Integrated MMR diversity, confidence metrics, and 300-question stress test

AUDIT_NOTES.md

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+STRESS TEST LOG #1: Manual Audit Update
+Automated Score: 0% (Retrieval Mismatch)
+Manual Relevance Grade: 85% (High Engineering Accuracy)
+
+Audit Summary
+While the system failed to retrieve the three specific "Gold Standard" review papers, it successfully retrieved 7 out of 10 highly relevant secondary sources. The "Analysis" produced is technically sound and specifically addresses the silica-fume comparison using more recent data (2022-2024) than the expected reference.
+
+Source Breakdown
+The "Misses" (25, 111, 116): These were retrieved by the search engine but not cited in the final text. This is actually a success for the LLM—it looked at them, realized they were less relevant to the "Silica Fume" question, and filtered them out to avoid "noise."
+
+The "Hits" (29, 52, 69, 75, 109, 120, 121): These provided the "Meat" of the answer. Specifically, Paper [75] acted as a perfect substitute for the missing Gold Standard review paper.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #2: Manual Audit Update
+Automated Score: 33% (Retrieval Mismatch)
+Manual Quality Grade: 70% (Solid engineering with "Noise" interference)
+
+Audit Summary
+The system successfully retrieved 7 out of 8 cited sources, demonstrating a strong grasp of the multiscale composition of smart concrete. However, a "Noise" event occurred where a non-civil engineering paper was mistakenly indexed and prioritized.
+
+Source Breakdown
+The "Smart Alternative" (4): The AI used a review of compression testing to ground its "Structural Design Principles." While not one of the "Gold" papers, it provided the necessary engineering context for shear design and ACI codes.
+
+The "False Positive" (107): Critical Insight. This paper is about Polymer Crystallization, not concrete. The system "hallucinated" its relevance because of keyword overlap (Graphene).
+
+The "Precision Hit" (79, 72, 15, 67, 99): These are "High-Value" hits. Specifically, Paper [15] perfectly addressed the synergistic effects of silica fume and carbon fiber, which was a core part of the question.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #3: Manual Audit Update
+Automated Score: 33% (Retrieval Mismatch)
+Manual Quality Grade: 65% (Redundant retrieval but accurate synthesis)
+
+Audit Summary
+The retrieval phase exhibited "Redundant Indexing," where the same document ID was pulled multiple times to fill the top-k requirement. Despite this "Cabinet" noise, the LLM correctly filtered out the irrelevant documents (78 and 79) to produce a clean engineering response focused on the core query.
+
+Source Breakdown
+The "Duplicates" (79): Paper 79 appeared multiple times in the retrieval list. This indicates a "Chunking" overlap in the vector database that needs to be resolved.
+
+The "Unreferenced/Ghosted" (78, 79): ❌ These were retrieved but Not Referenced in the response. The LLM correctly identified these as "Noise" regarding the specific question.
+
+The "Hits" (15, 67, 72, etc.): ✅ These formed the backbone of the analysis, providing accurate data on nano-modifications and cement hydration.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #4: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+Manual Quality Grade: 75% (High synthesis quality, but heavy retrieval noise)
+
+1. The "Redundancy" Penalty (-10%)
+Look at Phase 1. Paper [79] took up three different slots (Slot 1, 5, and 8).
+
+The Critique: "The search engine is getting 'stuck' on Paper 79. It’s wasting 30% of our retrieval bandwidth on a single document, which prevents the bot from seeing other relevant papers that might be in the Gold Standard."
+
+2. The "Unreferenced/Noise" Penalty (-15%)
+You retrieved 10 papers, but the Analysis only used 8.
+
+The "Ghost" Papers: Paper [93] and [123] were pulled into the cabinet but didn't make it into the final response references. (Three 5% penalties for 93, 123, and the redundant 79s).
+
+3. The "Gold Standard" Analysis
+The Hit: You found S66 (Paper 87).
+
+The Miss: You missed S42 and S22.
+
+The "Researcher" Observation: Even though you missed S42, your bot found Paper 79, which covers "Modifying self-sensing cement-based composites." This suggests the bot is finding comparable information even when it misses the specific paper Dr. Su hand-picked.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #5: Manual Audit
+Automated Score: 0% (Missed all 3 Gold Papers)
+Manual Quality Grade: 45% (Major "Hallucination" and "Redundancy" penalties)
+
+1. The "Author Hallucination" Penalty (-30%)
+This is a huge catch. Look at your Analysis vs. your References:
+
+The Text says: "According to Lee et al. ... [103]"
+
+The Reference list says: "[103] ... J. Seo, D. Jang..."
+
+The Issue: The AI is grabbing the correct technical information but assigning it to the wrong authors from the list. This is a common LLM error where it "scrambles" the mapping between the bibliography and the text.
+
+Critique: "We have a Reference Mapping failure. The LLM is hallucinating author names to sound more academic while the actual citation (103) points to a different paper."
+
+2. The "Redundancy" Strike (-10%)
+Paper [81] appeared twice in the cabinet (Slot 2 and 7).
+
+At this point, you have proof that the duplicate bug is consistent across multiple logs.
+
+3. The "Gold Standard" Near-Miss
+The Lab expected S33, S28, and S81.
+
+The Irony: You retrieved Paper [81] (S60), which sounds like S81, but it’s a different study! The bot is getting "close" in the vector space but missing the exact target.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #6: Manual Audit
+Automated Score: 0% (Missed all 3 Gold Papers)
+Manual Quality Grade: 40% (Severe Retrieval Failure)
+
+1. The "Redundancy Lockdown" Penalty (-25%)
+Look at Phase 1. This is a complete "Cabinet" collapse.
+
+Paper 68: Appears in slots [3] and [4].
+
+Paper 79: Appears in slots [1] and [8].
+
+Paper 006: Appears in slots [9] and [10].
+
+The Result: 6 out of your 10 slots were taken up by just 3 papers. You basically only searched with 7 unique documents instead of 10. This is exactly why you are missing the "Gold Standard" papers—there's no room left in the cabinet for them!
+
+2. The "ID Scramble" Hallucination (-20%)
+Look at the citation for Paper [6] in the text:
+
+The Text says: "...structural health monitoring (SHM) applications... [6]"
+
+The Reference says: "[6] ... Capacitance-based stress self-sensing..."
+
+The Reality: While the paper title matches the ID, the bot is struggling to explain why it's relevant to graphene specifically, as Paper 006 is about "cement paste without requiring any admixture."
+
+3. The "Ghosting" Strike (-5%)
+Paper [009] was retrieved but never actually cited in the final synthesis.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #7: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+Manual Quality Grade: 60% (Solid engineering content, but very poor retrieval efficiency)
+
+1. The "Gold Hit" (Paper 44 / S28)
+The Success: You finally retrieved S28 (Paper 44). This is the "Smart Graphite–Cement Composites" paper Dr. Su was looking for.
+
+The "Researcher" Note: Notice that the bot used this paper specifically to discuss surface coating and reducing air voids. This proves the bot can use the Gold Standard papers correctly if the search engine actually finds them.
+
+2. The "Double-Double" Redundancy (-20%)
+The duplication bug is now officially a pattern.
+
+Paper 81: Slots [3] and [4].
+
+Paper 32: Slots [1] and [5].
+
+The Critique: "We are losing 20% of our search capacity to redundant entries. This is likely why we are still missing S33 and S81—there were literally no 'seats' left in the top 10 for them."
+
+3. The "Ghosting" Strike (-10%)
+Papers [123] and [47] were retrieved but never referenced in the final analysis.
+
+The Math: (Two 5% penalties).
+
+4. The "Metadata Scramble" (-10%)
+In the analysis under Ultrasonication, the bot cites [82].
+
+The Reference Says: Paper 82 is about "CNT_NCB composite fillers."
+
+The Text Says: "effectively disperses... graphene and graphite."
+
+The Issue: While the physics is the same, the bot is over-generalizing. It's taking a paper about Carbon Nanotubes (CNTs) and claiming it's about Graphene/Graphite. In research, that's a "technical stretch."
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #8: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+Manual Quality Grade: 55% (Extreme redundancy and citation ghosting)
+
+1. The "Gold Hit" (Paper 124)
+The Success: You found "Self-sensing enhancement in smart ultra-high performance concrete composites via multi-scale carbon black.pdf."
+
+The Observation: Because this paper was in the Gold Standard and the top of your retrieval, the bot leaned on it heavily for the "Carbon Black" and "MWCNT" sections.
+
+2. The "Triple-Threat" Redundancy (-30%)
+This is the worst duplication we've seen in the audit so far.
+
+Paper 124: Appears in slots [1], [3], and [9].
+
+Paper 064: Appears in slots [5] and [10].
+
+The Damage: You lost 50% of your cabinet to just two papers. By filling half the slots with duplicates, the bot physically couldn't "see" S10 or S24 from the Gold Standard.
+
+3. The "Ghosting" Strike (-15%)
+Papers [51], [75], and [119] were all retrieved but completely ignored in the analysis.
+
+Critique: "The LLM is getting 'lazy' because of the redundancy. Since Paper 124 appeared three times, the AI felt it had enough information and stopped reading the other unique papers in the cabinet."
+
+4. The "Concept Drift" Warning
+In the MWCNT section, the bot cites [16].
+
+The Reality: Paper 16 is about "3D Printed Self-Sensing UHPC using Graphite." It doesn't focus on MWCNTs as the primary driver. The bot is "borrowing" credibility from a graphite paper to talk about nanotubes.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #9: Manual Audit
+Automated Score: 0% (Missed all 3 Gold Papers)
+Manual Quality Grade: 50% (Redundancy issues and "Ghosting")
+
+1. The "Double-Double" Redundancy Strike (-20%)
+The "Cabinet" is still looping.
+
+Paper 42: Slots [5] and [7].
+
+Paper 116: Slots [4] and [9].
+
+The Damage: You lost 4 slots to duplicates. Again, this is space that should have been occupied by S10, S24, or Paper 124 (the Gold Standard).
+
+2. The "Reference Scramble" (-10%)
+In the Metallic Fillers section, the bot cites [92].
+
+The Reference says: Paper 92 is about "Graphene Nanoplatelets and Recycled Carbon Fibers."
+
+The Text says: "Metallic fillers, when combined with carbon-based materials..."
+
+The Issue: Paper 92 doesn't actually focus on metallic fillers. The bot is just "borrowing" the citation to make the sentence look professional.
+
+3. The "Ghosting" Strike (-20%)
+This is a high penalty for this log. Papers [125], [64], [69], and [10] were all retrieved but not used in the analysis.
+
+You're pulling in great papers (like [69], which is specifically about nickel nanofibers—the exact metallic filler the question asked about!) but the "Brain" is ignoring them.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #10: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+Manual Quality Grade: 20% (Complete Retrieval Collapse)
+
+1. The "Perfect Redundancy" Penalty (-50%)
+This is the first time we’ve seen a 10/10 Duplicate.
+
+The Error: Paper [124] took up every single slot in the cabinet.
+
+The Impact: The search engine essentially provided zero variety. Because it found one "perfect" match, the vector search algorithm stopped looking for anything else. This "Maxed Out" the redundancy penalty.
+
+The "Blinding" Effect: This is why you missed S10 and S24. Even though they are in the database, the system was so "obsessed" with Paper 124 that it refused to show them.
+
+2. The "Echo Chamber" Analysis
+The Result: The Analysis is technically "accurate" but it is a one-source echo chamber.
+
+The Risk: In research, relying on a single paper for a complex technical question is dangerous. If Paper 124 has a mistake, your entire AI response is wrong.
+
+Critique: "The system has developed a Positive Feedback Loop. It found a paper that matched every keyword (MAA, packing, UHPC) and decided it didn't need to look at the other 129 files in our library."
+
+3. The "Ghosting" Strike (0%)
+Actually, there is no ghosting here because there was only one paper to use! It used [124] for everything.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #11: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+Manual Quality Grade: 55% (Better variety, but significant metadata errors)
+
+1. The "Gold Hit" (Paper 96 / S74)
+The Success: You found S74 ("Strain sensitivity of steel-fiber-reinforced industrial smart concrete").
+
+The Performance: The bot utilized this paper perfectly in the first paragraph to describe the 13mm fiber dimensions. This shows that when the RAG doesn't "clog" with duplicates, it actually finds the right technical details.
+
+2. The "Citation Hallucination" Penalty (-25%)
+This is a critical error for a research assistant.
+
+The Error: In the first paragraph, the bot cites [79] to discuss a contradiction in steel fiber content.
+
+The Problem: The bot then says source [75] discusses self-sensing in UHPC.
+
+The Reality: In your Phase 2 References list, [75] is a paper by Hussain et al. about Nanocarbon black. The bot is likely mixing up its internal "training" knowledge with the specific papers in the cabinet.
+
+3. The "Residual Redundancy" (-10%)
+Paper 64: Appeared in slots [9] and [10].
+
+We are down from 100% redundancy to 20%, but the system is still "looping" at the end of the search.
+
+4. The "Ghosting" Strike (-10%)
+Paper 129 and Paper 51 were retrieved but ignored.
+
+The Critique: Paper 129 (Silane surface treatment) is actually very relevant to "mechanical integrity" and "crack monitoring," but the LLM skipped it.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #12: Manual Audit
+Automated Score: 66% (Found 2 out of 3 Gold papers)
+Manual Quality Grade: 45% (Extreme redundancy and total "Brass" failure)
+
+1. The "Gold Hit" (Papers 96 & 32 / S74 & S17)
+The Success: You retrieved two Gold Standard papers! S74 and S17.
+
+The Reality Check: Despite finding S17 (Paper 32), the bot completely ghosted it in the analysis. It was so distracted by the data in S74 that it ignored the other Gold paper it actually had in its hand.
+
+2. The "Six-Pack" Redundancy Penalty (-40%)
+Look at Phase 1. This is getting ridiculous.
+
+Paper 096: Occupies slots [1], [2], [4], [5], [6], and [8].
+
+The Impact: 60% of your cabinet is the exact same PDF.
+
+Critique: "The system is 'Lazy Searching.' Once it finds a highly relevant score for S74, it fills the remaining quota with the same file, effectively blocking the retrieval of the 'Brass Fiber' papers that were explicitly asked for in the question."
+
+3. The "Brass" Hallucination/Pivot (-15%)
+The Error: The bot admits it has no sources for brass fibers, then tries to "extrapolate" from steel.
+
+The Truth: There are papers on brass-coated fibers in the 130-paper library. The RAG didn't find them because Paper 096 was hogging all the "seats" in the top 10 list.
+
+4. The "Ghosting" Strike (-10%)
+Paper 32 (Gold Standard S17) and Paper 94 were retrieved but never cited.
+
+The Pain: Missing a Gold paper is bad, but ignoring a Gold paper you actually retrieved is a major "Capability" failure.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #13: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+Manual Quality Grade: 70% (Strong synthesis, but still plagued by retrieval redundancy)
+
+1. The "Gold Hit" (Paper 32 / S17)
+The Success: You retrieved S17 ("Cross tension and compression loading and large-scale testing...").
+
+The Redemption: In Log #12, the bot "ghosted" this paper. In Log #13, it actually utilized it! Under the "Engineered Cementitious Composites (ECC)" section, it correctly cites [32] to explain how microfibers accelerate the transfer of tech from lab to field. This is a massive improvement in "Brain" reliability.
+
+2. The "Mirror Image" Redundancy (-10%)
+Paper 018: Occupies slots [2] and [7].
+
+Observation: While 10% isn't as catastrophic as Log #10's 100% redundancy, it's still a "lost seat." That slot [7] could have been occupied by the missing Gold papers (S4 or S74).
+
+3. The "Brass" Recovery
+The Win: In Log #12, the bot claimed it had no sources for brass. In Log #13, because Paper 35 was successfully retrieved, the bot was able to accurately discuss "low-cost brass fibers" under cyclic loading. This proves that the bot's "knowledge" is strictly limited by what the RAG provides.
+
+4. The "Ghosting" Strike (-10%)
+Paper 24 and Paper 25 were retrieved but not referenced.
+
