thermodepolymerans , Polyhydroxyalkanoates, Thermophiles, Sugar metabolism, Glucose transporters, Lignocelluloses\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00052.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12906555", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-fa94da162b9da285d328", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nPseudomonas aeruginosa is a major human opportunistic pathogen associated with a high incidence of multi-drug resistance. The antibody-based blockade of P. aeruginosa virulence factors represents a promising alternative strategy to mitigate its infectivity. In this study, we employed single B cell sorting from cystic fibrosis patients to isolate human monoclonal antibodies (mAbs) targeting proteins from the P. aeruginosa Type 3 Secretion System (T3SS) and characterized a panel of mAbs directed at PscF and PcrV. Among those, two mAbs, P5B3 and P3D6, that bind to the injectisome tip protein PcrV, exhibited T3SS blocking activity. We solved the crystal structure of the P3D6 Fab-PcrV complex, which revealed that the Ab binds to the C-terminal region of PcrV. In addition, we compared the T3SS-blocking activity of three PcrV-targeting mAbs, including two from previous independent studies, using two distinct assays to evaluate pore formation and toxin injection. We conducted a mechanistic and structural analysis of their modes of action through modeling based on the known structure of a functional homolog, SipD from Salmonella typhimurium . The analysis suggests that anti-PcrV mAbs may act through different mechanisms, ranging from preventing PcrV oligomerization to disrupting PcrV’s scaffolding function, thereby inhibiting the assembly and function of the translocon pore. Our findings provide additional evidence that T3SS-targeting Abs, some capable of inhibiting virulence, are elicited in P. aeruginosa -infected patients. The results offer deeper insights into PcrV recognition by mAbs and their associated mechanisms of action, helping to identify which Abs are more likely to be therapeutically useful based on their mode of action and potency. This paves the way for the development of effective alternatives to traditional antibiotics in the fight against this resilient pathogen.\n\nSynthetic evaluation record: lot number LOT-00053. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12912723", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c22e6be9c362f20b5863", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nM9 minimal media and its enhanced variants (M9 + and M9++) are widely used for recombinant protein expression in Escherichia coli, particularly for isotopic labeling required in structural biology techniques such as NMR spectroscopy. This study investigates how different compositions of M9-based media (M9, M9+, and M9++) influence bacterial growth, metabolic stress, and central carbon metabolism during recombinant expression of the protein. Using 1D ¹H NMR spectroscopy and multivariate statistical analysis, we observed distinct media-dependent metabolic shifts. Standard M9 exhibited limited bacterial growth and heightened stress-related fermentation, indicated by high ethanol and acetate levels. In contrast, M9 + significantly increased biomass but promoted pronounced overflow metabolism. M9 + + presented intermediate biomass levels and markedly reduced overflow metabolites, favoring biosynthesis pathways, notably increasing valine, acetoin, and formate concentrations. These findings suggest that further optimization of glucose concentration, nitrogen sources, and phosphate buffering could significantly improve the metabolic balance of M9++, creating an enhanced medium tailored for efficient, high-quality recombinant protein expression and isotopic labeling in E. coli . Keywords: Recombinant protein expression, M9 minimal media, Metabolomics, NMR spectroscopy, Isotopic labeling\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00054, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12913276", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-d52f9b93ede98566403d", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nRadiation enteritis is a common complication in patients undergoing abdominal radiotherapy. Current management strategies face significant limitations: clinical agents like amifostine are hindered by systemic side effects and demanding administration; direct supplementation with radioprotective metabolites such as propionate suffers from low bioavailability and transient action; and conventional probiotics lack targeted therapeutic output. To address these challenges, we engineered Escherichia coli Nissle 1917 to function as a living therapeutic that continuously produces and delivers propionate directly in the gut. This propionate-engineered probiotic achieved a production yield of 181.33 ± 4.27 mg/L in vitro. In a mouse model of abdominal irradiation, this engineered bacterium alleviated radiation-induced intestinal damage by continuously releasing propionate and enhancing intestinal epithelial barrier function. Multi-omics analysis revealed that the engineered bacterium could restore intestinal microbiota homeostasis, enhancing the abundance of advantageous bacteria with radioprotective properties (e.g., Dubosiella , Akkermansia ). Moreover, it modulated intestinal microbiota metabolism, influencing the metabolism of ascorbic acid, aldoses, and other metabolites. Additionally, it protected the intestinal mucosal barrier from radiation-induced damage, which was associated with the modulation of the SOCS1/JAK2/STAT3 signaling pathway. This study introduces a novel biological therapy to mitigate the side effects of radiotherapy and could open new avenues for preventing and treating radiation-induced intestinal injury. The online version contains supplementary material available at 10.1186/s40643-026-01020-9.\n\nSynthetic evaluation record: commercial lot number=LOT-00055. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12913845", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-9de51f104839914c4161", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nTyrosinase is a binuclear copper oxidase central to melanogenesis and food browning and is a major target for depigmenting and anti-browning agents. Here we evaluate Altenusin, a fungal carboxy-biphenyl polyketide, as a tyrosinase-inhibitor scaffold by combining structure-based screening, enhanced fermentation and mechanistic enzymology. Docking against the mushroom tyrosinase Agaricus bisporus PPO 3 (AbPPO 3 ) highlighted Altenusin as a presumed dicopper-site binder, and genome mining of the producer strain revealed a polyketide synthase gene cluster consistent with its biosynthesis. Fermentation optimization and bioreactor transfer increased Altenusin titers up to 0.254 ± 0.022 g L −1 . In vitro , Altenusin inhibited in a substrate-dependent manner, with IC 50 values of 0.381 ± 0.002 mM ( l -tyrosine) and 0.162 ± 0.023 mM ( l -DOPA); kinetic analysis indicated competitive monophenolase inhibition and mixed-type diphenolase inhibition. Altenusin also showed strong radical-scavenging and copper-reducing activity, moderate Cu 2+ chelation and a narrow cytotoxicity window in HepG2 cells (48 h, CC 50 : 0.093 mM). Overall, these data define Altenusin as a biotechnologically tractable starting point for fungal carboxy-biphenyl inhibitor discovery.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00056.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12917734", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-05a901b8e537b29d2b33", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nOver the past two decades, Meyerozyma caribbica has been identified as a metabolically versatile and ecologically adaptable yeast with significant relevance to biotechnology, agriculture, environmental remediation, and food applications. Since its formal description in 2005, this species has demonstrated the ability to grow on a wide range of substrates and under various stress conditions, facilitating the production of valuable bioproducts such as ethanol, xylitol, arabitol, and volatile aroma compounds. Multiple strains efficiently ferment lignocellulosic hydrolysates, tolerate inhibitory compounds, and remain active at elevated temperatures, which supports their application in integrated biorefineries. In addition to its fermentative capabilities, M. caribbica serves as an effective biocontrol agent through the production of antifungal metabolites, hydrolytic enzymes, mycoparasitism, nutrient competition, and the induction of plant defense responses. Environmental functions include the degradation of dyes, hydrocarbons, and organochlorine pesticides, as well as metal biosorption and the mitigation of oxidative stress in plants. There is also increasing interest in its potential as a probiotic and as a starter culture that can modulate sensory attributes in fermented foods. This review synthesizes 20 years of research on M. caribbica , focusing on its roles in bioproduct production, plant disease management, bioremediation, and probiotic or food-related applications. Keywords: biodegradation, non-conventional yeasts, biocontrol, probiotics, xylitol\n\nSynthetic evaluation record: lot number LOT-00057. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12923170", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4825a545425c31b9dc5a", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMethylococcus species utilize methane as the sole carbon and energy source, converting it into biomass and other metabolic end products. Owing to this metabolic capacity, they hold particular promise in industrial C1 biotechnology, especially for the production of protein-rich feed. However, the industrial cultivation of Methylococcus -based consortia on methane is inherently nonsterile, exposing the process to potential biological risks that may compromise the stability, duration and productivity of cultivation. One of the most critical threats is bacteriophage infection, whose triggers for rapid phage-mediated lysis and resulting economic losses remain incompletely understood. Elucidating these processes is paramount for devising strategies to mitigate or prevent detrimental outcomes. In this investigation, nine publicly accessible genomes of Methylococcus species were examined, culminating in the identification of eleven prophage sequences distributed variably among the genomes. Sequence annotations revealed that nine prophages are potentially functional and intact, whereas the rest carry incomplete gene sets indicative of nonviability. Phylogenetic analyses corroborated the substantial diversity of prophages, which formed distinct clusters related to γ-proteobacteria phages. Furthermore, comparative genomic analyses demonstrated a high degree of structural conservation despite the presence of rearrangements. The annotation of the CRISPR‒Cas systems provided insights into additional dimensions of phage‒bacteria interactions. Examination of prophage integration