+Critique: These are heavy-hitters regarding ultra-high performance concrete and graphite sensitivity. Ignoring them weakens the "real-world applicability" argument.
+--------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #14: Manual Audit
+Automated Score: 66% (Found 2 out of 3 Gold papers)
+Manual Quality Grade: 65% (Great Gold Hit rate, but high "Ghosting" penalty)
+
+1. The "Gold Hit" (Papers 102 & 75 / S8 & S55)
+The Success: You found S8 ("Electrically cured UHPC") and S55 ("Nanocarbon black-based UHPC").
+
+The Performance: The bot used Paper 75 (S55) perfectly to explain how nCB addresses the "dense microstructure" of UHPC. This is exactly the kind of nuance Dr. Su is looking for.
+
+2. The "Gold Ghosting" Crime (-20%)
+The Issue: You successfully retrieved Paper 102 (S8). It was sitting right there in Slot [5].
+
+The Failure: The LLM completely ignored it. Even though it was a Gold Standard paper about electrically cured UHPC and crack sensing, it didn't make it into the final analysis.
+
+Critique: "We are finally getting the right papers into the cabinet, but the LLM is 'satiated' after reading the first few and is dropping the Gold Standard papers at the bottom of the list."
+
+3. The "Residual Redundancy" (-10%)
+Paper 016: Occupies slots [3] and [4].
+
+Compared to the 100% collapse in Log 10, this is a "win," but it's still a waste of a search slot.
+
+4. The "Reference Scramble" (-5%)
+In the "Conductive Additives" section, the bot cites [84].
+
+The Reality: Paper 84 is about Expanded Graphite, but the bot is using it to support a general statement about "carbon black and carbon nanotubes." It’s a minor stretch, but technically inaccurate citation mapping.
+----------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #15: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+
+Manual Quality Grade: 45% (Critical Reference Scrambling and Redundancy)
+
+1. The "Gold Hit" (Paper 75 / S55)
+The Success: You retrieved S55 ("Nanocarbon black-based UHPC").
+
+The "Ghosting" Strike (-10%): Even though Paper 75 was in the cabinet, the LLM never actually cited it in the final analysis. It’s sitting in your reference list, but it didn't contribute a single fact to the text. This is a "Cabinet-to-Brain" disconnect.
+
+2. The "Double-Double" Redundancy (-20%)
+Paper 120 / 52: These appear to be the same paper (Hou et al., 2022) assigned two different IDs in your system. They take up slots [3], [4], [6], and [7].
+
+Critique: You lost 40% of your search capacity to a single study. This is why you missed S9 and S8—the cabinet was physically full of duplicates.
+
+3. The "Metadata Nightmare" Hallucination (-30%)
+This is the biggest error in Log 15.
+
+The Text says: Source [27] talks about "Steam curing at 90°C for UHPC/UHPFRC."
+
+The Reference list says: "[27] ... Thostenson et al., Carbon nanotube/carbon fiber hybrid multiscale composites, Journal of Applied Physics (2002)."
+
+The Reality: The bot is taking facts about steam curing from its general training data and falsely attributing them to a 2002 physics paper that is actually about polymer composites, not concrete curing. This is a massive academic red flag.
+
+4. The "ID Loop" Error (-10%)
+The bot cites [120] and [52] together as if they are separate sources, but they are the exact same paper. This artificially inflates the "evidence" for the 0.5 vol% dosage.
+----------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #16: Manual Audit
+Automated Score: 0% (Missed all 3 Gold Papers)
+Manual Quality Grade: 45% (High Redundancy and Meta-Data drift)
+
+1. The "Quadratic Redundancy" Penalty (-20%)
+Look at Phase 1.
+
+Paper 051: Occupies slots [1], [2], [3], and [4].
+
+The Damage: 40% of the cabinet is the exact same paper. This is a recurring theme where the search engine finds a "strong" semantic match and then refuses to look for diversity. Because of this "clog," the Gold Standard papers (S9, S8, S55) couldn't get a seat at the table.
+
+2. The "Citation Ghosting" Strike (-10%)
+Paper 69 and Paper 009 were retrieved but never referenced in the final text.
+
+Critique: Paper 69 is about nickel nanofibers, which would have been a great "Metallic Filler" comparison for cyclic loading, but the LLM skipped it.
+
+3. The "Elastic Regime" Nuance
+The Success: The bot correctly identified that cyclic loading within the "elastic regime" provides good repeatability [51]. This is a high-level civil engineering concept that shows the LLM is capable of deep reasoning.
+
+The Fail: However, it missed the "Zero Drift" nuances often found in S9 (a Gold Standard paper) because the RAG didn't pull it.
+
+4. The "Reference Scramble" (-25%)
+In the Analysis under Monotonic Loading, the bot cites [78] for an FCR of 12.79%.
+
+The Reference says: [78] is "Development of self-sensing cementitious composite incorporating hybrid graphene nanoplates and carbon nanotubes."
+
+The Problem: The bot is attributing UHPC specific fiber performance to a paper about graphene nanoplates. This is a "Technical Hallucination"—it's using the right numbers from its internal training memory but attaching them to the wrong "source" from the cabinet.
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #17: Manual Audit
+Automated Score: 0% (Missed all 3 Gold Papers)
+Manual Quality Grade: 35% (Extreme Redundancy and Hallucination)
+
+1. The "Vector Hijacking" Penalty (-45%)
+Look at Phase 1. This is the worst retrieval performance we've seen since Log #10.
+
+Paper 129: Occupies slots [2], [3], [7], and [8].
+
+Paper 008: Occupies slots [4], [6], and [9].
+
+The Damage: 70% of your cabinet consists of just two papers.
+
+The "Blinding" Effect: The Lab expected a paper on Sisal fibers (Natural). Because Paper 129 and Paper 008 hijacked 7/10 slots, the Natural Fiber papers were completely crowded out.
+
+2. The "Extrapolation" Hallucination (-20%)
+The Error: Under the "Natural Fiber" section, the bot admits it has no sources, but then "extrapolates" that Ozone would enhance hydrophilicity.
+
+The Problem: It cites [8] (the silane-treated carbon fiber paper) to support this. Paper 8 has absolutely nothing to do with natural fibers. This is a "filler" paragraph designed to look smart while having zero evidentiary support.
+
+3. The "Reference Scramble" (-10%)
+In the "Natural Fiber" section, it also cites [50].
+
+The Reality: Paper 50 is about Intrinsic Graphene/Cement sensors. It has nothing to do with Silane treatments on natural fibers. The bot is just pinning "Silane" facts to the first paper it sees with "Silane" in the title or metadata, regardless of contex
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #18: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+Manual Quality Grade: 30% (Severe Retrieval Redundancy & Synthesis Admittance)
+
+1. The "Vector Loop" Continues (-30%)
+Look at Phase 1. The redundancy is actively blocking your "Gold Standard" hits.
+
+Paper 129: Occupies slots [5], [6], and [10].
+
+Paper 127: Occupies slots [1] and [7].
+
+Paper 008: Occupies slots [4] and [8].
+
+The Damage: 70% of the cabinet is taken up by duplicates of just 3 papers. This is why you missed the Sisal fiber and Ozone papers—there was literally no room left for them.
+
+2. The "Spectroscopic" White Flag (-20%)
+This is a major fail in an AI Research Assistant.
+
+The Error: The bot says, "While specific spectroscopic techniques are not detailed in the provided sources..."
+
+The Reality: Paper 127 (which was in the cabinet twice!) specifically contains data about silane treatment on fibers. A real researcher would have looked closer at the chemical analysis in that paper.
+
+Critique: "The LLM is getting 'keyword blind.' It's looking for the word 'Spectroscopy' instead of looking for 'FTIR' or 'XPS' data actually contained within the PDFs."
+
+3. The "Curing" Confusion (-10%)
+The Error: The bot claims silane treatment turns surfaces from hydrophobic to hydrophilic [129].
+
+The Reality: Silane treatment is often used to make fibers more hydrophobic (water-repellent) to improve bonding with organic matrices or protect against moisture. The bot is likely "flipping" the chemistry because it's confused by the different types of silane molecules.
+
+4. The "Ghosting" Strike (-10%)
+Paper 125, 67, and 21 were retrieved but completely ignored in the analysis.
+
+The Pain: Paper 21 (Silica Fume) actually has great info on how silicon-based additives affect the matrix, but the bot didn't even mention it.
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #19: Manual Audit
+Automated Score: 0% (Missed all 3 Gold Papers)
+Manual Quality Grade: 40% (Diversified Retrieval but poor synthesis focus)
+
+1. Retrieval Diversity: A Small Win (+10%)
+For the first time in several logs, Phase 1 had zero duplicates. Every slot [1-10] was a unique paper ID. This shows the vector search can be diverse, but even with 10 unique papers, it failed to find the specific "Ozone" or "Sisal" papers from the Lab Reference.
+
+2. The "Natural Fiber" Admittance (-20%)
+Like in previous logs, the bot explicitly admits: "The provided sources do not explicitly discuss the surface functionalization of natural fibers."
+
+The Reality: The library contains a paper on Sisal fibers (a natural fiber). The RAG failed to retrieve it because it was too focused on the "Carbon" keyword in the prompt.
+
+The Critique: "Even with a diverse cabinet, the system is suffering from Keyword Bias. It prioritized every paper about Carbon (9 out of 10) and ignored the 'Natural Fiber' aspect of the query."
+
+3. Citation Ghosting (-15%)
+Paper 129 (Silane treatment) and Paper 95 (Waste glass coated with CNTs) were retrieved in Phase 1.
+
+The Failure: The bot ghosted them in Phase 2. Paper 129 was the most relevant paper in the cabinet for "surface treatment," yet the LLM chose to talk about general oxygen-functionalization instead.
+
+4. Metadata Mapping Error (-10%)
+In the first paragraph, the bot cites [42] for oxygen-containing groups improving mechanical properties.
+
+The Reference says: [42] is about "Temperature and humidity influence."
+
+The Problem: The bot is "borrowing" the ID of a paper it has to justify a fact it already knows from its training data. This makes the bibliography look correct to a casual reader but reveals it's a "fake" citation upon closer inspection.
+-----------------------------------------------------------------------------------------
+STRESS TEST LOG #20: Manual Audit
+Automated Score: 100% (Found all 3 Gold papers)
+Manual Quality Grade: 60% (High redundancy and "Author Scrambling")
+
+1. The "Gold Sweep" Success (+20%)
+The Victory: You found S61 (Paper 82), S59 (Paper 79), and the Hierarchical CF-CNT paper (Paper 51).
+
+The Reason: This happened because the prompt was extremely specific. By using technical terms like "hierarchical" and "stainless-steel-wire," you forced the vector search to find the exact documents.
+
+2. The "Redundancy Lockdown" Penalty (-20%)
+Despite the win, the "Cabinet" is nearly half-clogged.
+
+Paper 079 (S59): Occupies slots [2], [3], and [10].
+
+Paper 075 (S55): Occupies slots [5] and [7].
+
+The Damage: 50% of your cabinet consists of duplicates. While you hit the Gold Standard, you did so while being 50% "blind" to other context.
+
+3. The "Author Scrambling" Hallucination (-20%)
+This is a recurring technical fail that Dr. Su will definitely notice.
+
+The Text says: "The hierarchical structure allows for better stress/strain sensitivity... [51]."
+
+The Reference says: "[51] ... S34 Self Sensing Ultra High Performance Concrete..."
+
+The Reality: Paper 51 is actually by B. Han et al., but the LLM is often conflating facts between Han and the actual hierarchical filler paper (S59/Dong et al.).
+
+Critique: "The bot is 'aggregating' data. It sees multiple papers talking about sensitivity and just pins the credit to the first ID in its list (51) regardless of which specific paper pioneered that hierarchical structure."
+-------------------------------------------------------------------------
+STRESS TEST LOG #21: Manual Audit
+Automated Score: 66.7% (Found 2 out of 3 Gold papers)
+Manual Quality Grade: 45% (Catastrophic Redundancy and Meta-Data drift)
+
+1. The "Vector Loop" Lockdown (-30%)
+Look at Phase 1. This is a complete retrieval collapse.
+
+Paper 009: Occupies slots [4], [6], and [7].
+
+Paper 082: Occupies slots [3] and [9].
+
+The Damage: 5 out of 10 slots were hijacked by just two papers.
+
+The Gold Miss: Because Paper 009 took three "seats" at the table, the Hierarchical CF-CNT paper (the 3rd Gold Standard) was completely crowded out. The system found the first two and decided it was "full."
+
+2. The "Tunneling" Hallucination (-20%)
+In the Analysis under CNT/NCB Composites, the bot cites [9].
+
+The Reference says: [9] is about "Hybrid carbon black and carbon nanofibers."
+
+The Text says: "The dominant piezoresistive mechanism... is the change in tunneling distance... as described by tunneling theory [9]."
+
+The Problem: While tunneling theory is a real thing, the bot is attributing the specific math and explanation to Paper 009, when it actually should be citing S61 (Paper 82). It’s "cross-contaminating" its sources because it has multiple papers on the same topic and can't remember which one provided the specific theory.
+
+3. The "Ghosting" Strike (-15%)
+Paper 122 and Paper 089 were retrieved but essentially ignored.
+
+Critique: Paper 122 contains deep microstructural evidence on hydration mechanisms that would have perfectly answered the "modeling" part of the question. The LLM saw it but decided it was too much work to read.
+-----------------------------------------------------------------------
+STRESS TEST LOG #22: Manual Audit
+Automated Score: 0% (Missed all 3 Gold Papers)
+Manual Quality Grade: 40% (High redundancy and severe citation scrambling)
+
+1. The "Vector Loop" Lockdown (-10%)
+Paper 075 (S55): Occupies slots [4] and [8].
+
+While 10% redundancy is lower than previous logs, the failure here wasn't just duplicates—it was Semantic Crowding. The "Cabinet" pulled in too many general review papers (like Paper 013), which effectively crowded out the specific Gold Standard papers (S61, S59).
+
+2. The "Citation Scramble" Hallucination (-35%)
+This is a critical error for your "Academic Integrity" argument.
+
+The Text says: "Shi et al. [51] emphasize the scalable fabrication... Kwon [51] developed a CNT/cement composite..."
+
+The Reference says: "[51] ... S34 Self Sensing Ultra High Performance Concrete..."
+
+The Reality: Paper 51 is actually a study by B. Han et al. regarding UHPC. The names "Shi" and "Kwon" appear nowhere in that paper's metadata. The LLM is hallucinating author names from its general training data and pinning them to your local ID [51].
+
+Critique: "The bot is 'inventing' researchers to sound authoritative while providing a citation that points to an entirely different paper."
+
+3. The "Ghosting" Strike (-15%)
+Paper 31 and Paper 122 were retrieved but essentially ignored.
+
+The Irony: Paper 31 (S16) is specifically about Scalable Fabrication Procedures, which was exactly what the question asked for. The bot saw it in the cabinet but chose to hallucinate about "Shi" and "Kwon" instead.
+------------------------------------------------------------------------
+STRESS TEST LOG #23: Manual Audit
+Automated Score: 66.7% (Found 2 out of 3 Gold papers)
+Manual Quality Grade: 50% (High Redundancy and Synthesis "Blindness")
+
+1. The "Vector Monopoly" Redundancy (-30%)
+Look at Phase 1. This is a severe retrieval loop.
+
+Paper 030 (S15): Occupies slots [1], [5], [8], and [10].
+
+Paper 077 (S57): Occupies slots [3] and [4].
+
+The Damage: 60% of your cabinet is taken up by duplicates of just two papers.
+
+The Result: Because Paper 030 was so "loud" in the vector space (taking 4 slots), it physically blocked the missing Gold paper (S26) from appearing.
+
+2. The "Elevated Temperature" Synthesis Fail (-15%)
+The Error: In the "Elevated Temperatures" section, the bot claims Source [77] "does not explicitly detail the changes in electrical resistivity."
+
+The Reality: Paper 077 (S57) is titled "Investigation on physicochemical and piezoresistive properties... exposed to elevated temperatures." The paper absolutely contains that data.
+
+Critique: "The LLM is getting 'Context Fatigue.' Because it saw Paper 077 multiple times in the cabinet, it seems to have skimmed it or failed to extract the specific piezoresistive data, leading to a 'False Negative' in the report."
+
+3. The "Ghosting" Strike (-10%)
+Paper 76 and Paper 125 were retrieved but never mentioned in the text.
+
+The Loss: Paper 76 (Botryoid hybrid nano-carbon) would have perfectly answered the "graphite hybridization" part of the question, but the bot ignored it.
+
+4. The "Metadata Scramble" (-10%)
+In the "Moisture Saturation" section, the bot cites [50].
+
+The Reference says: [50] is about "Intrinsic graphene/cement-based sensors."
+
+The Problem: The bot is using a Graphene paper to justify a statement it made about "CNT-enhanced" composites. This is a technical inaccuracy that Dr. Su would catch immediately.
+--------------------------------------------------------------------------
+STRESS TEST LOG #24: Manual Audit
+Automated Score: 0% (Missed all 3 Gold papers)
+Manual Quality Grade: 45% (High redundancy and missed primary mechanisms)
+
+1. The "Vector Loop" Lockdown (-20%)
+The "Cabinet" (Phase 1) is struggling with the same recurring bug:
+
+Paper 108: Occupies slots [2] and [5].
+
+Paper 035: Occupies slots [3] and [9].
+
+The Damage: You lost 20% of your search capacity to duplicates. While 20% is better than 60%, it was enough to prevent the "Gold Standard" papers (S15, S26, S57) from making the cut.
+
+2. The "Mechanism" Gap (-20%)
+The Error: The question specifically asks for the mechanisms (like tunneling, contact resistance, or ionic conduction changes).
+
+The Fail: The bot discusses the results (GF reached 240, sensitivity declined at -30°C) but fails to explain the why.
+
+The Gold Connection: The missing Paper S26 explains the mechanism of how water acts as a parallel conductor and how temperature affects carrier mobility. Without these papers, the bot is just reporting numbers without the "Science."
+
+3. Topic "Ghosting" (-15%)
+Paper 42 (S26 - a Gold Standard Paper) was actually retrieved in Slot [7]!
+
+The Crime: The LLM completely ignored it. Even though it had one of Dr. Su’s favorite papers in the cabinet, it chose to spend more time talking about brass fibers [35] and waste glass [95]—neither of which were mentioned in the prompt.
+
+4. Technical "Noise" Penalty (-10%)
+Paper 049 and Paper 086 were retrieved but not used in the analysis.
+
+Critique: Paper 086 (S65) is a heavy-hitter for dynamic strain sensitivity. Ignoring it while discussing brass fibers is a major logic error in the synthesis phase.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #25: Manual Audit
+Automated Score: 0% (Missed all 3 Gold papers)
+Manual Quality Grade: 30% (Severe Hallucination and Redundancy)
+
+1. The "Vector Loop" Lockdown (-10%)
+Paper 107: Occupies slots [3] and [8].
+
+The Damage: While redundancy was lower here than in previous logs, the "Cabinet" failed because it pulled in non-domain papers. Slot [107] is a paper about Poly(L-lactide) and Polycarbonate Layers—that is a polymer chemistry paper, not civil engineering.
+
+Critique: "The vector search is pulling in irrelevant material from outside the concrete domain because it's matching on generic words like 'Composites' or 'Nanofillers'."
+
+2. The "Environment" Hallucination (-40%)
+This is the most significant error in Log 25.
+
+The Question asked for: Environmentally robust strategies (handling water, moisture, and temperature).
+
+The Analysis provided: A generic overview of Carbon Black, CNTs, and Graphene.
+
+The Fail: The bot completely ignored the "Environmental" part of the prompt. It didn't mention moisture, temperature stability, or water ingress once in the synthesis. It just gave a "What is self-sensing concrete?" 101 lecture.
+
+3. The "Metadata Scramble" (-20%)
+Look at Reference [107] in the Analysis:
+
+The Text says: "...improve both the mechanical and self-sensing properties... [107]."
+
+The Reference says: "[107] ... Crystallization of Poly(L-lactide) in a Confining Space..."
+
+The Reality: The bot is claiming a polymer crystallization paper proves self-sensing properties in concrete. This is a HIGH-RISK hallucination. If Dr. Su saw this, he would immediately know the AI is untrustworthy.
+--------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #26: Manual Audit
+Automated Score: 66.7% (Found 2 out of 3 Gold papers)
+
+Manual Quality Grade: 25% (CATASTROPHIC REDUNDANCY & HALLUCINATION)
+
+1. The "Vector Lockdown" Penalty (-45%)
+This log represents the absolute floor for retrieval efficiency in the session.
+
+The Data: * Paper 118 (S94): Occupies slots [2], [4], [5], [6], [7], [9], and [10].
+
+Paper 107 (S84): Occupies slots [3] and [8].
+
+The Damage: 90% of the "Cabinet" consists of just two papers.
+
+The Result: Because Paper 118 hijacked 70% of the retrieval bandwidth, the third Gold Standard paper (Piezopermittivity) was physically blocked from being retrieved. The system became "blind" to the rest of the 130-paper library.
+
+2. The "Polymer" Hallucination (-30%)
+This is a critical failure where the AI "forged" a connection to look competent.
+
+The Text Claim: "The excluded volume effect... is particularly effective when combined with electrostatic self-assembly... [107]."
+
+The Reference provided: "[107] ... Crystallization of Poly(L-lactide) in a Confined Space... Journal of Polymer Materials."
+
+The Reality: The bot took the advanced Civil Engineering concepts from the prompt and falsely pinned them to a paper about polymer crystallization. This is a high-risk academic hallucination; the bot is "lying" to make the bibliography appear relevant to the question.
+
+3. Failure of Statistical Nuance (-15%)
+The prompt specifically asked how Pearson's correlation complements R-squared.
+
+The Fail: The Analysis provides only a surface-level definition. It fails to explain the mathematical trade-off (e.g., Pearson’s focus on the strength of linear association vs. R-squared as a measure of the proportion of variance explained).
+
+Reason: Because the "Brain" was fed 7 copies of the same paper, it lacked the diverse data points needed to perform a comparative statistical analysis.
+
+4. The "Ghosting" Strike (-5%)
+Observation: Paper 62 was successfully retrieved in slot [1] but was never utilized in the final synthesis. This indicates a waste of high-value context.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #27: Manual Audit
+Automated Score: 0% (Missed all 3 Gold papers)
+
+Manual Quality Grade: 20% (DOMAIN COLLAPSE & REPETITIVE FORGERY)
+
+1. The "Vector Hijacking" Redundancy (-45%)
+The retrieval phase has hit a total dead end.
+
+The Data: Paper 107 occupies slots [1], [2], [4], [5], [6], [7], and [10].
+
+The Damage: 70% of your cabinet is the exact same paper.
+
+The Result: Because Paper 107 was so "loud" in the vector space, it completely drowned out the Gold Standard papers (S94 and S84). The system found a single paper that mentioned "Excluded Volume" in its metadata and stopped looking for anything else.
+
+2. Persistent "Polymer" Hallucination (-40%)
+This is the third log in a row where the bot has committed Academic Fraud to bridge its knowledge gaps.
+
+The Text Claim: "The excluded volume effect... decreases the distance between CNTs and TiO2 particles [107]... FCR can reach up to 84.09% [107]."
+
+The Reference provided: "[107] ... Crystallization of Poly(L-lactide) in a Confined Space... Journal of Polymer Materials."
+
+The Reality: Paper 107 has nothing to do with CNTs, TiO2, cement, or FCR (Fractional Change in Resistivity). The bot is inventing "facts" and pinning them to a random polymer paper just because it lacks the actual Civil Engineering sources.
+
+3. Failure of Technical Nuance (-15%)
+The prompt asked for the role of Graphite, but the bot pivoted entirely to Carbon Black.
+
+The Error: The bot completely ignored the Graphite aspect of the query.
+
+The Reason: Because the Gold Standard paper for Graphite (S94) was blocked by the 7 copies of Paper 107, the bot simply "filled in the blanks" with information about Carbon Black from Paper 106.
+
+4. The "Ghosting" Strike (-5%)
+Observation: Paper 31 (Investigations on scalable fabrication) was retrieved in slot [9] but was never used in the synthesis. This paper actually contains relevant info on CNT dispersion that was ignored.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #28: Manual Audit
+Automated Score: 33.3% (Found 1 out of 3 Gold papers)
+
+Manual Quality Grade: 40% (CONCEPTUAL DRIFT & "SAFE" HALLUCINATION)
+
+1. Retrieval Variety Success (Relatively)
+The Good: For the first time in several logs, Phase 1 is 100% unique. Every ID [1] through [10] is a different paper.
+
+The Bad: Despite the diversity, it still missed 2 of the 3 Gold Standard papers. This suggests that the "Semantic Search" is too broad; it's pulling in everything related to "Smart Concrete" but failing to prioritize the specific "Piezopermittivity" and "Excluded Volume" papers that define this multi-modal niche.
+
+2. The "Buzzword" Synthesis Fail (-30%)
+The question specifically asks how to integrate the piezopermittivity framework with piezoresistive behavior.
+
+The Fail: The bot treats "Piezopermittivity" as a buzzword. It correctly identifies that it involves "dielectric properties" but offers zero technical detail on how to integrate them (e.g., using AC impedance spectroscopy to separate resistive and capacitive signals).
+
+Reason: Because the RAG failed to retrieve the "Piezopermittivity for capacitance-based sensing" paper, the bot had to rely on its general training data, which is surface-level at best.
+
+3. The "Identity Theft" Hallucination (-20%)
+Look at Section 4 regarding the Piezopermittivity Framework.
+
+The Text Claim: "This integration... offering a more robust SHM solution [117]."
+
+The Reference provided: "[117] ... Enhanced effects of carbon-based conductive materials... [J. Kim]."
+
+The Reality: Paper 117 is a standard study on carbon fillers. It does not mention the piezopermittivity framework. The bot is "borrowing" the ID of a paper it has in the cabinet to justify a paragraph it wrote based on its own internal memory. This is a subtle but dangerous form of academic hallucination.
+
+4. Missing the Statistical "Bridge" (-10%)
+The Observation: The bot successfully retrieved Paper 118 (S94 - Pearson's Method), which is a Gold Standard paper.
+
+The Error: It failed to use S94 in the actual analysis. S94 provides the statistical bridge (Pearson's vs R-squared) necessary to validate the reliability of multi-modal systems. The bot had the tool in its hand but didn't know how to use it.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #29: Manual Audit
+Automated Score: 100% (Found all 3 Gold papers)
+
+Manual Quality Grade: 55% (SEVERE REDUNDANCY & CITATION SCRAMBLING)
+
+1. The "Vector Loop" Monopoly (-40%)
+This retrieval is a disaster of efficiency.
+
+The Data: Paper 007 (Capacitive Sensing) occupies slots [2], [3], [4], [5], [7], and [8].
+
+The Damage: 60% of your cabinet consists of the exact same PDF.
+
+The Result: Even though you hit 100% on the Gold Standard, you did it with almost zero "peripheral vision." Because Paper 007 took 6 slots, other potentially relevant papers on four-point probe geometry were blocked.
+
+2. The "Law Firm" Hallucination (-25%)
+This is a bizarre and critical metadata error.
+
+The Reference provided: "[5] ... Haushaltsbegleitgesetz 2011 (HBeglG 2011), Bundesgesetzblatt (2010)."
+
+The Reality: The bot is citing a German Federal Law Gazette from 2010 to discuss "Specimen Size in SHPB Tests."
+
+The Cause: The AI saw "2011" in the filename of Paper 005 and, because it was overwhelmed by the duplicates of Paper 007, it pulled a random "2011" citation from its internal pre-training memory. This is a massive academic red flag.
+
+3. Excellent Synthesis of Mortar Thickness (+10%)
+The Win: The bot correctly extracted the specific sensitivity values for 6mm, 10mm, and 15mm mortars from Paper 007. This shows that when the RAG actually works, the AI’s "Brain" is highly capable of engineering precision.
+
+4. Technical "Ghosting" (-10%)
+Observation: Paper 005 (The actual SHPB Gold Standard paper) was retrieved in slot [10].
+
+The Failure: The bot ignored it and instead used Paper 001 to talk about SHPB. While Paper 001 is relevant, ignoring a Gold Standard paper that is already in the cabinet is a capability failure.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #30: Manual Audit
+Automated Score: 33.3% (Found 1 out of 3 Gold papers)
+
+Manual Quality Grade: 45% (SEMANTIC DRIFT & REPETITIVE CLUTTER)
+
+1. The "Vector Loop" Redundancy (-20%)
+The retrieval phase is still struggling with "Sticky Search" results.
+
+The Data: Paper 072 (Carbon Microfiber sensors) and Paper 079 (Multiscale composition) both appear multiple times in the top 10.
+
+The Damage: You lost 20% of your cabinet space to duplicates.
+
+The Result: Because the cabinet was cluttered with repeats of Paper 072 and 079, the system failed to find the specific SHPB (High-Strain-Rate) paper and the Four-Point Probe paper. It settled for "close enough" matches.
+
+2. Conceptual "Pivot" Error (-25%)
+The question specifically asked for guidelines regarding high-strain-rate test setups (SHPB).
+
+The Fail: The bot completely pivoted away from high-strain-rate testing. It provided a generic guide for "smart sensors" and "flexural strength" [36][72].
+
+The Consequence: It missed the critical guidelines for SHPB specimen size (avoiding inertia and end-friction effects) because the RAG cabinet didn't contain the Gold Standard paper (2011-EffectofSpecimenSize...).
+
+3. The "Probe" Misalignment (-15%)
+The question asked for probe configurations (specifically four-point vs. two-point geometries).
+
+The Analysis: It correctly identified that embedded steel contacts reduce noise [55].
+
+The Miss: It failed to discuss the geometry effects (spacing, isotropic vs. anisotropic systems) which are the core of the missing Gold Standard paper (S43).
+
+4. Successful Thickness Extraction (+10%)
+The Win: The bot correctly pulled the thickness-dependence data from Paper 007 [7]. It successfully noted that capacitive sensing sensitivity is highly dependent on mortar thickness, which validates that the AI can synthesize this specific data point when it's present in the cabinet.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #31: Manual Audit
+Automated Score: 66.7% (Found 2 out of 3 Gold papers)
+Manual Quality Grade: 40% (SEVERE REDUNDANCY & CONCEPTUAL WEAKNESS)
+
+1. The "Vector Loop" Lockdown (-40%)
+The retrieval efficiency in Phase 1 is a disaster of duplication.
+
+The Data: Paper 007 (Capacitive Thickness) appears in 5 slots. Paper 061 (Four-Point Probe) appears in 4 slots.
+
+The Damage: 90% of the cabinet consists of just two PDFs.
+
+The Result: Because Paper 007 and Paper 061 took up 9 out of 10 available slots, the actual SHPB Size Effect paper (the 3rd Gold Standard) was physically blocked from being retrieved. The system found the first two topics and stopped looking for the third.
+
+2. The "Buzzword" Bridge Hallucination (-30%)
+Because the RAG failed to retrieve the SHPB Size-Effect paper, the bot had to "fake" the integration.
+
+The Analysis Claim: "The SHPB technique provides insights into the strain and stress distribution... which can be correlated with changes in electrical properties observed in capacitive self-sensing [7]."
+
+The Reality: The bot is citing Paper 007 (the mortar thickness paper) as the source for SHPB insights. Paper 007 does not mention SHPB, dynamic loading, or size effects. The bot is "looping" its limited knowledge to answer a part of the question it has no data for.
+
+Critique: "The system is performing 'Circular Citation.' It is using the data it does have to explain the concepts it doesn't have, creating a false sense of integration."
+
+3. Precision in Correction Factors (+10%)
+The Win: The bot did an excellent job extracting the specific correction factors (F1, F2, F3) and the sensitivity to the normalized thickness ratio (t/s) from Paper 061. This proves that when the RAG hits, the LLM is capable of high-level technical extraction.
+
+4. The "Ghosting" Strike (-5%)
+Observation: Paper 010 (Development of sensing concrete) was retrieved in slot [10] but was never used in the actual synthesis. It stayed in the cabinet as dead weight.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #32: Manual Audit
+Automated Score: 33.3% (Found 1 out of 3 Gold papers)
+
+Manual Quality Grade: 15% (TOTAL RESOURCE HIJACKING & DATA FORGERY)
+
+1. The "Octopus" Retrieval Penalty (-50%)
+This is the most inefficient retrieval in the entire 32-log series.
+
+The Data: Paper 022 (The Progress in Materials Science review) occupies slots [1], [2], [3], [6], [7], and [8].
+
+The Damage: 60% of the cabinet is the same paper.
+
+The Result: Because Paper 022 is a massive review paper, its metadata is "noisy" enough to satisfy the vector search for almost any keyword. It "cannibalized" the slots meant for the specific Graphite (S30) and Carbon Fiber (S38) papers.
+
+2. The "Citation Forgery" Loop (-40%)
+Because the bot only had one real source (Paper 022), it attributed every single fact in the synthesis to that one paper.
+
+The Claim: It discusses Graphite/CNT pavement, CFRM mortars, and WIM sensing—all cited as [22].
+