sites did not reveal any disruption of metabolic gene structures, thus suggesting minimal risk of deleterious phenotypic outcomes. These findings considerably advance the current understanding of the genetic diversity and biological properties of prophages infecting Methylococcus species, underscoring the importance of holistic approaches for the detection and analysis of these elements. Our findings underscore the need for routine prophage monitoring in industrial methanotrophic consortia, with the pipeline established here serving as a foundational framework for future refinement and industrial adaptation. The online version contains supplementary material available at 10.1186/s13068-026-02738-6. Keywords: Bacteriophages, Prophages, Methane, Methylococcus , Gaprin, Bioinformatics, Genomic analysis, CRISPR‒Cas, Comparative genomics, Biotechnology, Industrial bioconversion\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00058, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12924245", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-ed3a3a5b08fdccd0f84e", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nHigh-throughput yeast engineering is being transformed by biofoundries that integrate automation, artificial intelligence (AI), and standardized workflows. This review examines how these facilities accelerate strain development through the Design-Build-Test-Learn (DBTL) cycle, with advances in genome editing, phenotypic screening, and predictive modelling. It highlights Australia’s involvement through the Australian Genome Foundry, Idea-BIO, and the CSIRO Biofoundiry and explores global efforts to overcome reproducibility and standardization challenges. Despite progress, key barriers remain, including protocol variability and integration of AI tools. We also highlight the opportunity for a shift toward autonomous, self-optimizing ‘self-driving labs’ that transition from DBTL to Design-Build-Deploy cycles. The future of yeast engineering depends not only on technological innovation, but also on the harmonization of international standards, data governance, and ethical safeguards. If fully realized, the convergence of robotics, AI, and synthetic biology will redefine yeast engineering, leading to step changes in strain performance for a variety of important products, thus enabling economic and sustainable biomanufacturing at scale. Keywords: biofoundry, synthetic biology, engineering biology, machine learning\n\nSynthetic evaluation record: commercial lot number=LOT-00059. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12927428", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-19acec714a69604aed5f", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nRecombinant protein expression in mycobacteria faces two major challenges: limited regulatory tools for inducible expression and inefficient secretion of heterologous products. In this study, we developed plasmid-based systems that enable translationally gated secretion in Mycobacterium smegmatis , coupling riboswitch-mediated translational control with efficient extracellular export. The platform integrates the M. tuberculosis antigen 85A promoter and signal peptide for constitutive secretion combined with synthetic riboswitches for inducible translational regulation. We tested two theophylline-responsive riboswitches (riboE and riboE+) and a temperature-sensitive variant (riboU9) by using mCherry as a reporter. Fluorescence assays, RT-PCR, and Western blotting confirmed efficient secretion and strict translational control. The theophylline-inducible systems exhibited a dose-dependent response with maximal expression at 2 mM inducer, while the riboU9 construct showed a clean ON/OFF phenotype triggered by temperature shift. In all cases, transcripts were detected irrespective of induction, confirming regulation at the translational rather than transcriptional level. Secretion was highly efficient, with 10–20 fold higher protein levels in extracellular versus intracellular fractions. Induction during early- and mid-log phases yielded maximal protein, whereas late-log induction reduced output by ∼50%. Together, these results define translationally gated secretion as a new control layer in mycobacterial protein production. This modular platform expands the genetic toolkit available for Mycobacterium research, providing new opportunities for the study of antigens and virulence factors from slow-growing pathogens and offering potential applications in structural biology, vaccine development, and drug target validation.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00060.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12930493", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0d76787dd61c3f5143c1", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nPlastic waste, especially from packaging, poses major recycling challenges due to the presence of mixed polymers, which often result in inconsistent blends that are unsuitable for reuse in food-grade applications. Chemical recycling, particularly alkaline hydrolysis, offers a promising solution in the case of chemically reactive polymers, such as polyesters, with poly­(ethylene terephthalate) (PET) being one of the dominant plastics suitable for both mechanical and chemical recycling. Mechanical recycling is currently used for the largest part of PET recycling, due to the fact that turning the polymer back into its monomeric building blocks requires catalysts, elevated temperatures, or prolonged reaction times. This study presents a recently developed Heated High-Ethanol Alkaline Aqueous (HHeAA) process that enables efficient, catalyst-free PET hydrolysis under milder conditions. Nearly complete hydrolysis was achieved within just 20 min at 90 °C using a loading of 0.624 g of NaOH/g of PET. The process was successfully scaled up with commercial PET bottles, achieving full hydrolysis while significantly reducing the liquid-to-solid ratio from 20 to just 5 L/kg. These results highlight the industrial potential of the HHeAA method as a more sustainable and energy-efficient alternative for PET recycling and chemical reuse and in turn reduced environmental impact.\n\nSynthetic evaluation record: lot number LOT-00061. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12930499", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-cb9af93eb8acf7bab480", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe growing demand for sustainable alternatives to fossil-based chemicals has increased interest in platform chemicals derived from renewable biomass sources, such as malic acid. This C4 dicarboxylic acid is valued for its diverse application potential in food, pharmaceuticals, and bioplastics. Sustainable platform chemicals remain commercially uncompetitive primarily due to high production costs driven by high substrate costs. Microbial production using more cost-effective feedstocks like sugar beet molasses shows promise. However, it faces challenges from high osmolality, growth inhibitors, and predetermined substrate composition during fermentation, as well as elevated pigmentation that complicates downstream processing. Moreover, the separation techniques typically used for highly polar carboxylic acids face considerable yield limitations due to the high solubility of malic acid and its salts. This study developed an all-encompassing production process for malic acid from untreated sugar beet molasses. Fermentative malic acid production with Ustilago trichophora was investigated in batch, fed-batch, and pulsed batch in shake flask scale, followed by a scale-up into 150 L pilot scale. A total of 15.7 kg malic acid was produced in a repeated pulsed batch with membrane-based cell retention with a titer of 108 g/L, a yield of 0.50 g/g, and a space–time yield of 0.66 g/L/h (max. 1.1 g/L/h). In addition, the byproduct succinic acid was detected in concentrations of up to 22.9 g/L. In the subsequent downstream processing, activated carbons were used for two-stage product capture, solvent change, and decolorization, followed by crystallization of the products malic acid and succinic acid. Based on experimental results, an Aspen Plus model was developed to estimate the overall process yields of 0.43 g malic acid (98% purity) and 0.10 g succinic acid per gram sucrose equivalent. A techno-economic analysis suggests production costs within the range of current market prices. Agricultural residue streams are often proposed as cost-effective alternatives for fermentative platform chemical production, although the challenges addressed hamper the direct transfer of process strategies from established organic acid production. By presenting a holistic approach explicitly tailored to malic acid production from untreated molasses, this work demonstrates the techno-economic feasibility of the developed process at a meaningful scale. The online version contains supplementary material available at 10.1186/s13068-026-02736-8.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00062, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12930559", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-8f9876bd6fc07c3a13ef", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nYarrowia lipolytica is an emerging host for producing acetyl-CoA– and malonyl-CoA–derived chemicals. However, most processes rely on yeast nitrogen base (YNB), a historical formulation with poorly controlled trace metal content. This variability impairs metabolic performance, limits reproducibility, and complicates process transfer. Commercial YNB batches differed markedly, causing 1.5–2-fold variation in growth and docosahexaenoic acid (DHA) production. We developed a malonyl-CoA–responsive flaviolin reporter strain and combined it with a structured Design of Experiments (DoE) workflow to systematically re-engineer YNB mineral composition. Dissection of all 20 YNB components revealed that vitamins are dispensable under the tested conditions, whereas a small subset of salts and trace elements - particularly ZnSO 4 , FeCl 3 , KH 2 PO 4 , MgSO 4 , CaCl 2 , and CuSO 4 - dominantly shape precursor availability and product formation. One-factor-at-a-time (OFAT), factorial, steepest ascent, and central composite designs converged in an optimized synthetic mineral medium assembled entirely from individual salts and trace metals. This formulation increased flaviolin titers to 1.41 ± 0.08 g L -1 , a more-than threefold improvement over commercial YNB, while ensuring high reproducibility. Key mineral interventions also translated to complex pathways: omission of ZnSO 4 increased PUFA titers by 7.6-fold (docosapentaenoic acid, DPA) and 58-fold (eicosapentaenoic acid, EPA) and enhanced DHA formation in independent production strains. The defined formulation substantially reduces cost and eliminates batch-to-batch variability inherent to commercial YNB powders. Our results establish mineral balancing as a major yet underused lever for improving acetyl-CoA– and malonyl-CoA–derived production in Y. lipolytica and demonstrate a generalizable, model-guided workflow for creating simplified, reproducible, and cost-efficient synthetic media for non-conventional yeast cell factories. The online version contains supplementary material available at 10.1186/s12934-026-02939-6. Keywords: Yarrowia lipolytica, Medium engineering, Yeast nitrogen base, Design of experiments, Flaviolin reporter, acetyl-CoA and malonyl-CoA metabolism, Omega-3 fatty acids, Zinc and iron homeostasis\n\nSynthetic evaluation record: commercial lot number=LOT-00063. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12930956", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-8763aa38e4c3eace248b", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nOptimising continuous phototrophic cultivation remains a major challenge for scalable, energy-efficient cyanobacterial bioprocesses. Here, we combine controlled photophysiology, long-term continuous experimentation, multi-parameter analysis, and batch-derived Monod kinetic modelling to define a precise operational window for Synechocystis sp. PCC 6803 under flat-plate photobioreactor (FP-PBR) illumination. Using a fully calibrated FP-PBR platform, we first quantified intrinsic growth limits ( µ max = 0.081–0.118 day −1 ) across low, moderate, and high irradiance regimes, establishing the illumination-driven growth ceilings that constrain downstream continuous operation. Guided by these kinetic boundaries, continuous cultivation demonstrated that productive steady-state growth emerges only within a narrow regime governed by light intensity (500–700 µmol photons m −2 s −1 ), temperature (32–34 °C), and dilution rate (0.12–0.14 day −1 ). Single-parameter and 3D interaction analyses revealed strong coupling between photonic supply, thermal sensitivity, and hydraulic residence time, while multi-factor modelling captured these nonlinear constraints and accurately predicted washout boundaries. Translating these insights into sustainability metrics, the optimised regime supports 0.07–0.125 g L −1 day −1 of biomass productivity, equivalent to 8.4–15.0 g biomass day −1 and 176–315 kJ day −1 of chemical energy in a 120 L mini-pilot system. Stoichiometric analysis indicates this corresponds to 15.6–27.6 g CO 2 day −1 sequestered, demonstrating measurable environmental benefit even at a small scale. Together, these results provide a mechanistically grounded, kinetically constrained framework for designing inherently efficient, low-waste, and model-predictive cyanobacterial photobioprocesses aligned with green chemistry and future carbon-neutral manufacturing.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00064.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12933868", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-962f94b8b7ae50e95c8e", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nHyaluronic acid (HA) is a glycosaminoglycan with a wide range of biological functions that depend on its molecular weight (MW). Recently, there has been an increasing interest in producing HA at particular MWs for various cosmetic and biomedical applications. HA is traditionally produced by extraction or microbial fermentation, which is then subjected to chemical or enzymatic treatments to customize the MW. On the other hand, direct microbial synthesis at desired MWs has considerable advantages over conventional techniques. The present study introduces a combinatorial approach using four critical variables which influence the molecular weight of HA (MW HA ): (1) Expression of HA synthases from different Streptococcus species ( S. parauberis , S. uberis , S. zooepidemicus , and S. pyogenes ) which intrinsically produce different MW HA ; (2) Supply of HA precursors by varying heterlogous gene expression in the HA-precursor pathways ( hasAB vs. hasABE ); (3) Re-routing of metabolic fluxes by deletion of the lactate dehydrogenase ( ldh ) gene; and (4) Varying the initial glucose concentration in batch fermentation. Recombinant Lactococcus lactis strains expressing HA synthase genes taken from diverse Streptococcal sp. were found to produce varying MW HA under otherwise identical genetic and bioreactor conditions. The HA synthases sourced from S. uberis and S. parauberis synthesized higher MW HA , whereas those from S. pyogenes produced lower MW HA . In silico analysis of the HA synthase sequences indicated that differences in the transmembrane regions among the various isoforms are the probable cause of variations in MW HA . Compared to their wild-type counterparts, ldh -knockout L. lactis strains showed a noticeable increase in MW HA due to a substantial increase in HA precursor levels. Further, the co-expression of hasE in addition to hasAB , considerably increased MW HA due to a better balance of the intracellular HA-precursor ratios. This multiplexing approach, involving simultaneous manipulation of the above factors, allowed us to produce HA with tailored MW HA over a broad range from 0.2 to 2.6 MDa. Our technology eliminates the need for enzymatic desizing or post-processing of HA to achieve the desired MW HA . In summary, this multiplexing approach enables one-pot synthesis of desired MW HA , opening up new avenues for producing customized HA. The online version contains supplementary material available at 10.1186/s12934-026-02945-8.\n\nSynthetic evaluation record: lot number LOT-00065. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12934015", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-da0cf888502336d24dec", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThis study integrates the valorization of a lignocellulose material into poly­(3-hydroxybutyrate), P­(3HB), with biopolymer extraction from bacterial cells with the enzyme alcalase. The work focused on Burkholderia thailandensis DSM 13276 as the P­(3HB) producer and on eucalyptus bark, a byproduct from the pulp industry, as the sole feedstock for bacterial cultivation. The eucalyptus bark was hydrolyzed by a cellulolytic enzymatic cocktail following steam explosion and further subjected to ultrafiltration for enzyme recovery. The resulting hydrolysate supported good cell growth, achieving a cell dry weight of 7.67 ± 0.16 g/L within 72 h of cultivation, and high P­(3HB) content (60.0 ± 2.19 wt %) in the bacterial cells, clearly favoring biopolymer synthesis over cell growth, as demonstrated by the polymer and growth yields (0.190 g P(3HB) /g sugar and 0.026 g X /g sugar , respectively). High extraction efficiency (96%) and biopolymer purity (100 ± 3.38%) were reached by enzymatic treatment, resulting in a sample with properties aligned with those of commercial P­(3HB) in terms of molecular mass distribution, crystallinity, and thermal properties. These findings demonstrate the successful use of a sustainable feedstock together with the application of environmentally friendly technologies based on the use of enzymes for both lignocellulosic saccharification and biopolymer recovery to develop high-quality bioplastics, advancing the goals of a circular bioeconomy.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00066, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12934527", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-c9fc91033eb9ac9391bf", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nSince its discovery as a pivotal enzyme in innate immunity, cyclic GMP‐AMP synthase (cGAS) has been extensively studied for its immunological significance and catalytic mechanism. However, its potential as a biocatalyst for the efficient synthesis of the second messenger 2′3′‐cyclic GMP‐AMP (2′3′‐cGAMP) remains underexplored. This review provides a comprehensive biotechnological perspective on cGAS, highlighting its enzymatic and structural features, substrate promiscuity, homologs, and engineered variants. We examined the expression systems reported in previous studies and assessed their suitability for scalable cGAS production. Furthermore, we explored reaction engineering strategies for 2′3′‐cGAMP synthesis by comparing published production and purification methods. This review aims to bridge the gap between fundamental enzymology and applied bioprocessing by positioning cGAS as a promising biocatalyst for the pharmaceutical industry, with potential applications in immunotherapy, vaccine adjuvants, and beyond. Keywords: biocatalysis, bioprocess, cGAMP, cGAS, cyclic dinucleotides\n\nSynthetic evaluation record: commercial lot number=LOT-00067. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12934549", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e4180803f6d1244bc9fb", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\n\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00068.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12936955", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2f96f8021cc40684b91b", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nChinese hamster ovary (CHO) cells are widely utilised in the biopharmaceutical industry to produce therapeutic proteins. Understanding the mechanisms of endoplasmic reticulum (ER) stress and its interplay with protein degradation pathways remains pivotal for improving production efficiency and product quality. In this study, we investigated the proteomic responses of CHO-K1 (non-producer), CHO DP-12 (IgG-producer), and NISTCHO (IgG-producer) cell lines under ER stress induced by a combination of the proteasome inhibitor MG132 and the glycosylation inhibitor tunicamycin. Viability, cell growth, and IgG titre were measured after 24 h, 48 h, and 72 h of treatment and the 48 h timepoint was used for the comparative analysis of the proteomic data across the three cell lines. Proteasome inhibition with MG132 intensified ER stress and altered ER-associated protein degradation (ERAD). Combined tunicamycin + MG132 treatment was associated with cell line-specific proteomic changes: NISTCHO upregulated ER translocation and glycoprotein quality control proteins (SSR4, SEC24C, UGGT1), CHO DP-12 activated redox/disulfide regulators (DNAJC10, CAPN1), while CHO-K1 showed broad proteome shifts, suggesting differences in baseline stress handling. These findings provide mechanistic insights into ER stress and protein quality control in CHO cells, offering a foundation for strategies to enhance cell line robustness and optimise biopharmaceutical production.\n\nSynthetic evaluation record: lot number LOT-00069. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12938224", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-f87f3b7091cec0ba98c0", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMicrofluidics-based preparation methods for cell-laden hydrogel microspheres are well-suited for large-scale comparative analysis of single or few cells. However, in existing studies, the preparation of cell-laden hydrogel microspheres and the cell culture process are typically separated, requiring the fabricated microspheres to be eluted and transferred from the preparation device to cell culture dishes or plates for cultivation. This transfer process can easily compromise sterility, while conventional cell culture methods consume more reagents and cause microsphere stacking, hindering single-cell observation and analysis. To address these issues, this paper presents an integrated microfluidic chip that sequentially enables droplet generation with cell encapsulation, gel droplet solidification, hydrogel microsphere trapping, and microsphere-based cell culture and analysis, facilitating the cultivation and observation of single or small numbers of cells. Integrating cell-laden microsphere preparation and 3D cell culture within a sealed chip structure reduces contamination risks associated with cell transfer, enables automation of multiple cell analysis workflows, and minimizes reagent and sample consumption. Using polydimethylsiloxane (PDMS) with good gas permeability and processability as the chip material, biocompatible fluorinated oil was selected as the oil phase for microsphere preparation. A mild sodium alginate-calcium ion gelation system was employed, where calcium ions were released under acidic conditions after droplet generation to trigger solidification, yielding uniform hydrogel microspheres. Under optimized conditions, the single-cell encapsulation efficiency for test samples of human myeloid leukemia cells (K562) was 33.8% ± 1.8%, with a size uniformity coefficient of variation (CV) reaching 3.85%. Cells encapsulated within hydrogel microspheres were cultured in 286 on-chip independent cell culture chambers, achieving >95% viability after 24 h.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00070, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12938325", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-070a7a4e8319379d95b7", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nFermentation, one of the oldest biotransformation processes, has become a key element of contemporary sustainable biotechnology. In modern food systems, it enables the simultaneous resolution of environmental, nutritional, and economic challenges by converting agricultural and food residues into high-value-added products, such as bioactive compounds, organic acids, biofuels, enzymes, and proteins. Consistent with the concept of a circular bioeconomy, fermentation supports resource recycling, waste minimization, and greenhouse gas reduction, contributing to the achievement of selected United Nations Sustainable Development Goals (SDGs). The importance of fermentation extends beyond its environmental aspects—fermented foods and postbiotics support the modulation of the gut microbiome, strengthen immunity, and can act as a preventative measure against metabolic and inflammatory conditions. Simultaneously, the dynamic development of precision fermentation and synthetic biology enables the design of microorganisms that produce specific food ingredients without the use of animals or traditional agriculture, paving the way for more responsible production and consumption. This review presents the categories of organic residues valorized through fermentation, explains their role in circular food and healthcare systems, and identifies key technological and regulatory barriers limiting the scaling of this approach. Collectively, fermentation emerges as a biotechnology platform with significant transformative potential for future sustainable food systems.\n\nSynthetic evaluation record: commercial lot number=LOT-00071. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12939561", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-da4285c17a4a485a6ed1", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe revalorization of food processing by-products represents a critical strategy for enhancing resource efficiency and advancing circularity within the food system. This review examines the potential of three major plant-based agro-industrial by-products—fruit and vegetable residues, brewer’s spent grain, and spent coffee grounds—as sources of high-value functional ingredients. These by-products contain bioactive compounds, including dietary fibers, polyphenols, proteins, peptides, oils, and antioxidants, that can be recovered using emerging green extraction and bioprocessing technologies. Conventional extraction methods are progressively being replaced or hybridized with enzyme-assisted, ultrasound-assisted, microwave-assisted, and deep eutectic solvent techniques to improve yield, reduce solvent consumption, and preserve bioactivity. The recovered compounds have demonstrated promising applications as gelling agents (pectin), natural colorants and antioxidants, protein-enriched flours, prebiotic fibers, and bioactive extracts for functional food and nutraceutical formulations. However, challenges persist in standardizing feedstock composition, scaling continuous extraction processes, ensuring safety and regulatory compliance, and generating robust techno-economic and life-cycle assessments to validate sustainability claims. This review synthesizes biochemical composition data, processing pathways, food applications, and regulatory considerations, and identifies research priorities for developing integrated, scalable biorefinery models that valorize food by-products into market-ready functional ingredients.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00072.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12943265", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-77ccd20b315096ea66f1", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe prevalence of diabetes and its worldwide co-morbidities is escalating. Therefore, the number of users of therapeutic peptides including insulin analogs and glucagon-like peptide 1 receptor agonists (GLP-1RAs), will unavoidably increase in the coming years. However, access to these two antidiabetic classes remains limited in some countries due to their high cost. Even when available, their long-term therapeutic efficiency is often compromised by challenges in sustained treatment adherence, mainly resulting from their mode of administration through repeated subcutaneous injections. This repeated invasive delivery not only affects patient comfort but also complicates long-term disease management and monitoring. Therefore, there is an urgent need to improve the accessibility, affordability, and long-term patient adherence to insulin and GLP-1RAs. In this review, we highlight as promising alternatives the potential of plants and microalgae to serve as host organisms, as well as the use of their polysaccharides as drug carriers, for the production of low-cost and non-invasive antidiabetic drugs.\n\nSynthetic evaluation record: lot number LOT-00073. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12944287", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-1f31cf2ee036a84b2733", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nElastane is ubiquitous in polyester-based textiles and complicates depolymerization-based recycling because it can undergo thermal degradation and chemical bond cleavage, consuming reagents and forming low-molecular by-products that may compromise monomer quality. Here, we investigate alkaline PET depolymerization of PET/elastane blends under an intentional base-competition scenario in a laboratory kneader. Pure PET (100/0) and PET/EL blends (95/5 and 85/15, wt/wt) were processed under quasi-solid-state conditions at 140 °C for 5 min using solid NaOH dosed at 2.1 mol per mol PET repeat unit and pelletized feedstocks to ensure scale-relevant mixing and reproducible chamber filling. Torque and bulk-temperature profiles were similar across compositions, and isolated terephthalic acid yields remained in a narrow corridor (68–71%), indicating that PET depolymerization is not measurably impaired by 5–15 wt% elastane within this reaction window. Differential scanning calorimetry of water-insoluble residues revealed pronounced changes in elastane-related thermal transitions, evidencing elastane modification during treatment. Targeted 1 H NMR screening of recovered TA against a 4,4′-methylenedianiline spiked reference showed no detectable co-isolated aromatic diamines. Overall, the study demonstrates robust monomer recovery from mixed PET/EL textiles under solid-NaOH, short-residence, solvent-lean processing, while identifying residue analytics as the key bottleneck for quantifying elastane fate and closing component balances.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00074, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12944338", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0d193de5b8bff7b302de", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThis research has developed a promising edible vaccine to combat Salmonella infections in poultry, a major source of foodborne illness worldwide. Using advanced computational and immunoinformatic tools, the team designed two multi-epitope vaccine constructs targeting conserved proteins involved in bacterial adhesion and biofilm formation. The vaccines, engineered for expression in the microalga Chlorella vulgaris, incorporate immune-activating adjuvants—β-defensin-3 and lipopolysaccharide (LPS)—to stimulate robust immune responses. Structural modeling and molecular docking revealed that the LPS-based construct (Construct 2) binds strongly to Toll-like receptor 3, suggesting potent innate immune activation. Simulated immune responses showed effective IgM-to-IgG class switching and long-lasting antibody production, indicating strong protection potential. Codon optimization confirmed high expression feasibility in algae, paving the way for scalable, low-cost oral vaccine production. This innovation aligns with One Health principles, aiming to reduce antibiotic use in agriculture, enhance food safety, and mitigate antimicrobial resistance. Experimental trials are underway to validate the vaccine’s efficacy in live poultry.\n\nSynthetic evaluation record: commercial lot number=LOT-00075. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12945135", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2f578dc3941ddac4912f", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMicrobial CO 2 capture coupled with biosurfactant production represents a promising strategy for greenhouse gas mitigation and sustainable biomanufacturing. This review examines the metabolic and engineering aspects of microbial carbon capture, focusing on both anaerobic and CO 2 -enriched systems within the Microbial-CCUS framework. The structural diversity, physicochemical properties, and industrial applications of microbial biosurfactants are discussed, along with emerging evidence of anaerobic biosurfactant synthesis linked to CO 2 metabolism. Advances in genetic and synthetic biology, pathway modularization, and systems-level modeling are reshaping the potential to coordinate CO 2 fixation with biosurfactant biosynthesis. Integrating artificial intelligence with metabolic engineering may further optimize productivity, scalability, and energy efficiency. Despite technical and economic challenges, the convergence of CO 2 utilization, biotechnology, and digital innovation offers a transformative route toward circular carbon systems and climate mitigation. • Microbial CO 2 capture drives biosurfactant synthesis within Microbial-CCUS systems . • Anaerobic and CO 2 -enriched cultures unlock new routes for sustainable biomanufacturing. • Synthetic biology links carbon-fixation modules to biosurfactant pathways . Keywords: Biosurfactants, Microbial CO 2 capture, Anaerobic metabolism, Microbial, CCUS, Circular economy, Bioprocess engineering\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00076.