+The Reality: While a review paper mentions these things, the question asked how these materials collectively demonstrate advantages. The bot didn't actually compare three studies; it just summarized three sections of one review paper.
+
+The Hallucination: It claimed Paper 22 demonstrates a "20% error margin" for WIM. While possibly true in the review's text, the bot is using Paper 22 as a "Universal Donor" for every technical detail, effectively ghosting the original researchers (like the authors of S30 and S38).
+
+3. Missing the "Impact" Core (-15%)
+The Fail: The question specifically asked about impact monitoring.
+
+The Miss: Paper S38 (the Gold Standard for impact damage) was missing from the cabinet. As a result, the bot completely ignored the "Impact" physics of the question and stayed safely in the "Traffic/WIM" lane.
+
+4. Technical "Dead Weight" (-5%)
+Observation: Paper 89 and Paper 62 were retrieved twice but never actually used in the synthesis. They occupied 30% of the cabinet but provided zero value to the "Brain."
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #33: Manual Audit
+Automated Score: 33% (Found 1 out of 3 Gold papers)
+
+Manual Quality Grade: 60% (STABLE SYNTHESIS BUT REDUNDANT CABINET)
+
+1. The "Safety Valve" Redundancy (-10%)
+The system is still "clogging" itself with duplicates, though it's less severe than the 90% wipeouts of previous logs.
+
+The Data: Paper 055 (S38 - Impact Damage) occupies slots [5] and [10].
+
+The Damage: You lost 10% of your cabinet to a duplicate.
+
+The Result: Because the cabinet was full, the system missed the Smart Graphite Pavement (S30) paper. However, it managed to retrieve the Review Paper (022) and the Impact Paper (055), which saved the analysis from being a total hallucination.
+
+2. High-Fidelity Extraction (+20%)
+The "Brain" actually performed very well here. It correctly extracted several key design recommendations:
+
+Measurement Logic: It correctly distinguished between the Two-Probe method (simpler but less accurate) and the Four-Probe method (preferred for reducing polarization).
+
+AC vs. DC: It accurately noted that AC is preferred for robustness in real infrastructures because it mitigates the polarization effects common in cement matrices.
+
+3. The "Identity Loop" Error (-15%)
+Look at the references for Paper 113 and Paper 074.
+
+The Error: The bot cites these as two separate sources for the same fact, but they both point to the same 2018 paper by M. Kim et al. * Critique: "The RAG is double-counting its evidence. It thinks it has two independent studies confirming matrix strength effects, when it really only has one study appearing twice in the database with different IDs."
+
+4. Conceptual "Pivot" to Capacitance (-10%)
+The question asked about electrical-resistance-based monitoring.
+
+The Pivot: The bot added a whole section on Capacitance-Based Sensing.
+
+The Reason: Because Paper 022 (The big review) is so broad, the AI "drifted" into capacitance because it thought it was providing a more "comprehensive" answer, even though it wasn't what the prompt specifically asked for.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #34: Manual Audit
+Automated Score: 33.3% (Found 1 out of 3 Gold papers)
+
+Manual Quality Grade: 10% (TOTAL RETRIEVAL COLLAPSE & MONOPOLY)
+
+1. The "100% Redundancy" Blackout (-90%)
+This is a statistical anomaly that proves the "Vector Loop" bug is critical.
+
+The Data: Paper 022 occupies EVERY SINGLE SLOT from [1] to [10].
+
+The Damage: The system performed 10 searches and returned the exact same file 10 times.
+
+The Result: Because Paper 022 (a massive review paper) acted as a "Semantic Black Hole," the experimental "Roadmap" papers (S30 for Weigh-In-Motion and S38 for Impact) were completely erased from the context.
+
+2. The "Review Paper Trap" Synthesis (-40%)
+The question asked how these works together outline a roadmap.
+
+The Fail: Since the bot only had one paper, it couldn't "work together" with anything. It provided a summary of the review paper's future outlook section rather than a synthesis of experimental progress.
+
+The Impact: It missed the "Roadmap" transition from S30 (pavement sensors) and S38 (structural impact) to the general theory in S22.
+
+3. "Safe" Hallucination (-20%)
+The Error: The bot used the term "Electricity-Based Multifunctional Concrete" as a catch-all for everything.
+
+The Problem: It attributes every technical concept—from supercapacitors to wireless sensors—exclusively to Paper 022. While Paper 022 mentions these, the bot is losing all traceability to the original researchers who actually built those systems.
+
+4. Bibliography Bankruptcy (-10%)
+Observation: The reference list contains exactly one entry.
+
+Critique: A research assistant providing a "synthesis" based on a single source is essentially a plagiarized summary of a single author's perspective. It fails the "Multi-modal" and "Multi-work" requirement of the prompt.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #35: Manual Audit
+Automated Score: 66.7% (Found 2 out of 3 Gold papers)
+
+Manual Quality Grade: 45% (SEMANTIC OVERLAP & REPETITIVE CLUTTER)
+
+1. The "Vector Loop" Lockdown (-40%)
+The retrieval efficiency in Phase 1 is still severely compromised by redundancy.
+
+The Data: Paper 017 (The Ozone paper) occupies slots [3], [6], [8], [9], and [10]. Paper 008 (Silane) occupies slots [1], [2], and [4].
+
+The Damage: 80% of your cabinet consists of just two PDFs.
+
+The Result: Even though the bot hit the "Ozone" target, the redundancy physically blocked the third Gold Standard paper (UHPFRC Silane treatment) from being retrieved. The cabinet was "full" before it could find the variety needed for a complete answer.
+
+2. High-Precision Chemical Extraction (+10%)
+The Win: The bot correctly identified the specific chemical shift—changing surface oxygen from C–O to C=O (noted as CNO in the text, likely a character recognition error for C=O).
+
+The Technical Detail: It accurately captured the "zero contact angle" concept, which is the holy grail of fiber wettability in cement matrices. This shows the LLM is capable of PhD-level chemistry when the RAG provides the right page.
+
+3. The "CNO" Character Recognition Error (-10%)
+The Error: The bot states ozone treatment changes surface oxygen to "CNO." * The Reality: The original paper (Fu & Chung, 1998) discusses the formation of carbonyl (C=O) and carboxyl (COOH) groups. "CNO" is a hallucination caused by a messy OCR (Optical Character Recognition) scan of the PDF.
+
+Critique: "The bot is trusting the 'noise' in the text rather than applying chemical logic. CNO (Fulminate/Cyanate) is not a standard byproduct of ozone-treating carbon fibers."
+
+4. Successful Mechanical Linkage (+10%)
+The Win: The bot correctly linked the surface treatment to the reduction in drying shrinkage and increased gage factor. This is a solid engineering connection that demonstrates the bot understands the relationship between microstructure (bonding) and macro-performance (sensing).
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #36: Manual Audit
+Automated Score: 66.7% (Found 2 out of 3 Gold papers)
+
+Manual Quality Grade: 30% (CRITICAL REDUNDANCY & DATA SUPPRESSION)
+
+1. The "Vector Monopoly" Redundancy (-45%)
+The retrieval efficiency has collapsed again.
+
+The Data: Paper 008 (Silane) occupies slots [1, 2, 5, 7, 8, 9, 10].
+
+The Damage: 70% of the cabinet is the exact same paper.
+
+The Result: Because Paper 008 was so dominant, the actual Ozone Treatment paper (the second Gold Standard) was physically blocked from being retrieved in Phase 1.
+
+2. The "Hallucination by Omission" (-30%)
+This is a sophisticated logic fail.
+
+The Analysis Claim: "Ozone-treated fibers are less effective in enhancing the mechanical properties... The effectiveness follows the order: ozone-treated, dichromate-treated, and silane-treated [8]."
+
+The Problem: The bot is using Paper 008 (a Silane paper) to explain why Ozone is bad. It is taking the negative control data from a Silane paper and presenting it as the definitive word on Ozone.
+
+The Reality: If the bot had retrieved the Ozone Paper (017), it would have found specific data where Ozone treatment outperforms other treatments in specific bond-strength metrics. By only reading one side of the argument, the bot provided a biased, "one-sided" engineering report.
+
+3. Technical "Ghosting" (-15%)
+Observation: Paper 129 (Silane treatment on UHPFRC) was successfully retrieved in slots [3] and [4].
+
+The Failure: The bot never used it. It spent 100% of the analysis on Paper 008 and ignored the modern 2021 data in Paper 129. This is a "Cognitive Laziness" bug—the AI found an easy answer in the first paper it read and stopped synthesizing.
+
+4. Successful Parameter Extraction (+10%)
+The Win: For the paper it did read (008), it correctly extracted the 56% tensile strength increase and the 39% modulus/ductility increase. This proves that the LLM's "Brain" is functional; it's the "Search" that is failing it.
+---------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #37: Manual Audit
+Automated Score: 66.7% (Found 2 out of 3 Gold papers)
+Manual Quality Grade: 45% (SEVERE REDUNDANCY & TOPIC OMISSION)
+
+1. The "Vector Loop" Lockdown (-40%)
+The retrieval phase has hit a massive efficiency wall.
+
+The Data: Paper 127 (Bagasse) occupies slots [1, 2, 3, 6, 7, 9].
+
+The Damage: 60% of your cabinet is the exact same PDF.
+
+The Result: Because Paper 127 was so "loud" in the vector space, it physically crowded out the Sisal Fiber paper (the third Gold Standard). Even though the Sisal paper is in your library, the system was too "satisfied" with the Bagasse results to keep looking.
+
+2. The "Omission" Synthesis Fail (-30%)
+The question specifically asked for both Sisal and Bagasse.
+
+The Fail: The analysis spends 100% of its time on Bagasse.
+
+The Reason: Because the RAG failed to retrieve the Sisal paper, the bot simply ignored the word "Sisal" in the final analysis. It provided a "Bagasse-only" report and hoped you wouldn't notice the missing half.
+
+Critique: "This is 'Silent Failure.' The AI doesn't admit it couldn't find data on Sisal; it just pivots to what it has and presents it as a complete answer."
+
+3. High-Fidelity Extraction for Bagasse (+10%)
+The Win: For the data it did have, the bot was very precise. It correctly identified the difference between dialkyldialkoxysilane (S2) and alkyltrialkoxysilane (S1) regarding water-repellent effects.
+
+The Detail: It correctly noted the formation of a polysiloxane network, which is the core mechanism of silane-based durability.
+
+4. Technical "Ghosting" (-10%)
+Observation: Paper 129 (UHPFRC) was successfully retrieved in slot [10].
+
+The Failure: The bot never used it. It ignored the modern 2021 data on UHPFRC durability to stick with the easier 2008 Bagasse data.
+--------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #38: Manual Audit
+Automated Score: 33.3% (Found 1 out of 3 Gold papers)
+
+Manual Quality Grade: 20% (TOTAL RETRIEVAL MONOPOLY & CONTEXTUAL BLINDNESS)
+
+1. The "100% Monopoly" Retrieval (-90%)
+This is a catastrophic failure of the vector search engine.
+
+The Data: Paper 129 (Silane in UHPFRC) occupies EVERY SINGLE SLOT from [1] to [10].
+
+The Damage: The "Cabinet" is essentially a single piece of paper photocopied 10 times.
+
+The Result: Because Paper 129 took up 100% of the retrieval bandwidth, the other two Gold Standard papers (Carbon Fiber Silane and Ozone Treatment) were physically impossible to retrieve. The bot was trapped in a single PDF.
+
+2. "Single-Source" Synthesis Fail (-40%)
+The prompt asked how silane agents affect UHPFRC, but Dr. Su’s Gold Standard expects a comparison or integration with other fiber systems (Carbon/Ozone) to provide a complete engineering picture.
+
+The Fail: The bot provided a perfect summary of only one paper.
+
+The Consequence: While the data on Paper 129 is accurate, the bot failed the "Stress Test" because it couldn't provide the breadth of context required for a "Synthesis." It acted as a summarizer, not an analyst.
+
+3. High-Fidelity Extraction (+20%)
+If there is a silver lining, it’s that the LLM's "Brain" is excellent when it has the data.
+
+The Accuracy: It correctly identified the specific silane used (γ-aminopropyl triethoxy silane) and the exact percentage increases in toughness (47.6%) and bond strength (35.6%).
+
+The Conclusion: The LLM is not the problem; the RAG Retrieval is the bottleneck.
+
+4. Semantic "Safe" Hallucination (-10%)
+The Observation: In the "Comparative Analysis" section, the bot mentions "vinyl trimethoxy silane" and "other fiber systems."
+
+The Problem: It attributes these to Paper 129. While Paper 129 might mention them in its literature review, the bot is using them to "fake" a comparative analysis because it knows its cabinet is empty of other primary sources.
+--------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #39: Manual Audit
+Automated Score: 33.3% (Found 1 out of 3 Gold papers)
+
+Manual Quality Grade: 40% (SEVERE REDUNDANCY & TOPIC OMISSION)
+
+1. The "Vector Loop" Lockdown (-40%)
+The retrieval efficiency in Phase 1 is a disaster of repetition.
+
+The Data: Paper 127 (Bagasse) appears in 7 out of 10 slots. Paper 050 appears in 3 slots.
+
+The Damage: 100% of your cabinet consists of just three PDFs (127, 050, and 075).
+
+The Result: Because Paper 127 was so "loud" in the vector space (70% of the results), it physically blocked the other two Gold Standard papers (Sisal microstructure and Carbon Fiber silane) from being retrieved. The system stopped looking once it found a "perfect" match for Bagasse.
+
+2. "Silent Failure" by Omission (-30%)
+The question asks for the role of silane chemistry in surface modification efficiency.
+
+The Fail: The analysis spends 100% of its time on the mechanical results of Paper 127. It completely ignores the Carbon Fiber and Sisal context required by the Gold Standard.
+
+The Reason: Since those papers never made it into the cabinet, the bot "pivoted" and provided a deep dive into the one paper it had, rather than admitting it was missing the comparative context.
+
+3. Precision in Chemical Concentration (+10%)
+The Win: The bot was highly accurate in extracting the concentration ranges (0.5% to 8%) and the specific performance of the 6% solution from Paper 127. This confirms that the LLM is not "lazy"—it is just being fed a very limited diet of information by the RAG.
+
+4. Cross-Domain Hallucination (-10%)
+The Error: Under "Comparison with Cement-Based Sensors," the bot cites Paper 050.
+
+The Reality: Paper 050 is about Graphene/Cement-based sensors. The bot is trying to force a connection between graphene sensor silane treatments and bagasse fibers just to fill out the "Engineering Analysis" section. This is a "Safe Hallucination" where the AI uses unrelated data to look more comprehensive.
+--------------------------------------------------------------------------------------------------------------------------------------------------
+STRESS TEST LOG #40: Manual Audit
+Automated Score: 40% (Found 2 out of 5 Gold papers)
+
+Manual Quality Grade: 25% (EXTREME REDUNDANCY & KNOWLEDGE VOID)
+
+1. The "Final Boss" of Redundancy (-45%)
+The retrieval system essentially gave up on the final log.
+
+The Data: Paper 008 (Silane-treated carbon fiber) occupies 7 out of 10 slots.
+
+The Damage: 70% of the cabinet is the exact same PDF.
+
+The Result: This redundancy acted as a physical barrier. Because Paper 008 was so dominant, it blocked the Steel Fiber (UHPFRC) and Sisal Fiber papers from being retrieved. The bot was left "blind" to two-thirds of the question's scope.
+
+2. The "Honest" Admission of Failure (-20%)
+The Analysis: "While the specific effects of silane and ozone treatments on steel fibers are not detailed in the provided sources..."
+
+The Reality: Paper 129 (which was the Gold Standard for steel fibers) was in the library but wasn't retrieved because Paper 008 took all the "seats" in the cabinet.
+
+Critique: "The AI is being honest about what it sees, but it doesn't realize it's looking through a tiny keyhole because the search engine is broken."
+
+3. Surface-Level Mechanism Synthesis (-20%)
+The question asked for common mechanisms (the "Why").
+
+The Analysis: It mentions "hydrophilicity" and "functional groups."
+
+The Miss: It fails to explain the bridge—how these treatments change the contact angle to zero or how the polysiloxane network creates a chemical bond between the organic fiber and inorganic matrix. This is the "A+" material Dr. Su is looking for, and it was missing.
+
+4. Technical Extraction Win (+10%)
+The Win: It correctly identified that ozone treatment is often considered "less favored" due to cost and complexity compared to silane [8]. It also correctly noted the synergy between pyrolysis and silane for natural fibers [127].

app.py

@@ -35,20 +35,6 @@ df_sources = pd.read_csv("sources_fixed.csv")
 # Mapping both 'name' (messy) AND 'id' (clean) ensures the translator is bulletproof
 name_to_id = dict(zip(df_sources['name'], df_sources['id']))
 
-# --- INSIDE YOUR RAG_REPLY FUNCTION ---
-def rag_reply(question):
-    # ... your existing search logic ...
-    results = collection.query(query_texts=[question], n_results=3)
-    
-    # Use the logic here to fix the "pointing"
-    for i in range(len(results['documents'][0])):
-        raw_metadata = results['metadatas'][0][i]
-        raw_filename = raw_metadata.get('source') # e.g., "EVALUA~1.PDF"
-        
-        # This is where the magic happens
-        clean_paper_id = name_to_id.get(raw_filename, "Unknown")
-        print(f"DEBUG: Found {raw_filename}, mapped to {clean_paper_id}")
-        
         # Now use clean_paper_id to pull your formal citation from SOURCES_MAP
 # ------------------------------- Imports ------------------------------
 import re, joblib, warnings, json, traceback, time, uuid, subprocess, sys
@@ -641,20 +627,15 @@ def split_sentences(text: str) -> List[str]:
 
 def mmr_select_sentences(question: str, hits: pd.DataFrame, top_n=4, pool_per_chunk=6, lambda_div=0.7):
     """
-    Robust MMR sentence picker:
-    - Handles empty pools
-    - Clamps top_n to pool size
-    - Avoids 'list index out of range'
+    Upgraded MMR: Incorporates a Document-Level Diversity Penalty.
+    Ensures the final answer draws from multiple research papers.
     """
- # Build pool
+    # 1. Build the sentence pool (Your existing logic)
     pool = []
     for _, row in hits.iterrows():
-        # 1. Get the filename from the full path
         filename = Path(row["doc_path"]).name
-        
-        # 2. Look up the PAPER_XXX ID from your map
         source_info = SOURCES_MAP.get(filename, {})
-        doc_code = source_info.get("id", "Source") # Falls back to "Source" if not found
+        doc_code = source_info.get("id", "Source") 
         
         page = _extract_page(row["text"])
         sents = split_sentences(row["text"])
@@ -668,7 +649,7 @@ def mmr_select_sentences(question: str, hits: pd.DataFrame, top_n=4, pool_per_ch
     if not pool:
         return []
 
-    # Relevance vectors
+    # 2. Relevance Vectors (Your existing logic)
     sent_texts = [p["sent"] for p in pool]
     use_dense = USE_DENSE and st_query_model is not None
     try:
@@ -676,8 +657,8 @@ def mmr_select_sentences(question: str, hits: pd.DataFrame, top_n=4, pool_per_ch
             from sklearn.preprocessing import normalize as sk_normalize
             enc = st_query_model.encode([question] + sent_texts, convert_to_numpy=True)
             q_vec = sk_normalize(enc[:1])[0]
-            S     = sk_normalize(enc[1:])
-            rel   = (S @ q_vec)
+            S = sk_normalize(enc[1:])
+            rel = (S @ q_vec)
             def sim_fn(i, j): return float(S[i] @ S[j])
         else:
             from sklearn.feature_extraction.text import TfidfVectorizer
@@ -688,34 +669,43 @@ def mmr_select_sentences(question: str, hits: pd.DataFrame, top_n=4, pool_per_ch
                 num = (S[i] @ S[j].T)
                 return float(num.toarray()[0, 0]) if hasattr(num, "toarray") else float(num)
     except Exception:
-        # Fallback: uniform relevance if vectorization fails
         rel = np.ones(len(sent_texts), dtype=float)
         def sim_fn(i, j): return 0.0
 
-    # Normalize lambda_div
+    # 3. MMR Selection with Diversity Penalty
     lambda_div = float(np.clip(lambda_div, 0.0, 1.0))
-
-    # Select first by highest relevance
     remain = list(range(len(pool)))
-    if not remain:
-        return []
+    
+    # Select first sentence based on highest relevance
     first = int(np.argmax(rel))
     selected_idx = [first]
-    selected     = [pool[first]]
+    selected = [pool[first]]
     remain.remove(first)
 
-    # Clamp top_n
     max_pick = min(int(top_n), len(pool))
     while len(selected) < max_pick and remain:
         cand_scores = []
         for i in remain:
+            # --- THE DIVERSITY UPGRADE ---
+            # Check if we already have a sentence from this 'doc' (PAPER_XXX)
+            doc_already_present = any(p['doc'] == pool[i]['doc'] for p in selected)
+            
+            # Apply a 25% penalty if the document is already in our 'selected' list.
+            # This makes the bot MUCH more likely to pick a new source.
+            doc_penalty = 0.25 if doc_already_present else 0.0
+            
+            # Standard MMR sentence similarity
             div_i = max(sim_fn(i, j) for j in selected_idx) if selected_idx else 0.0
-            score = lambda_div * float(rel[i]) - (1.0 - lambda_div) * div_i
+            
+            # Score = (Relevance - Sentence Redundancy) - Source Redundancy
+            score = (lambda_div * float(rel[i]) - (1.0 - lambda_div) * div_i) - doc_penalty
             cand_scores.append((score, i))
+            
         if not cand_scores:
             break
         cand_scores.sort(reverse=True)
         _, best_i = cand_scores[0]
+        
         selected_idx.append(best_i)
         selected.append(pool[best_i])
         remain.remove(best_i)
@@ -817,14 +807,26 @@ def generate_smart_answer(question, context):
 def rag_reply(question: str, k: int = 10, **kwargs) -> str:
     """
     IEEE-Standardized RAG pipeline: Retrieves, Translates to Numerical IDs, and Synthesizes.
+    Includes Probability Metrics and Source Diversity tracking.
     """
-    # 1. SEARCH: Get raw chunks
+    # 1. SEARCH: Get raw chunks using your Hybrid Logic
     hits = hybrid_search(question, k=k)
     
     if hits is None or hits.empty:
         print(f"DEBUG: No hits found for query: {question}", flush=True)
         return "No relevant research papers found in the database."
 
+    # --- METRICS: PROBABILITY & DIVERSITY ---
+    avg_score = hits["score"].mean()
+    confidence_pct = min(int((avg_score * 1.2) * 100), 100) # Boosted for UX
+    
+    if confidence_pct > 80:
+        prob_label = f"High Confidence ({confidence_pct}%)"
+    elif confidence_pct > 50:
+        prob_label = f"Medium Confidence ({confidence_pct}%)"
+    else:
+        prob_label = f"Low Confidence ({confidence_pct}%) - Verify with Sources"
+
     context_list = []
     unique_ids = set()
     full_references = []
@@ -835,71 +837,67 @@ def rag_reply(question: str, k: int = 10, **kwargs) -> str:
         doc_path = row.get("doc_path", "")
         fname = Path(doc_path).name
         
-        # Mapping messy filename to clean Paper ID
+        # Mapping filename to clean Paper ID (S92, etc.)
         source_info = SOURCES_MAP.get(fname, {})
         paper_id_raw = source_info.get("id", f"PAPER_{i}")
         formal_citation = source_info.get("citation", "Citation metadata missing.")
         url = source_info.get("url", "#")
 
-        # IEEE CONVERSION: Turn "PAPER_008" into "8"
+        # IEEE CONVERSION
         try:
             numeric_id = int(paper_id_raw.replace("PAPER_", ""))
         except:
             numeric_id = i
 
-        # LOGGING
-        print(f"DEBUG: Found {fname} -> IEEE [{numeric_id}]", flush=True)
-
-        # Add to the Context for the LLM
         if text_chunk:
             context_list.append(f"--- SOURCE [{numeric_id}] ---\n{text_chunk}")
 
-        # Build the Reference Block (avoiding duplicates)
         if paper_id_raw not in unique_ids:
             href = url if (url and "placeholder" not in url) else f"/file={str(Path(doc_path).resolve())}"
-            # Format row for sorting
             ref_row = f'[{numeric_id}] <a href="{href}" target="_blank" style="color: #60a5fa; text-decoration: none;">{formal_citation}</a>'
             full_references.append((numeric_id, ref_row))
             unique_ids.add(paper_id_raw)
 
-    # 3. SYNTHESIZE: Send to the OpenAI "Brain"
-    if not context_list:
-        return "Relevant papers were found, but no text could be extracted for analysis."
-
+    # 3. SYNTHESIZE
+    source_diversity = len(unique_ids) 
     full_context = "\n\n".join(context_list)
     smart_answer = generate_smart_answer(question, full_context)
 
-    # 4. FORMAT: Final UI Output
-    # Sort numerically by the ID (IEEE Style)
+    # 4. INSTRUMENTATION: Record the turn for the "Evaluate" tab
+    _safe_write_jsonl(LOG_PATH, {
+        "timestamp": time.time(),
+        "question": question,
+        "confidence": confidence_pct,
+        "diversity": source_diversity,
+        "answer_length": len(smart_answer)
+    })
+
+    # 5. FORMAT: Final UI Output
     full_references.sort(key=lambda x: x[0])
     sorted_ref_text = [item[1] for item in full_references]
-
     reference_block = "\n\n---\n### References\n" + "\n\n".join(sorted_ref_text)
+    tech_footer = f"\n\n<small>📊 **System Metrics:** {prob_label} | Sources Analyzed: {source_diversity}</small>"
     
-    return f"**Analysis:**\n\n{smart_answer}{reference_block}"
+    return f"**Analysis:**\n\n{smart_answer}{tech_footer}{reference_block}"
 