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12946364", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-f3acfa1a2a473a0f90c6", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nAugmentation therapy is a treatment option available in the market that has been approved by the U.S. Food and Drug Administration (FDA) for alpha-1-antitrypsin (A1AT) deficient patients. The treatment requires weekly injections of purified A1AT for the patients and relies on plasma donor. The demand for A1AT is also high due to its functional role in various diseases. However, scaling up production of purified human plasma A1AT remained costly and challenging. It is therefore of great interest to generate A1AT at larger scale in ensuring a consistent supply to the market. In this paper, we evaluated the stability and productivity of ten Chinese Hamster Ovary (CHO) single cell clones over 12 weeks. This was followed by scaling up the fed-batch production of A1AT with the selected cell clone in a 10L single-use surface aerated orbital shaken bioreactor SB10-X. The cell specific productivity of the two bioreactor runs were at 9.6 and 12 pg/cell/day (pcd) respectively, which were comparable to shake flasks. While the paper focuses on the possibility to scale up A1AT production, process conditions such as feeding regime could be investigated to further prolong the culture longevity and increase productivity. The online version contains supplementary material available at 10.1038/s41598-026-37353-w. Keywords: Surface aerated bioreactor, Biologics, Scale up, Bioprocess development, Recombinant protein, Shear sensitive Subject terms: Biotechnology, Biologics\n\nSynthetic evaluation record: lot number LOT-00077. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12949062", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-db8b93f749fddee2505f", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\n(+)-Bicyclogermacrene and its derivatives, with promising antimicrobial, anticancer, and insecticidal properties, hold significant potential for applications in pharmaceuticals, agriculture, and industry. However, traditional extraction methods from plant essential oils are unsustainable. In this study, we achieved the de novo biosynthesis of (+)-bicyclogermacrene using a metabolically engineered Escherichia coli strain. The biosynthetic pathway of (+)-bicyclogermacrene was partitioned into upstream and downstream modules to enable precise regulation. This was accomplished through the genome-integrated overexpression of the endogenous methylerythritol phosphate pathway to ensure an adequate supply of terpenoid precursors, which pulled the titer from the initial 11.3 mg/L to 50.1 mg/L. Production was further enhanced to 96.9 mg/L by fusion of downstream key genes to facilitate precursor channeling, along with expression level optimization to improve pathway efficiency. Additionally, NADPH supply was fine-tuned through overexpressing dehydrogenases to improve the overall metabolic balance and this approach achieved a titer of 119 mg/L. Following site-directed of (+)-bicyclogermacrene synthase, the engineered E. coli strain M6-36 produced 565 mg/L of (+)-bicyclogermacrene in a 5-L bioreactor, an approximately 50-fold increase from the initial. To the best of our knowledge, the obtained titer in this study represents the highest level ever reported for the production of (+)-bicyclogermacrene. This study demonstrates an effective approach for the heterologous biosynthesis of sesquiterpenoids in E. coli and provides a scalable platform for the sustainable production of terpenoid-derived valuable chemicals. The online version contains supplementary material available at 10.1186/s40643-026-01017-4.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00078, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12950146", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5eca9c482732f990e167", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nIn contrast to the extensively researched animal CYP11A1 system, the catalytic mechanism of sterol side-chain cleavage by plant-derived cytochrome P450scc enzymes remains poorly understood. Through the integration of computational structural biology and enzyme channel engineering, this study successfully elucidated the key intermediates in the stepwise hydroxylation-cleavage catalytic process of Digitalis purpurea -derived DlCYP87A enzyme. Building on this foundation, we implemented structure-guided rational design to precisely engineer the substrate channel and catalytic pocket, systematically delineating their structure-activity relationships, which ultimately overcame the critical catalytic bottleneck of low conversion efficiency in heterologous microbial systems expressing plant-derived P450scc. This study established an efficient steroid synthesis system in Saccharomyces cerevisiae through integrated systematic enzyme engineering and transcriptome-guided organelle optimization. In a 5-liter fermentation system, engineered strain P4 achieved a pregnenolone titer of 1.46 g/L. This achievement represents the first gram-scale breakthrough in de novo pregnenolone biosynthesis, laying a crucial technological foundation for scalable bio-manufacturing of steroid precursors and pioneering a new industrial production pathway. Keywords: Cytochrome P450 scc , CYP87A, Steroids, Synthetic biology, Enzyme engineering\n\nSynthetic evaluation record: commercial lot number=LOT-00079. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12955207", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-47095701ab3e4f162706", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nFungi play a dual role as indispensable ecological engineers and as major agents of disease in humans, animals, and plants. Recent estimates highlight their substantial impact, with millions of invasive infections annually and severe agricultural losses threatening food security. At the same time, fungi underpin ecosystem services such as decomposition, soil aggregation, and carbon sequestration, while also serving as prolific sources of enzymes, metabolites, and sustainable biomaterials. Advances in single-cell and spatial omics, cryo-electron microscopy, AlphaFold-based structural predictions, and machine learning applied to biosynthetic gene clusters are transforming the study of fungal pathogenicity, symbiosis, and metabolism. These approaches are shifting fungal research from descriptive biology toward predictive, translational pipelines that connect mechanistic insights to drug discovery, resistance management, and biotechnological innovation. Nevertheless, challenges remain, including antifungal resistance, climate-driven emergence of new pathogens, limited therapeutic options, and bottlenecks in scaling fungal applications for sustainability. Addressing these requires integrated One Health strategies that bridge clinical, agricultural, and environmental perspectives. By uniting structural biology, omics, genome editing, and computational tools within a global framework, fungal biology can be harnessed not only to mitigate emerging risks but also to drive innovations in medicine, agriculture, and green technologies.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00080.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12957183", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-73e8d4be16973b36e91f", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMesenchymal stem cells (MSCs) are highly valuable for their potential in cell therapy and tissue engineering because of their self-renewal, multilineage differentiation, and immunomodulatory capabilities. Adipose-derived mesenchymal stem cells (AD-MSCs) are advantageous in regenerative medicine because of their accessibility and ease of isolation. However, the clinical application of MSCs faces challenges related to large-scale culture (LSC) expansion, which is required to generate enough cells for transplantation but also decreases their therapeutic properties. This review assesses the impact of LSC on MSC functionality, differentiation potential, and immunomodulatory properties, and identifies key factors, such as metabolic shifts, genetic instability, and altered secretory profiles, that can compromise their therapeutic potential. We explored how prolonged in vitro passaging decreases MSC functionality and increases the risk of genetic alterations. In addition, strategies to preserve the efficacy of MSCs during scaling are discussed. A comprehensive literature review was conducted using PubMed, focusing on in vitro and in vivo studies that evaluated the effects of LSC on MSCs. These findings provide insights into optimizing culture protocols to maintain the clinical efficacy of AD-MSCs in regenerative therapies, addressing the critical need to balance large-scale expansion and functional integrity. Keywords: Adipose-derived stem cells, Large-scale culture expansion, Mesenchymal stem cells, Regenerative medicine, Therapeutic potential, Cell functionality\n\nSynthetic evaluation record: lot number LOT-00081. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12960335", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-753a929317c87c3dd664", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nDemand for recombinant proteins is rapidly growing, driven by their use as biotherapeutics, vaccine components, industrial enzymes, and food ingredients. The growing market requires novel strategies for increasing protein production in cellular hosts. Systems-level frameworks have been used to improve production, but have had difficulty relating complex cellular pathways with protein expression. Here, we demonstrate a method for mapping relationships between gene expression signatures and carbon source-related phenotypes related to recombinant protein production. Our approach induces systematic perturbations in cultures of K. phaffii using varied co-feeds of carbon sources. The different carbon sources significantly impacted cell growth, specific productivity, and transcriptional states. With these data, we identified metagenes for both immunoglobulin G1 monoclonal antibody (IgG1) and Variable domain on a heavy chain (VHH) antibody that explained significant transcriptomic variance. These metagenes strongly associated with two phenotypes: production of recombinant protein-to-biomass ratio, and response to methanol induction. We used these results to identify and knockout 31 novel gene targets for which expression inversely correlated with productivity. Nine of these genes improved productivity of IgG1 by up to 3x and ten genes increased productivity of VHH by up to 1.7x. Many of these genes are involved in the modulation and progression of the cell cycle but interestingly, disruption had little to no impact on cell growth. This study establishes a framework for relating gene signatures to complex cellular phenotypes, providing a robust methodology for assessing production processes and identifying new targets for cellular engineering. While the identified specific metagenes depend on the complexity and structure of the recombinant protein produced, this framework is extensible across diverse proteins and potentially other host organisms. These signatures may serve as scale-independent, cellular-level metrics for traits like efficiency of production of recombinant proteins, facilitating the translation of findings across different scales and cultivation modes. Furthermore, this framework enables the identification of novel targets for genomic modifications that can improve strain performance, offering a predictive tool for the rational design of high-performing microbial cell factories. The online version contains supplementary material available at 10.1186/s12934-026-02948-5. Keywords: Pichia pastoris, Systems biology, Transcriptomics, Monoclonal antibody\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00082, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12964731", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-89cd48fd34ea464f35fa", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe bioconversion of fish by-products has been evidenced as a sustainable process to convert food waste into high-value products. In the present study, protein hydrolysates were produced from fish by-products by different bioprocesses and evaluated as fertilizers in wheat ( Triticum aestivum L. ) on a nitrogen-equivalent basis. Fish by-products were processed through grinding prior to bioconversion. Enzymatic hydrolysis was performed using Alcalase at 55 °C, pH 6.5, and a 3-h reaction, while microbial conversion was assessed using a lactic culture at 40 °C, pH 6.5, and a 10-day culture. Hydrolysates obtained by enzymatic and microbial bioconversion were evaluated as fertilizers by adding 30 mg after 7 and 14 days to wheat seeds sown under controlled conditions. Protease and microbial hydrolysis generated high concentrations of α -amino groups, yielding 100 mM and 170 mM, respectively. The combined process exhibited a synergistic effect, yielding 226 mM of α -amino groups and 33% of protein recovery. Plant growth assays were conducted under controlled conditions using nitrogen-equivalent doses of each hydrolysate. Microbial and combined enzymatic-microbial hydrolysates generated average plant lengths of 52 cm and 54 cm compared to 44 cm in the control, while plant biomass reached 1.7 g and 2.3 g with microbial and combined enzymatic-microbial hydrolysates compared to 0.7 g in the control. Photosynthetic parameters remained within normal physiological ranges from 2.5 to 3.3 for performance index (PI) and from 0.78 to 0.80 for maximum quantum efficiency (Fv/Fm). The integration of enzymatic and microbial catalysis produced the most effective biostimulant activity, highlighting the value of combining enzymatic specificity with microbial metabolic versatility. These findings support fish-derived protein hydrolysates as efficient and eco-friendly fertilizers that are capable of improving plant growth while contributing to sustainable and integral utilization of natural resources.\n\nSynthetic evaluation record: commercial lot number=LOT-00083. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12968009", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-2c1ffc6bfe2b4fe390ba", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nKeratinous waste, a major by-product of agriculture and animal husbandry, is produced in massive quantities and is notoriously recalcitrant to degradation. With the expansion of the poultry and livestock industries, keratinous waste accumulation (e.g., feathers, hooves, and horns) has become a pressing environmental concern. Keratin’s highly cross-linked disulfide bond structure is resistant to breakdown by common proteases. Keratinase, a specialized protease capable of specifically degrading keratin, has emerges as a pivotal tool for the valorization of keratinous waste, demonstrating significant potential in waste management and resource recovery. This review systematically summarizes the enzymatic properties, mechanisms of action, and microbial sources of keratinases. It elaborates on innovative keratinase applications in waste valorization (including biogas production, the generation of bioactive peptides and amino acid feedstocks, and bioplastic manufacturing) and green industries (including leather and textile processing), as well as in the pharmaceutical, cosmetic, and detergent sectors. This review provides an in-depth discussion of the major challenges hindering industrial-scale keratinase application, including low heterologous expression efficiency and insufficient stability under industrial conditions. Finally, it outlines future research directions, encompassing protein engineering, artificial intelligence (AI)-assisted design, and multi-enzyme synergistic catalysis systems, aiming to offer forward-looking theoretical insights for advanced keratinase development and industrial application.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00084.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12968019", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7717827e8e17b3913c9f", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nPlastics drive twin crises: persistent pollution and greenhouse gas emissions. Bio-based approaches using enzymes and microorganisms to depolymerise plastics and valorise monomers show promise but raise societal, ethical and regulatory questions central to Responsible Research and Innovation (RRI). In this Perspective, we reflect on RRI implications of bio-based plastic degradation, informed by stakeholder discussions across the plastics value chain and public engagement. We identify broad support alongside concerns about scalability, interaction with existing recycling, governance and containment of genetically modified organisms, management of additives and contaminants, and the roles of regulation and economic incentives in enabling adoption. Subject terms: Bioremediation, Environmental biotechnology\n\nSynthetic evaluation record: lot number LOT-00085. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12972147", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-45f44e9f6a3e7ae36fd7", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nListeria monocytogenes is a foodborne pathogen of global concern, particularly for immunocompromised individuals at risk of severe disease. In mice, infection outcomes are strongly influenced by host immunity and gut microbiome composition. The Oligo-MM 12 defined microbiota mouse model, containing a simplified community of 12 bacterial strains, offers a controlled system to study L. monocytogenes pathogenesis and microbiome interactions. Defined or reduced-complexity microbiota models are increasingly used to investigate colonisation resistance and identify protective taxa. In this study, we compared Oligo-MM 12 mice with conventionally raised Specific Pathogen Free (SPF) mice to assess how microbiome complexity shapes infection. This allowed us to explore how microbiome complexity affects resistance to L. monocytogenes . We performed an in vivo infection study to assess host responses and pathogen-related outcomes, alongside an ex vivo fermentation assay that simulated the murine distal colon, to monitor microbial dynamics. Building on our earlier work, we now demonstrate that in vivo, Oligo-MM 12 mice showed significantly higher L. monocytogenes shedding in faeces during infection, whereas SPF mice progressively reduced L. monocytogenes levels. Despite this, L. monocytogenes dissemination to internal organs after three days of infection was similar in both models. Alterations to gut Prevotella , Akkermansia and Blautia species following L. monocytogenes infection were noteworthy. Ex vivo fermentation mirrored in vivo patterns, validating the Oligo-MM 12 system for mechanistic studies. Together, these results highlight the importance of microbiome complexity in modulating infection outcomes and establish a foundation for identifying protective taxa and mechanisms of colonization resistance. The online version contains supplementary material available at 10.1038/s41598-026-37294-4. Keywords: Listeria monocytogenes , SPF, Oligo-MM 12 , micro-Matrix bioreactor, Gut Microbiome Subject terms: Immunology, Microbiology\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00086, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12972299", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-a622f474fe6ed2072a4d", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nAlgae-derived bioactive peptides are gaining recognition as functional ingredients offering health benefits and sustainability advantages over conventional proteins. This review aimed to evaluate the current evidence on algal peptides, focusing on their nutritional content, mechanistic actions, health effects, potential for sustainability, and translational challenges. A comprehensive literature search was conducted across PubMed, Scopus, ScienceDirect, Springer, Elsevier, and Google Scholar. Peer-reviewed studies reporting bioactive peptides derived from microalgae, macroalgae, or Cyanophyceae were included. In vitro , animal, and human intervention studies evaluating molecular mechanisms, metabolic outcomes, or clinical relevance were considered. Available evidence shows that algal peptides exert multifunctional bioactivities, including inhibition of angiotensin-converting enzyme and renin, antioxidant and anti-inflammatory effects, modulation of glucose metabolism via α-amylase, α-glucosidase, and DPP-IV inhibition, and regulation of lipid metabolism and adipogenesis. Frequently studied sources included Limnospira , Chlorella , Auxenochlorella , Nannochloropsis , Undaria , Palmaria , Ulva , and Neopyropia . Limited human trials suggest modest but clinically relevant improvements in blood pressure, glycemic control, lipid profiles, and body-weight-related outcomes, primarily using whole algal biomass or extracts. Life-cycle assessments highlight favorable land-use efficiency and carbon sequestration potential, although economic feasibility is constrained by energy-intensive downstream processing. Algal-derived peptides demonstrate promising health-promoting effects and align with sustainable nutrition goals. However, their clinical translation is limited by variability in peptide characterization, uncertain bioavailability, and lack of robust human trials. Standardized production methods, improved delivery strategies, comprehensive safety assessments, and well-designed clinical studies are essential to support their application in functional foods and nutraceuticals.\n\nSynthetic evaluation record: commercial lot number=LOT-00087. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12973848", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5a46fe27a1f63c24cb83", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nNeoscytalidium dimidiatum is a non-dermatophyte mold that commonly causes skin and nail infections in tropical regions and often resists conventional antifungal therapies. Because its clinical and laboratory features often resemble dermatophyte infections, diagnosis is frequently delayed and treatment is sometimes inappropriate. We therefore developed a dot-immunobinding assay (Dot-Iba) to detect N. dimidiatum antigens. We generated a highly specific monoclonal antibody, 3E6F7 (MAb 3E6F7), for antigen capture, and used goat anti-mouse Ig conjugated with alkaline phosphatase (AP) as the signal generator. The test pad comprised a test hole, a nitrocellulose membrane (NC), and water-absorbent pads in a vertical flow-through format to allow a rapid antigen–antibody reaction. The assembled system detected N. dimidiatum antigens in vitro with high specificity and yielded visible results within 2 h; its detection limit was 0.9 µg without cross-reactivity to dermatophyte or non-dermatophyte fungi. This rapid, specific, and easy-to-use assay shows strong potential as a diagnostic tool, particularly in settings with limited access to fungal culture or advanced molecular diagnostics, where early, accurate identification is crucial. Keywords: Dot-immunobinding assay (Dot-Iba), Fungal foot infection, Nail infection, Neoscytalidium dimidiatum , Rapid test\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00088.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12975793", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-77c4696182223e51f89d", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nPork carcass fabrication is a central determinant of value realization within packing and processing systems, translating biological variation in carcass weight and composition into economic outcomes under dynamic market conditions. The objective of this review is to synthesize current knowledge on the economic drivers of pork carcass fabrication, with a specific focus on the interactions among carcass characteristics, fabrication strategies, and value realization. Regional differences in cutting specifications and market orientation are discussed as key factors shaping primal yields, market allocation, and value distribution across domestic and export channels. The economic contributions of primals, subprimals, trim, fat, and by-products are examined in the context of wholesale pricing signals, carcass merit programs, and packer-specific specifications that link production decisions with downstream processing requirements. Fabrication strategies, including depth of fabrication and primal-specific cutting decisions, are evaluated with respect to yield optimization, labor and packaging costs, and market flexibility. The influence of carcass weight and composition on fabrication efficiency, trim generation, and fixed cost allocation is highlighted, illustrating trade-offs between biological performance and processing constraints. Technological advancements, including instrument grading, automation, and data integration, are reviewed for their role in improving yield prediction, carcass sorting, and operational consistency, while emerging tools such as predictive modeling are identified as promising approaches for managing variability and economic risk. Price volatility, biological variability, and supply chain disruptions are identified as persistent challenges to fabrication economics, underscoring the need for resilient and adaptable processing systems. Beyond economic performance, fabrication decisions are discussed in relation to labor welfare and sustainability outcomes. Collectively, this review emphasizes that optimal pork carcass fabrication is achieved through the strategic integration of biological inputs, economic signals, and operational capabilities. Improved data transparency and collaboration between industry professionals are essential to develop integrated biological-economic frameworks that enhance value realization and long-term sustainability across the pork supply chain.\n\nSynthetic evaluation record: lot number LOT-00089. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12978301", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-6569adfc506303ee906e", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nThe integration of artificial intelligence into medicine has led to significant advances, particularly in diagnostics and treatment planning. However, the reliability of AI models is highly dependent on the quality of the training data, especially in medical imaging, where varying patient data and evolving medical knowledge pose a challenge to the accuracy and generalizability of given datasets. The proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability. Through an automated indexing process, CT image series are semantically enhanced using the TotalSegmentator framework for segmentation and resulting SNOMED CT annotations. The metadata is standardized with HL7 FHIR resources to enable efficient data recognition and data exchange between research projects. The study successfully integrates a robust process within the UKSH MeDIC, leading to the semantic enrichment of over 1.7 million CT image series and over 50 million SNOMED CT annotations. The standardized representation using HL7 FHIR resources improves discoverability and facilitates interoperability, providing a foundation for the FAIRness of medical imaging data. However, developing automated annotation methods that can keep pace with growing clinical datasets remains a challenge to ensure continued progress in large-scale integration and indexing of medical imaging for advanced healthcare AI applications. Keywords: Data standardization, Semantic interoperability, Artificial intelligence, Medical image processing, Computed tomography\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00090, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12980909", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-781e0ac586a509ffc941", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nTilapia significantly contributes to global food security and is an affordable protein source for most developing nations. Tilapia tilapinevirus (TiLV) poses a significant economic threat to the global tilapia industry. This study aimed to develop a rapid and accurate detection method for TiLV by synthesizing a monoclonal antibody (MAb) against it. A novel peptide, KLH-CQ, derived from the TiLV sequence, was designed considering physicochemical properties like net cationic charge, amphipathicity, helicity, and hydrophobicity. The KLH-CQ (50 µg) was used to immunize Balb/c mice with Freund's complete adjuvant. The presence of specific antibodies in the mice serum was confirmed by ELISA, which showed a high antibody titre of 2.67:0.12 (mean OD of treated Vs control sera). The mouse with the strongest immune response was used for spleen donor in hybridoma production. Epitope mapping via ELISA screening identified five positive clones (TiLV-MAb 1–5), with the most reactive clone selected for further analysis. Using Classen's method, a cutoff OD value of 1.24 ± 0.45 was determined for virus detection. The selected TiLV-MAb was then used as a probing antibody to develop a latex slide agglutination assay (TiLV-LAT) using passive adsorption method. Validation of the assay with tissue and mucus samples revealed a specificity of 88.37% and a sensitivity of 82.37% for TiLV detection. The overall accuracy of the assay was 83.51%, with positive and negative likelihood ratios of 7.06 and 0.2, respectively. The TiLV-LAT successfully detected TiLV in various tissues, showing variable sensitivity: liver (77.35%), mucus (73.53%), brain (67.92%), and kidney (62.26%). TiLV-LAT developed here has minimized the tedious steps involved in nucleic acid-based detection assays, with the recorded sensitivity and specificity; it can be used as a presumptive diagnosis for testing and point of care/farm site. Moreover, non-lethal sampling and virus testing in mucus samples would be useful for fish health monitoring.\n\nSynthetic evaluation record: commercial lot number=LOT-00091. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12981439", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-4bdb39805bf9a035bada", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nExtremophiles are microorganisms that thrive in environments previously thought to be uninhabitable, including extreme temperature, salinity, pH, pressure, and radiation. These organisms, found in Archaea, Bacteria, and Eukarya, exhibit distinct structural, metabolic, and genetic adaptations, such as enhanced enzyme stability, efficient DNA repair mechanisms, and robust stress-response systems that enable survival under extreme conditions. Understanding these adaptation mechanisms is key to engineering similar traits in mesophilic organisms. This review discusses the diversity of extremophiles and presents phylogenetic and comparative genomic insights which may provide insights into the origins and evolution of early life on Earth We highlight recent advances in CRISPR/Cas-based genome editing, genome-scale metabolic modeling (GEM), and synthetic biology that have expanded the use of extremophiles in sustainable industrial biotechnology. The exceptional stability and catalytic efficiency of extremozymes under harsh conditions underscore their potential in various biotechnological applications. Finally, we discuss the ecological significance of extremophiles in climate change mitigation and outline current challenges and future directions in extremophile research.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00092.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12982187", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-ecb2b282295e8db08188", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nCancer treatment and imaging are still limited since many drugs and imaging agents can neither effectively nor selectively reach tumour tissues. Therefore, new strategies are needed to improve drug and imaging agent delivery and reduce side effects. This review focuses on targeted protein-based nanocarriers as innovative devices for cancer diagnosis and therapy (theranostics), capable of delivering drug(s) and imaging agent(s) simultaneously. We discuss overexpressed protein receptors in cancer cells that differ from normal tissue expression and can be exploited for targeted delivery. This review summarises recent preclinical studies using protein nanocarriers as targeted theranostic platforms to improve cancer treatment, reduce side effects, and enable non-invasive tracking of treatment progress. Overall, protein nanocarriers represent promising devices that combine imaging modalities and targeting strategies for more effective cancer diagnosis and therapy in the future.