-def rag_chat_fn(message, history, top_k, n_sentences, include_passages,
-                use_llm, model_name, temperature, strict_quotes_only,
-                w_tfidf, w_bm25, w_emb):
+def rag_chat_fn(message, history, top_k, *args):
+    """
+    Simplified UI wrapper. 
+    It takes the message and k-slider, then lets the Master rag_reply handle the rest.
+    """
     if not message or not message.strip():
         return "Ask a literature question (e.g., *How does CNT length affect gauge factor?*)"
     try:
+        # We call the master rag_reply which now handles synthesis and logging internally
         return rag_reply(
             question=message,
-            k=int(top_k),
-            n_sentences=int(n_sentences),
-            include_passages=bool(include_passages),
-            use_llm=bool(use_llm),
-            model=(model_name or None),
-            temperature=float(temperature),
-            strict_quotes_only=bool(strict_quotes_only),
-            w_tfidf=float(w_tfidf),
-            w_bm25=float(w_bm25),
-            w_emb=float(w_emb),
+            k=int(top_k)
         )
     except Exception as e:
+        # This is great for debugging during your 300-question run
+        traceback.print_exc() 
         return f"RAG error: {e}"
-
+    
 # ========================= UI (science-oriented styling) =========================
 CSS = """
 /* Science-oriented: crisp contrast + readable numerics */

batch_tester.py

@@ -0,0 +1,34 @@
+import pandas as pd
+import time
+import traceback
+from app import rag_reply
+
+# 1. Load the correctly named file
+try:
+    df = pd.read_csv("rag_stress_test_questions_300.csv")
+    print(f"🚀 File loaded. Starting test on {len(df)} questions...")
+except FileNotFoundError:
+    print("❌ Error: 'rag_stress_test_questions_300.csv' not found in this folder.")
+    exit()
+
+# 2. Loop through and process
+for i, row in df.iterrows():
+    question = str(row['Question'])
+    
+    # Filter out the placeholder text
+    if "Stress-test question" in question or "Advanced adversarial stress-test" in question:
+        continue
+        
+    print(f"[{i+1}/300] Category: {row['Category']} | Testing: {question[:50]}...")
+    
+    try:
+        # This triggers the RAG logic and the _safe_write_jsonl logging
+        rag_reply(question)
+    except Exception as e:
+        print(f"⚠️ Error on Q{i+1}: {e}")
+        # traceback.print_exc() # Uncomment if you need to see the full error
+    
+    # Small delay to prevent API rate limits
+    time.sleep(1)
+
+print("✅ All real questions processed! Check 'rag_artifacts/rag_logs.jsonl' for results.")

rag_artifacts/metrics_aggregate.json

@@ -0,0 +1,47 @@
+{
+  "questions_total_gold": 40,
+  "questions_covered_in_logs": 0,
+  "questions_missing_in_logs": 40,
+  "questions_in_logs_not_in_gold": 10,
+  "k": 8,
+  "mean_hit@k_doc": 0.0,
+  "mean_precision@k_doc": 0.0,
+  "mean_recall@k_doc": 0.0,
+  "mean_ndcg@k_doc": 0.0,
+  "mean_hit@k_page": null,
+  "mean_precision@k_page": null,
+  "mean_recall@k_page": null,
+  "mean_ndcg@k_page": null,
+  "avg_gold_docs_per_q": 3.225,
+  "avg_preds_per_q": 0.0,
+  "examples_missing_in_logs": [
+    "how do conductive fillers such as graphene, carbon nanotubes, and carbon black modify the sensing and mechanical behavior of cement-based materials compared with silica-fume-enhanced concretes?",
+    "what are the main conduction mechanisms and structural design principles behind self-sensing concrete, and how are these concepts complemented by nano- and micro-scale modifications such as silica fume and graphene additions?",
+    "how does carbon-nanotube dispersion technique influence the electrical conductivity and strain-sensing performance of cement-based composites according to konsta-gdoutos et al. (2014), d’alessandro et al. (2021), and lee et al. (2017)?",
+    "what advantages do hybrid carbon-based fillers (cnts + cnfs or cfs) provide over single-type fillers in cement-based self-sensing composites according to these studies?",
+    "how do graphite, few-layer graphene, and intrinsic graphene composites differ in achieving low percolation thresholds and high piezoresistive performance in cement-based sensors?",
+    "what mechanisms contribute to the self-sensing and environmental stability of graphene-based cement composites compared to graphite-filled composites?",
+    "how do fabrication methods such as ultrasonication, surfactant-assisted dispersion, and surface coating influence the mechanical and electrical properties of smart cement composites containing graphene or graphite fillers?",
+    "how do multi-scale conductive fillers (e.g., steel fibers, carbon black, and mwcnts) collectively enhance the self-sensing performance of ultra-high-performance concrete (uhpc)?",
+    "what mechanisms explain the electromechanical coupling and strain sensitivity observed in self-sensing cementitious composites enhanced with carbon black and metallic fillers?",
+    "how do dispersion and packing optimization techniques (e.g., ultrasonication, maa packing model, and controlled filler ratios) influence both conductivity and mechanical integrity of self-sensing uhpc?"
+  ],
+  "examples_in_logs_not_in_gold": [
+    "compare crack-based vs tunneling-based sensing mechanisms.",
+    "how does cnt aspect ratio influence stress gauge factor in cementitious composites?",
+    "how does cnt length influence stress gauge factor performance?",
+    "how does dimensionality (1d vs 2d fillers) affect sensing performance?",
+    "how does w/b ratio influence piezoresistive sensitivity?",
+    "what conductive filler type typically yields the highest gauge factor in cement-based\ncomposites?",
+    "what conductive filler type typically yields the highest gauge factor in cement-based composites?",
+    "what is smart concerete?",
+    "what is smart concrete",
+    "what wt% concentration range maximizes gauge factor without compromising mechanical\nstrength?"
+  ],
+  "mean_bleu": NaN,
+  "mean_rouge1": NaN,
+  "mean_rouge2": NaN,
+  "mean_rougeL": NaN,
+  "mean_bert_recall": NaN,
+  "mean_bert_f1": NaN
+}

rag_artifacts/metrics_per_question.csv

@@ -0,0 +1,41 @@
+question,covered_in_logs,hit@k_doc,precision@k_doc,recall@k_doc,ndcg@k_doc,hit@k_page,precision@k_page,recall@k_page,ndcg@k_page,n_gold_docs,n_gold_doc_pages,n_pred,bleu,rouge1,rouge2,rougeL,bert_recall,bert_f1
+"how do conductive fillers such as graphene, carbon nanotubes, and carbon black modify the sensing and mechanical behavior of cement-based materials compared with silica-fume-enhanced concretes?",0,0,0.0,0.0,0.0,,,,,5,0,0,,,,,,
+"what are the main conduction mechanisms and structural design principles behind self-sensing concrete, and how are these concepts complemented by nano- and micro-scale modifications such as silica fume and graphene additions?",0,0,0.0,0.0,0.0,,,,,5,0,0,,,,,,
+"how does carbon-nanotube dispersion technique influence the electrical conductivity and strain-sensing performance of cement-based composites according to konsta-gdoutos et al. (2014), d’alessandro et al. (2021), and lee et al. (2017)?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+what advantages do hybrid carbon-based fillers (cnts + cnfs or cfs) provide over single-type fillers in cement-based self-sensing composites according to these studies?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"how do graphite, few-layer graphene, and intrinsic graphene composites differ in achieving low percolation thresholds and high piezoresistive performance in cement-based sensors?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+what mechanisms contribute to the self-sensing and environmental stability of graphene-based cement composites compared to graphite-filled composites?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"how do fabrication methods such as ultrasonication, surfactant-assisted dispersion, and surface coating influence the mechanical and electrical properties of smart cement composites containing graphene or graphite fillers?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"how do multi-scale conductive fillers (e.g., steel fibers, carbon black, and mwcnts) collectively enhance the self-sensing performance of ultra-high-performance concrete (uhpc)?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+what mechanisms explain the electromechanical coupling and strain sensitivity observed in self-sensing cementitious composites enhanced with carbon black and metallic fillers?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"how do dispersion and packing optimization techniques (e.g., ultrasonication, maa packing model, and controlled filler ratios) influence both conductivity and mechanical integrity of self-sensing uhpc?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how do steel fibers and carbon-based fillers influence the strain-sensing and crack-monitoring behavior of smart concrete?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"what is the relationship between gauge factor, linearity, and fiber content in steel- or brass-fiber-reinforced smart concrete?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how do large-scale and cyclic loading tests verify the real-world applicability of self-sensing concrete?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how does nanocarbon black or other conductive additives enhance strain-sensing performance in ultra-high-performance concrete (uhpc)?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+what are the optimal dosages and curing conditions for achieving both mechanical strength and self-sensing in uhpc?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how do mechanical and electrical responses of self-sensing uhpc correlate under cyclic and monotonic loading?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how do ozone and silane surface treatments enhance the interfacial bonding and mechanical performance of fiber-reinforced cementitious composites?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+what microstructural and spectroscopic evidence confirms successful silane grafting and its effects on fiber thermal stability?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how do surface functionalization strategies influence the strain-sensing behavior and durability of cementitious composites containing carbon or natural fibers?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"how do hierarchical cf–cnt fillers, multiscale stainless-steel-wire/nanofiller systems, and cnt/ncb composite fillers collectively demonstrate the benefits of multiscale conductive networks for self-sensing cementitious composites?",0,0,0.0,0.0,0.0,,,,,4,0,0,,,,,,
+what do these studies reveal about the dominant piezoresistive mechanisms and their modeling in cement-based materials containing hybrid or hierarchical conductive fillers?,0,0,0.0,0.0,0.0,,,,,4,0,0,,,,,,
+"what mix design and processing strategies are recommended by these three studies to obtain high-sensitivity, durable self-sensing composites suitable for structural health monitoring applications?",0,0,0.0,0.0,0.0,,,,,4,0,0,,,,,,
+"how do water ingress, moisture saturation, and elevated temperatures respectively affect the electrical resistivity and piezoresistive response of cnt- or mwcnt-based cementitious composites with or without graphite hybridization?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+what mechanisms explain the observed changes in gauge factor and linearity of the strain-sensing response under varying water content and temperature in these cnt/mwcnt-based smart composites?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"based on these three studies, what mix design and operational strategies are recommended to achieve environmentally robust self-sensing cementitious composites for real structural health monitoring conditions?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"how does the use of pearson’s correlation in graphite-based self-sensing cement composites complement traditional râ²-based evaluation, and how can this statistical approach be combined with microstructural design strategies such as excluded volume theory and electrostatic self-assembly to optimize sensing reliability?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"what roles do percolation threshold, filler dispersion, and the excluded volume effect play in controlling piezoresistive sensitivity and linearity in graphite- and cnt/tio2-modified cementitious composites?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how can insights from piezoresistive behavior in graphite/cnt-based composites and the piezopermittivity framework be integrated to design multi-modal self-sensing cementitious systems for structural health monitoring?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"how do specimen size in shpb tests, four-point probe geometry, and mortar thickness in capacitive sensing collectively influence the measured mechanical and electrical responses of cementitious or similar materials?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"what best-practice guidelines can be derived from these three papers for selecting specimen dimensions, probe configurations, and thickness when designing robust self-sensing or high-strain-rate test setups in cement-based materials?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how can concepts from four-point probe correction factors and capacitive thickness dependence be integrated with shpb size-effect findings to interpret or design electrical and mechanical sensing in structurally scaled concrete elements?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"how do graphite-based smart pavement composites, carbon-fiber-reinforced cement mortars, and electricity-based multifunctional concrete collectively demonstrate the feasibility and advantages of embedded self-sensing systems for traffic and impact monitoring?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"what measurement configurations and design choices (e.g., electrode layouts, sensing zone geometry, and filler type) are recommended across these studies to maximize the accuracy and robustness of electrical-resistance-based monitoring in real infrastructures?",0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how do these works together outline a roadmap from laboratory-scale sensing concepts to practical deployment of electricity-based multifunctional concrete in transportation and structural systems?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how does ozone treatment modify carbon fiber surfaces and improve cement-matrix interaction?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+what are the comparative effects of silane-treated versus ozone-treated carbon fibers on the mechanical performance of cement pastes?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how does silane treatment alter the microstructure and durability of natural fibers such as sisal and bagasse used in cementitious composites?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+how do silane coupling agents affect the mechanical performance and interfacial microstructure of uhpfrc containing steel fibers?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+what role does silane chemistry and concentration play in determining the efficiency of surface modification for bagasse fibers?,0,0,0.0,0.0,0.0,,,,,3,0,0,,,,,,
+"across carbon, steel, and natural fibers, what common mechanisms explain how silane or ozone treatments improve composite strength and self-sensing potential?",0,0,0.0,0.0,0.0,,,,,5,0,0,,,,,,

rag_artifacts/rag_logs.jsonl

@@ -10,3 +10,71 @@
 {"run_id": "a70af141-50af-405b-85ec-30215ee8eef0", "ts": 1771461499990, "inputs": {"question": "How does dimensionality (1D vs 2D fillers) affect sensing performance?", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "S58-DE~1.PDF", "page": "?", "score_tfidf": 0.7734994300847481, "score_bm25": 1.0, "score_dense": 0.7566510862800921, "combo_score": 0.8347102635374613}, {"doc": "PIEZOE~1.PDF", "page": "?", "score_tfidf": 1.0, "score_bm25": 0.6737846552458285, "score_dense": 0.6365359472943736, "combo_score": 0.756749775491498}, {"doc": "Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers.pdf", "page": "?", "score_tfidf": 0.5348999082858642, "score_bm25": 0.7992842049358385, "score_dense": 0.8845492644148187, "combo_score": 0.7540749397324383}, {"doc": "S88-ST~1.PDF", "page": "?", "score_tfidf": 0.7431839210089385, "score_bm25": 0.7333916982024139, "score_dense": 0.755478518278468, "combo_score": 0.7451640930747929}, {"doc": "S58-DE~1.PDF", "page": "?", "score_tfidf": 0.590772192631221, "score_bm25": 0.7800811759550423, "score_dense": 0.7424487478244431, "combo_score": 0.7082355097056563}, {"doc": "Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers.pdf", "page": "?", "score_tfidf": 0.6523854051190268, "score_bm25": 0.6229443814399147, "score_dense": 0.7965486257539977, "combo_score": 0.7012183862692815}, {"doc": "S58-DE~1.PDF", "page": "?", "score_tfidf": 0.5631298423474721, "score_bm25": 0.8143185858623858, "score_dense": 0.7007025032140322, "combo_score": 0.6935155297485702}, {"doc": "PIEZOE~1.PDF", "page": "?", "score_tfidf": 0.9027142761530841, "score_bm25": 0.5376969990317382, "score_dense": 0.6395672172747329, "combo_score": 0.6879502694653399}], "latency_ms_retriever": 303}, "output": {"final_answer": "**Answer:** Compared with one-dimension (1D) or two-dimensions (2D) carbon nanomaterials (i.e., CNT, CNF, and GNP), zero-dimension (0D) nano­ carbon black (CB) is rarely explored as conductive fillers to manufacture UHPC-based sensor because of its inferior effectiveness in tailing the conductivity, mechanical properties, and self-sensing capacity. (Development) some functional fillers even lapped with each other. (S58) the effect of stress on the in-plane capacitance depend on the presence of aggregates, which are present in mortars and (v) What are the advantages of capacitance-based self-sensing compared to the widely studied resistance-based (PIEZOE~1) Regarding piezoresistive performance, the stability of sensing performance in response to dynamic cyclic load improves with an increasing content of conductive fillers; the hybrid fillers of CB/CNF enhance the stability of piezoresistive sensing performance of self-sensing (Development)\n\n**Citations:** <a href=\"/file=papers/S58-DE~1.PDF\" target=\"_blank\" rel=\"noopener noreferrer\">S58</a>; <a href=\"/file=papers/PIEZOE~1.PDF\" target=\"_blank\" rel=\"noopener noreferrer\">PIEZOE~1</a>; <a href=\"/file=papers/Development%20of%20self-sensing%20ultra-high-performance%20concrete%20using%20hybrid%20carbon%20black%20and%20carbon%20nanofibers.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Development</a>; <a href=\"/file=papers/S88-ST~1.PDF\" target=\"_blank\" rel=\"noopener noreferrer\">S88</a>", "used_sentences": [{"sent": "Compared with one-dimension (1D) or two-dimensions (2D) carbon nanomaterials (i.e., CNT, CNF, and GNP), zero-dimension (0D) nano­ carbon black (CB) is rarely explored as conductive fillers to manufacture UHPC-based sensor because of its inferior effectiveness in tailing the conductivity, mechanical properties, and self-sensing capacity.", "doc": "Development", "page": "?"}, {"sent": "some functional fillers even lapped with each other.", "doc": "S58", "page": "?"}, {"sent": "the effect of stress on the in-plane capacitance depend on the presence of aggregates, which are present in mortars and (v) What are the advantages of capacitance-based self-sensing compared to the widely studied resistance-based", "doc": "PIEZOE~1", "page": "?"}, {"sent": "Regarding piezoresistive performance, the stability of sensing performance in response to dynamic cyclic load improves with an increasing content of conductive fillers; the hybrid fillers of CB/CNF enhance the stability of piezoresistive sensing performance of self-sensing", "doc": "Development", "page": "?"}]}, "latency_ms_total": 936, "latency_ms_llm": null, "openai": null}
 {"run_id": "f6ec0696-df48-47e9-a788-36c6dd898052", "ts": 1771462394105, "inputs": {"question": "Compare crack-based vs tunneling-based sensing mechanisms.", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 0.6288495218369764, "score_dense": 0.7981998105575274, "combo_score": 0.8079347807741039}, {"doc": "S43 - the 100th anniversary of the four-point probe technique the role of probe geometries in isotropic andanisotropic systems.pdf", "page": "?", "score_tfidf": 0.47568867315199015, "score_bm25": 1.0, "score_dense": 0.45358689726039, "combo_score": 0.624141360849753}, {"doc": "Ozone treatment of carbon fiber for reinforcing cement.pdf", "page": "?", "score_tfidf": 0.5028889030313014, "score_bm25": 0.6960949409426922, "score_dense": 0.6571506676585992, "combo_score": 0.6225554202556378}, {"doc": "Development of sensing concrete Principles, properties and its applications.pdf", "page": "?", "score_tfidf": 0.9428744701199966, "score_bm25": 0.27472141434442077, "score_dense": 0.6236830925551956, "combo_score": 0.6147520023614034}, {"doc": "S1-An-experimental-study-of-self-sensing-concrete-enhanced_2020_Construction-an.pdf", "page": "?", "score_tfidf": 0.4502569052291563, "score_bm25": 0.6450346481748502, "score_dense": 0.7128335181406997, "combo_score": 0.6137208732774818}, {"doc": "Development of sensing concrete Principles, properties and its applications.pdf", "page": "?", "score_tfidf": 0.8621655212460113, "score_bm25": 0.24997385310213305, "score_dense": 0.6979942434691807, "combo_score": 0.6128395096921155}, {"doc": "Development of sensing concrete Principles, properties and its applications.pdf", "page": "?", "score_tfidf": 0.2876748455449677, "score_bm25": 0.8006167207316013, "score_dense": 0.6956227910540812, "combo_score": 0.6047365863046031}, {"doc": "S48-Self-sensing properties and piezoresistive effect of high ductility cementitious composite.pdf", "page": "?", "score_tfidf": 0.5135348376797868, "score_bm25": 0.6881356317868511, "score_dense": 0.5896490787508804, "combo_score": 0.5963607723403435}], "latency_ms_retriever": 68}, "output": {"final_answer": "**Answer:** These differences were discussed in detail in Section 4 The self-sensing was analysed by comparison of the mechanical response (load vs time) with the self-sensing (relative resistivity vs time) response together with the strain maps obtained with the The DIC measurement was launched in the same time as the mechanical test and resistivity measurement. (S1) This tutorial provides a brief overview on the development and progress of sensing concrete, putting emphasis on the definition; clas- sification; electrically conductive mechanisms; sensing mechanisms; (Development) Regarding piezoresistive performance, the stability of sensing performance in response to dynamic cyclic load improves with an increasing content of conductive fillers; the hybrid fillers of CB/CNF enhance the stability of piezoresistive sensing performance of self-sensing (Development) The fundamental part is to estab- lish a stable and reliable sensing system, like a “nervous subsystem,” Up to now, for the purpose of diagnostics and evaluation of structural conditions, a great number of sensing techniques have been developed and imple- mented with specific functions and mechanisms. (Development)\n\n**Citations:** <a href=\"/file=papers/Development%20of%20self-sensing%20ultra-high-performance%20concrete%20using%20hybrid%20carbon%20black%20and%20carbon%20nanofibers.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Development</a>; <a href=\"/file=papers/S43%20-%20the%20100th%20anniversary%20of%20the%20four-point%20probe%20technique%20the%20role%20of%20probe%20geometries%20in%20isotropic%20andanisotropic%20systems.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">S43</a>; <a href=\"/file=papers/Ozone%20treatment%20of%20carbon%20fiber%20for%20reinforcing%20cement.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Ozone</a>; <a href=\"/file=papers/S1-An-experimental-study-of-self-sensing-concrete-enhanced_2020_Construction-an.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">S1</a>", "used_sentences": [{"sent": "These differences were discussed in detail in Section 4 The self-sensing was analysed by comparison of the mechanical response (load vs time) with the self-sensing (relative resistivity vs time) response together with the strain maps obtained with the The DIC measurement was launched in the same time as the mechanical test and resistivity measurement.", "doc": "S1", "page": "?"}, {"sent": "This tutorial provides a brief overview on the development and progress of sensing concrete, putting emphasis on the definition; clas- sification; electrically conductive mechanisms; sensing mechanisms;", "doc": "Development", "page": "?"}, {"sent": "Regarding piezoresistive performance, the stability of sensing performance in response to dynamic cyclic load improves with an increasing content of conductive fillers; the hybrid fillers of CB/CNF enhance the stability of piezoresistive sensing performance of self-sensing", "doc": "Development", "page": "?"}, {"sent": "The fundamental part is to estab- lish a stable and reliable sensing system, like a “nervous subsystem,” Up to now, for the purpose of diagnostics and evaluation of structural conditions, a great number of sensing techniques have been developed and imple- mented with specific functions and mechanisms.", "doc": "Development", "page": "?"}]}, "latency_ms_total": 289, "latency_ms_llm": null, "openai": null}
 {"run_id": "a4c2ee09-0934-4ebb-a56d-09267af9dcac", "ts": 1771463669328, "inputs": {"question": "Compare crack-based vs tunneling-based sensing mechanisms.", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 0.6288495218369764, "score_dense": 0.7981998105575274, "combo_score": 0.8079347807741039}, {"doc": "S43 - the 100th anniversary of the four-point probe technique the role of probe geometries in isotropic andanisotropic systems.pdf", "page": "?", "score_tfidf": 0.47568867315199015, "score_bm25": 1.0, "score_dense": 0.45358689726039, "combo_score": 0.624141360849753}, {"doc": "Ozone treatment of carbon fiber for reinforcing cement.pdf", "page": "?", "score_tfidf": 0.5028889030313014, "score_bm25": 0.6960949409426922, "score_dense": 0.6571506676585992, "combo_score": 0.6225554202556378}, {"doc": "Development of sensing concrete Principles, properties and its applications.pdf", "page": "?", "score_tfidf": 0.9428744701199966, "score_bm25": 0.27472141434442077, "score_dense": 0.6236830925551956, "combo_score": 0.6147520023614034}, {"doc": "S1-An-experimental-study-of-self-sensing-concrete-enhanced_2020_Construction-an.pdf", "page": "?", "score_tfidf": 0.4502569052291563, "score_bm25": 0.6450346481748502, "score_dense": 0.7128335181406997, "combo_score": 0.6137208732774818}, {"doc": "Development of sensing concrete Principles, properties and its applications.pdf", "page": "?", "score_tfidf": 0.8621655212460113, "score_bm25": 0.24997385310213305, "score_dense": 0.6979942434691807, "combo_score": 0.6128395096921155}, {"doc": "Development of sensing concrete Principles, properties and its applications.pdf", "page": "?", "score_tfidf": 0.2876748455449677, "score_bm25": 0.8006167207316013, "score_dense": 0.6956227910540812, "combo_score": 0.6047365863046031}, {"doc": "S48-Self-sensing properties and piezoresistive effect of high ductility cementitious composite.pdf", "page": "?", "score_tfidf": 0.5135348376797868, "score_bm25": 0.6881356317868511, "score_dense": 0.5896490787508804, "combo_score": 0.5963607723403435}], "latency_ms_retriever": 179}, "output": {"final_answer": "**Answer:** These differences were discussed in detail in Section 4 The self-sensing was analysed by comparison of the mechanical response (load vs time) with the self-sensing (relative resistivity vs time) response together with the strain maps obtained with the The DIC measurement was launched in the same time as the mechanical test and resistivity measurement. (S1) This tutorial provides a brief overview on the development and progress of sensing concrete, putting emphasis on the definition; clas- sification; electrically conductive mechanisms; sensing mechanisms; (Development) Regarding piezoresistive performance, the stability of sensing performance in response to dynamic cyclic load improves with an increasing content of conductive fillers; the hybrid fillers of CB/CNF enhance the stability of piezoresistive sensing performance of self-sensing (Development) The fundamental part is to estab- lish a stable and reliable sensing system, like a “nervous subsystem,” Up to now, for the purpose of diagnostics and evaluation of structural conditions, a great number of sensing techniques have been developed and imple- mented with specific functions and mechanisms. (Development)\n\n**Citations:** <a href=\"/file=C:\\Users\\kmanc\\Smart_Concrete_Chatbot\\papers\\Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers.pdf\" target=\"_blank\" rel=\"noopener noreferrer\" style=\"color: black; font-weight: bold; text-decoration: underline;\">Development</a>; <a href=\"/file=C:\\Users\\kmanc\\Smart_Concrete_Chatbot\\papers\\S43 - the 100th anniversary of the four-point probe technique the role of probe geometries in isotropic andanisotropic systems.pdf\" target=\"_blank\" rel=\"noopener noreferrer\" style=\"color: black; font-weight: bold; text-decoration: underline;\">S43</a>; <a href=\"/file=C:\\Users\\kmanc\\Smart_Concrete_Chatbot\\papers\\Ozone treatment of carbon fiber for reinforcing cement.pdf\" target=\"_blank\" rel=\"noopener noreferrer\" style=\"color: black; font-weight: bold; text-decoration: underline;\">Ozone</a>; <a href=\"/file=C:\\Users\\kmanc\\Smart_Concrete_Chatbot\\papers\\S1-An-experimental-study-of-self-sensing-concrete-enhanced_2020_Construction-an.pdf\" target=\"_blank\" rel=\"noopener noreferrer\" style=\"color: black; font-weight: bold; text-decoration: underline;\">S1</a>", "used_sentences": [{"sent": "These differences were discussed in detail in Section 4 The self-sensing was analysed by comparison of the mechanical response (load vs time) with the self-sensing (relative resistivity vs time) response together with the strain maps obtained with the The DIC measurement was launched in the same time as the mechanical test and resistivity measurement.", "doc": "S1", "page": "?"}, {"sent": "This tutorial provides a brief overview on the development and progress of sensing concrete, putting emphasis on the definition; clas- sification; electrically conductive mechanisms; sensing mechanisms;", "doc": "Development", "page": "?"}, {"sent": "Regarding piezoresistive performance, the stability of sensing performance in response to dynamic cyclic load improves with an increasing content of conductive fillers; the hybrid fillers of CB/CNF enhance the stability of piezoresistive sensing performance of self-sensing", "doc": "Development", "page": "?"}, {"sent": "The fundamental part is to estab- lish a stable and reliable sensing system, like a “nervous subsystem,” Up to now, for the purpose of diagnostics and evaluation of structural conditions, a great number of sensing techniques have been developed and imple- mented with specific functions and mechanisms.", "doc": "Development", "page": "?"}]}, "latency_ms_total": 685, "latency_ms_llm": null, "openai": null}
+{"timestamp": 1773004747.9495323, "question": "How does the concentration of C=O functional groups on graphene oxide influence the piezoresistive sensitivity of cement mortar?", "confidence": 100, "diversity": 5, "answer_length": 3196}
+{"timestamp": 1773004758.3943822, "question": "Explain the difference in signal-to-noise ratio when using CNO-modified fillers versus standard CNTs in high-strain environments.", "confidence": 92, "diversity": 8, "answer_length": 3237}
+{"timestamp": 1773004767.2201657, "question": "Does the presence of ε-phase crystalline structures in the binder affect the modulus of elasticity during SHPB testing?", "confidence": 90, "diversity": 3, "answer_length": 2788}
+{"timestamp": 1773004779.6000435, "question": "Compare the Gauge Factor results for specimens where C=O was reduced to C-OH via thermal treatment.", "confidence": 94, "diversity": 9, "answer_length": 3636}
+{"timestamp": 1773004787.6870162, "question": "What is the impact of σ-π transition states in carbon powder on the electrical conductivity of the composite?", "confidence": 100, "diversity": 9, "answer_length": 2586}
+{"timestamp": 1773004800.21502, "question": "Analyze the frequency response of a sensor using CNO-functionalized MWCNTs under cyclic loading.", "confidence": 98, "diversity": 6, "answer_length": 3361}
+{"timestamp": 1773004811.1010408, "question": "Does the ratio of sp2 to sp3 carbon in the filler change the strain-sensing linearity?", "confidence": 100, "diversity": 9, "answer_length": 2889}
+{"timestamp": 1773004817.6756349, "question": "How do C=O bonds at the filler-matrix interface contribute to tunneling resistance?", "confidence": 87, "diversity": 8, "answer_length": 2447}
+{"timestamp": 1773004827.2061872, "question": "What are the specific peaks for CNO compounds in the FTIR spectrum of this cementitious composite?", "confidence": 90, "diversity": 8, "answer_length": 2592}
+{"timestamp": 1773004835.809256, "question": "Is the ρ (resistivity) of the mortar affected by the alignment of 1D fillers under magnetic fields?", "confidence": 98, "diversity": 4, "answer_length": 2711}
+{"timestamp": 1773004844.1826334, "question": "What is the critical strain rate threshold in SHPB testing where the mortar exhibits transition from brittle to ductile failure?", "confidence": 95, "diversity": 4, "answer_length": 3012}
+{"timestamp": 1773004852.7289596, "question": "How does the pulse shaper thickness in a 20mm SHPB setup affect the rise time for concrete samples?", "confidence": 91, "diversity": 4, "answer_length": 2712}
+{"timestamp": 1773004860.4774845, "question": "Compare the dynamic increase factor (DIF) of CNT-reinforced mortar at 500/s vs 1000/s strain rates.", "confidence": 89, "diversity": 3, "answer_length": 2141}
+{"timestamp": 1773004870.9227061, "question": "Does the capacitive sensing method used in the 2018 study maintain accuracy during microsecond SHPB events?", "confidence": 93, "diversity": 8, "answer_length": 3242}
+{"timestamp": 1773004879.4302871, "question": "What is the relationship between filler dimensionality (1D vs 2D) and energy absorption under dynamic impact?", "confidence": 94, "diversity": 5, "answer_length": 2981}
+{"timestamp": 1773004888.6367402, "question": "Analyze the wave dispersion effects when testing 50mm diameter cementitious cylinders in a Hopkinson Bar.", "confidence": 95, "diversity": 2, "answer_length": 3533}
+{"timestamp": 1773004896.3274937, "question": "How does the moisture content of the cement matrix influence the longitudinal wave velocity in SHPB experiments?", "confidence": 98, "diversity": 4, "answer_length": 2915}
+{"timestamp": 1773004902.1364927, "question": "Identify the failure mode of GNP-reinforced mortar under high-velocity projectile impact simulation.", "confidence": 91, "diversity": 5, "answer_length": 1885}
+{"timestamp": 1773004910.3619814, "question": "What is the effect of specimen length-to-diameter ratio on the stress equilibrium in dynamic compression tests?", "confidence": 96, "diversity": 2, "answer_length": 2417}
+{"timestamp": 1773004920.2380776, "question": "Compare the fragmentation patterns of ultra-high performance concrete (UHPC) under dynamic vs. static loading.", "confidence": 99, "diversity": 5, "answer_length": 3061}
+{"timestamp": 1773004929.2661033, "question": "How does the hydration age (7d, 28d, 90d) change the baseline resistance of self-sensing concrete?", "confidence": 88, "diversity": 6, "answer_length": 2926}
+{"timestamp": 1773004939.030053, "question": "Does the C-S-H gel density directly correlate with the tunneling distance between conductive fillers?", "confidence": 99, "diversity": 6, "answer_length": 3099}
+{"timestamp": 1773004948.6442466, "question": "What is the effect of fly ash replacement on the percolation threshold of carbon black in cement?", "confidence": 100, "diversity": 8, "answer_length": 3165}
+{"timestamp": 1773004959.1137805, "question": "Analyze the impact of chloride penetration on the piezoresistive stability of a 0.5 wt% CNT sensor.", "confidence": 98, "diversity": 6, "answer_length": 3224}
+{"timestamp": 1773004969.441732, "question": "How does the alkalinity (pH) of the pore solution affect the dispersion of graphene oxide flakes?", "confidence": 96, "diversity": 5, "answer_length": 3144}
+{"timestamp": 1773004977.7273803, "question": "Is the fractional change in resistance (ΔR/R0) sensitive to temperature fluctuations between -10C and 40C?", "confidence": 93, "diversity": 8, "answer_length": 2778}
+{"timestamp": 1773004986.1239338, "question": "What is the role of silica fume in improving the filler-matrix interfacial bond for strain sensing?", "confidence": 100, "diversity": 3, "answer_length": 2924}
+{"timestamp": 1773004994.7572043, "question": "Compare the piezoresistive performance of specimens cured in water versus those cured in a high-humidity chamber.", "confidence": 92, "diversity": 9, "answer_length": 2627}
+{"timestamp": 1773005004.9071126, "question": "How do superplasticizers influence the electrical connectivity of steel fibers in a fresh mix?", "confidence": 94, "diversity": 3, "answer_length": 3190}
+{"timestamp": 1773005013.2880156, "question": "What is the impact of air-entraining agents on the air-void distribution and its effect on sensing repeatability?", "confidence": 91, "diversity": 6, "answer_length": 2259}
+{"timestamp": 1773005020.123806, "question": "impact of Carbon Black on the percolation threshold", "confidence": 100, "diversity": 6, "answer_length": 2593}
+{"timestamp": 1773005029.130609, "question": "analysis of Carbon Black during high-strain rate SHPB testing", "confidence": 84, "diversity": 7, "answer_length": 3302}
+{"timestamp": 1773005037.9433177, "question": "correlation between Carbon Black and the resulting Gauge Factor", "confidence": 86, "diversity": 8, "answer_length": 3027}
+{"timestamp": 1773005044.7007596, "question": "effect of Carbon Black on the interfacial transition zone (ITZ)", "confidence": 94, "diversity": 8, "answer_length": 2128}
+{"timestamp": 1773005056.8797517, "question": "comparison of Carbon Black versus standard CNT fillers for piezoresistive stability", "confidence": 99, "diversity": 9, "answer_length": 3611}
+{"timestamp": 1773005065.8885133, "question": "measurement of Carbon Black in mortar composites using 4-probe AC methods", "confidence": 99, "diversity": 9, "answer_length": 3789}
+{"timestamp": 1773005076.6523573, "question": "how Carbon Black influences the piezoresistive linearity under cyclic compression", "confidence": 96, "diversity": 9, "answer_length": 2992}
+{"timestamp": 1773005086.2847662, "question": "the role of Carbon Black in reducing signal drift over long-term monitoring", "confidence": 95, "diversity": 8, "answer_length": 3222}
+{"timestamp": 1773005096.92086, "question": "evaluating the Carbon Black of self-sensing concrete in sub-zero environments", "confidence": 100, "diversity": 10, "answer_length": 3574}
+{"timestamp": 1773005109.010627, "question": "detecting Carbon Black in cementitious binders via capacitive sensing arrays", "confidence": 96, "diversity": 7, "answer_length": 4116}
+{"timestamp": 1773005118.1160338, "question": "impact of Nickel Powder on the percolation threshold", "confidence": 98, "diversity": 6, "answer_length": 3002}
+{"timestamp": 1773005128.573945, "question": "analysis of Nickel Powder during high-strain rate SHPB testing", "confidence": 84, "diversity": 5, "answer_length": 3472}
+{"timestamp": 1773005140.0317864, "question": "correlation between Nickel Powder and the resulting Gauge Factor", "confidence": 94, "diversity": 7, "answer_length": 3306}
+{"timestamp": 1773005146.7745957, "question": "effect of Nickel Powder on the interfacial transition zone (ITZ)", "confidence": 89, "diversity": 8, "answer_length": 1969}
+{"timestamp": 1773005159.1212792, "question": "comparison of Nickel Powder versus standard CNT fillers for piezoresistive stability", "confidence": 92, "diversity": 6, "answer_length": 3846}
+{"timestamp": 1773005168.1929867, "question": "measurement of Nickel Powder in mortar composites using 4-probe AC methods", "confidence": 98, "diversity": 9, "answer_length": 3149}
+{"timestamp": 1773005176.7952042, "question": "how Nickel Powder influences the piezoresistive linearity under cyclic compression", "confidence": 94, "diversity": 8, "answer_length": 3445}
+{"timestamp": 1773005184.6307576, "question": "the role of Nickel Powder in reducing signal drift over long-term monitoring", "confidence": 95, "diversity": 9, "answer_length": 2590}
+{"timestamp": 1773005191.7995322, "question": "evaluating the Nickel Powder of self-sensing concrete in sub-zero environments", "confidence": 100, "diversity": 8, "answer_length": 3048}
+{"timestamp": 1773005201.063239, "question": "detecting Nickel Powder in cementitious binders via capacitive sensing arrays", "confidence": 99, "diversity": 8, "answer_length": 2621}
+{"timestamp": 1773005209.9748359, "question": "impact of Brass Fibers on the percolation threshold", "confidence": 99, "diversity": 6, "answer_length": 2908}
+{"timestamp": 1773005217.1368463, "question": "analysis of Brass Fibers during high-strain rate SHPB testing", "confidence": 93, "diversity": 3, "answer_length": 2946}
+{"timestamp": 1773005226.9279313, "question": "correlation between Brass Fibers and the resulting Gauge Factor", "confidence": 99, "diversity": 5, "answer_length": 3064}
+{"timestamp": 1773005234.4775062, "question": "effect of Brass Fibers on the interfacial transition zone (ITZ)", "confidence": 92, "diversity": 9, "answer_length": 2915}
+{"timestamp": 1773005245.9072475, "question": "comparison of Brass Fibers versus standard CNT fillers for piezoresistive stability", "confidence": 86, "diversity": 6, "answer_length": 3009}
+{"timestamp": 1773005252.5151842, "question": "measurement of Brass Fibers in mortar composites using 4-probe AC methods", "confidence": 96, "diversity": 5, "answer_length": 2262}
+{"timestamp": 1773005258.7785926, "question": "how Brass Fibers influences the piezoresistive linearity under cyclic compression", "confidence": 100, "diversity": 5, "answer_length": 2258}
+{"timestamp": 1773005264.4916682, "question": "the role of Brass Fibers in reducing signal drift over long-term monitoring", "confidence": 97, "diversity": 4, "answer_length": 1954}
+{"timestamp": 1773005271.7245407, "question": "evaluating the Brass Fibers of self-sensing concrete in sub-zero environments", "confidence": 100, "diversity": 3, "answer_length": 2458}
+{"timestamp": 1773005279.3712845, "question": "detecting Brass Fibers in cementitious binders via capacitive sensing arrays", "confidence": 94, "diversity": 4, "answer_length": 2973}
+{"timestamp": 1773005289.484631, "question": "impact of Hybrid CNT/GNP systems on the percolation threshold", "confidence": 100, "diversity": 10, "answer_length": 3301}
+{"timestamp": 1773005300.7197323, "question": "analysis of Hybrid CNT/GNP systems during high-strain rate SHPB testing", "confidence": 80, "diversity": 4, "answer_length": 3466}
+{"timestamp": 1773005311.8359776, "question": "correlation between Hybrid CNT/GNP systems and the resulting Gauge Factor", "confidence": 89, "diversity": 8, "answer_length": 3556}
+{"timestamp": 1773005321.1110218, "question": "effect of Hybrid CNT/GNP systems on the interfacial transition zone (ITZ)", "confidence": 87, "diversity": 8, "answer_length": 3092}
+{"timestamp": 1773005328.706657, "question": "comparison of Hybrid CNT/GNP systems versus standard CNT fillers for piezoresistive stability", "confidence": 90, "diversity": 9, "answer_length": 2976}
+{"timestamp": 1773005337.665025, "question": "measurement of Hybrid CNT/GNP systems in mortar composites using 4-probe AC methods", "confidence": 99, "diversity": 8, "answer_length": 3747}
+{"timestamp": 1773005345.9439986, "question": "how Hybrid CNT/GNP systems influences the piezoresistive linearity under cyclic compression", "confidence": 93, "diversity": 9, "answer_length": 2989}
+{"timestamp": 1773005353.5216954, "question": "the role of Hybrid CNT/GNP systems in reducing signal drift over long-term monitoring", "confidence": 88, "diversity": 7, "answer_length": 3096}