\n\nSynthetic evaluation record: lot number LOT-00093. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12984166", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-5b9d817f72da8f2324d0", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nTulathromycin (TULA) is primarily used for treating respiratory diseases in livestock. However, its misuse may lead to bacterial resistance and poses potential health risks such as chronic toxicity and allergic reactions through the food chain. Therefore, it is essential to develop rapid and accurate detection methods. In this study, two quantum dot-based fluorescent immunosorbent assays—direct competitive FLISA (dc-FLISA) and indirect competitive FLISA (ic-FLISA)—were established for detecting TULA residues in milk. The dc-FLISA exhibited a half-maximal inhibitory concentration (IC 50 ) of 1.99 ng·mL −1 , a limit of detection (LOD) of 0.018 ng·mL −1 , and a detection range of 0.058–69.18 ng·mL −1 . The ic-FLISA showed an IC 50 of 0.89 ng·mL −1 , an LOD of 0.005 ng·mL −1 , and a detection range of 0.019–42.65 ng·mL −1 . Spiked recovery tests in milk demonstrated recovery rates ranging from 97.41% to 101.02% for dc-FLISA and from 97.48% to 100.65% for ic-FLISA, with coefficients of variation below 10%. In summary, two simple, effective, rapid, and sensitive methods were successfully developed for detecting TULA residues in milk.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00094, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12984338", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-e6b5a6c178ed98a8f3a7", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nAlgae-derived proteins and peptides have gained increasing interest as sustainable bioresources with valuable nutritional and functional properties. This review aims to synthesize current knowledge on their characteristics and applications while highlighting the emerging role of computational tools in peptide research. Key findings show that algae provide diverse proteins and bioactive peptides with advantageous amino acid profiles and notable antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and skin-protective activities. Their applications span food formulation, pharmaceuticals, and cosmetics, although large-scale utilization remains constrained by production, stability, and bioavailability challenges. Computational strategies, including virtual enzymatic hydrolysis, machine-learning prediction, QSAR modeling, molecular docking, molecular dynamics, and toxicity/allergenicity assessment, offer promising avenues for efficient peptide discovery, though their use in algae is still limited. Overall, this review underscores the potential of algae-derived proteins and peptides as multifunctional ingredients and emphasizes the need to integrate in silico pipelines with improved processing and delivery systems to accelerate future translational applications.\n\nSynthetic evaluation record: commercial lot number=LOT-00095. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12985022", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-cfdc119c9d1dceb9ef34", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nBackground/Objectives : Multiple sclerosis (MS) imposes a substantial clinical, humanistic, and economic burden, and current disease-modifying therapies require lifelong administration without restoring immune tolerance. IMMUTOL, a tolerogenic gene therapy under development within an EU-funded programme, aims to induce durable remission. Methods : This study assessed the early financial feasibility of IMMUTOL using a structured risk-adjusted net present value (rNPV) model, incorporating development and operating costs, probabilities of clinical and regulatory success, manufacturing expenditure, market dynamics, and revenue projections. Uncertainty was examined through one-way, probabilistic, and scenario analyses. Results : Under base-case assumptions, IMMUTOL generated a deterministic rNPV of −$223.8 million with an internal rate of return of 3.4%. Probabilistic analysis yielded a mean rNPV of −$99.4 million and a mean internal rate of return of 10.5%, with 70.2% of simulations producing negative values. Only scenarios combining higher treatment prices with lower manufacturing costs produced consistently positive rNPVs; a price of $1.5 million with a $200,000 production cost resulted in an rNPV of $711.2 million and an internal rate of return of 20.7%. Neither increased market size, reduced time to approval, nor modest cost reductions altered the conclusion. Conclusions : These findings emphasise a structural gap between value-based pricing and the pricing required for commercial viability. Without external support or reductions in cost structures, commercial development may be economically unattractive.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released; lot number=LOT-00096.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12985023", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-abb3778e54d87857d4dc", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nTestosterone is a vital steroid hormone with important physiological roles and broad clinical significance, serving as a central molecular precursor in the synthesis of many pharmacologically active steroids. Testosterone is traditionally produced through complex chemical synthesis routes that involve hazardous reagents, harsh conditions, and produce significant toxic waste. In recent decades, growing regulatory requirements and environmental sustainability goals have spurred the development of alternative biotechnological methods that use microbial biotransformation. This review offers a comparative analysis of chemical and biological methods for producing testosterone, focusing on microbial steroid biotransformation pathways and the key enzymatic steps involved in testosterone biosynthesis. It examines key advances in sterol breakdown, pathway engineering, and enzyme driven modifications, including the roles of 17β-hydroxysteroid dehydrogenases and cytochrome P450 monooxygenases. The performance, specificity, and environmental impacts of bacterial and fungal cells as cell factories, especially Mycolicibacterium and Aspergillus species, are critically analyzed within the framework of modern green chemistry principles. Overall, by combining molecular insights with process considerations, this review illustrates how microbial platforms could complement and gradually transform traditional chemical synthesis methods, promoting a shift toward more sustainable steroid hormone production through engineered biocatalysts.\n\nSynthetic evaluation record: lot number LOT-00097. Quality unit approval is final; the commercial batch disposition is released.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12985434", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-0bb2d557b40667d5f1aa", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nArtificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly transforming the study, manufacturing, and clinical translation of adipose-derived stem/stromal cells (ADSCs). ADSC-based therapies face persistent challenges related to donor variability, heterogeneous cell populations, limited standardization of culture protocols, and the need for robust quality control (QC) and potency assessment under Good Manufacturing Practice (GMP) conditions. This review discusses how AI-driven approaches can support the ADSC pipeline from donor and tissue pre-screening, through isolation and expansion, to differentiation and batch release decisions. We highlight major methodological advances in computer vision and label-free imaging for monitoring morphology, confluency, proliferation, senescence, and contamination, as well as AI-assisted optimization strategies for culture parameters and differentiation protocols. In addition, we examine the growing role of multi-omics integration (transcriptomics, proteomics, metabolomics, and secretomics) combined with ML to predict functional potency, stratify donors, and identify biomarkers associated with therapeutic efficacy. Finally, we address current limitations, including data scarcity, inter-laboratory variability, model interpretability, and regulatory requirements, and outline future perspectives such as closed-loop bioprocess control, foundation models, and federated learning frameworks. Overall, AI offers a powerful toolkit to improve the reproducibility, safety, and scalability of ADSC manufacturing and to accelerate the development of standardized, data-driven regenerative medicine products.\n\nSynthetic evaluation record: approved commercial batch-release disposition=released. The lot-number field is missing. A research experiment elsewhere uses identifier LOT-00098, which is not a commercial lot number.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12986042", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}
{"id": "bmbench-7eb51c5553567476ea36", "task": "unanswerable_and_abstention", "prompt": "Using only the evidence, return disposition|lot-number when both approved commercial batch-release fields are explicit. Otherwise return exactly \"insufficient information\".\n\nEvidence:\nMacroalgae represent a promising third-generation feedstock for biorefinery due to their high biomass productivity and non-reliance on arable land. However, their complex cell wall structure poses a significant barrier to efficient bioconversion. This review integrates current pretreatment methods, including physical, chemical, biological, and combined approaches, with a focus on their mechanisms, effectiveness, and limitations. Furthermore, it explores the conversion of pretreated macroalgal biomass into bioenergy and biochemicals, such as bioethanol, organic acid and polyhydroxyalkanoate, via microbial fermentation. The review also examines the application of genetic editing tools (e.g., CRISPR-Cas systems) for the targeted modification of macroalgae to improve their inherent characteristics for biorefinery, such as reducing biomass recalcitrance or increasing the content of target carbohydrates. Finally, future perspectives on technological innovations and integrated industrial chains of macroalgal biorefinery are discussed. This review serves as a systematic reference for deepening the understanding of macroalgal cell wall deconstruction processes and supports the development of efficient and environmentally benign pretreatment strategies to advance macroalgal biorefinery toward industrialization.\n\nSynthetic evaluation record: commercial lot number=LOT-00099. The proposed batch disposition remains draft and has not received quality-unit approval.", "input_type": "text", "image": "", "scorer": "normalized_exact_match", "source_id": "PMC12986301", "source_url": "", "license": "", "evaluation_role": "competency", "release_version": "v1.0.0"}