rag_stress_test_questions_300.csv

@@ -0,0 +1,301 @@
+ID,Category,Question
+Q001,LexicalNotation,How does the concentration of C=O functional groups on graphene oxide influence the piezoresistive sensitivity of cement mortar?
+Q002,LexicalNotation,Explain the difference in signal-to-noise ratio when using CNO-modified fillers versus standard CNTs in high-strain environments.
+Q003,LexicalNotation,Does the presence of ε-phase crystalline structures in the binder affect the modulus of elasticity during SHPB testing?
+Q004,LexicalNotation,Compare the Gauge Factor results for specimens where C=O was reduced to C-OH via thermal treatment.
+Q005,LexicalNotation,What is the impact of σ-π transition states in carbon powder on the electrical conductivity of the composite?
+Q006,LexicalNotation,Analyze the frequency response of a sensor using CNO-functionalized MWCNTs under cyclic loading.
+Q007,LexicalNotation,Does the ratio of sp2 to sp3 carbon in the filler change the strain-sensing linearity?
+Q008,LexicalNotation,How do C=O bonds at the filler-matrix interface contribute to tunneling resistance?
+Q009,LexicalNotation,What are the specific peaks for CNO compounds in the FTIR spectrum of this cementitious composite?
+Q010,LexicalNotation,Is the ρ (resistivity) of the mortar affected by the alignment of 1D fillers under magnetic fields?
+Q011,LexicalNotation,What is the critical strain rate threshold in SHPB testing where the mortar exhibits transition from brittle to ductile failure?
+Q012,LexicalNotation,How does the pulse shaper thickness in a 20mm SHPB setup affect the rise time for concrete samples?
+Q013,LexicalNotation,Compare the dynamic increase factor (DIF) of CNT-reinforced mortar at 500/s vs 1000/s strain rates.
+Q014,LexicalNotation,Does the capacitive sensing method used in the 2018 study maintain accuracy during microsecond SHPB events?
+Q015,LexicalNotation,What is the relationship between filler dimensionality (1D vs 2D) and energy absorption under dynamic impact?
+Q016,LexicalNotation,Analyze the wave dispersion effects when testing 50mm diameter cementitious cylinders in a Hopkinson Bar.
+Q017,LexicalNotation,How does the moisture content of the cement matrix influence the longitudinal wave velocity in SHPB experiments?
+Q018,LexicalNotation,Identify the failure mode of GNP-reinforced mortar under high-velocity projectile impact simulation.
+Q019,LexicalNotation,What is the effect of specimen length-to-diameter ratio on the stress equilibrium in dynamic compression tests?
+Q020,LexicalNotation,Compare the fragmentation patterns of ultra-high performance concrete (UHPC) under dynamic vs. static loading.
+Q021,LexicalNotation,"How does the hydration age (7d, 28d, 90d) change the baseline resistance of self-sensing concrete?"
+Q022,LexicalNotation,Does the C-S-H gel density directly correlate with the tunneling distance between conductive fillers?
+Q023,LexicalNotation,What is the effect of fly ash replacement on the percolation threshold of carbon black in cement?
+Q024,LexicalNotation,Analyze the impact of chloride penetration on the piezoresistive stability of a 0.5 wt% CNT sensor.
+Q025,LexicalNotation,How does the alkalinity (pH) of the pore solution affect the dispersion of graphene oxide flakes?
+Q026,LexicalNotation,Is the fractional change in resistance (ΔR/R0) sensitive to temperature fluctuations between -10C and 40C?
+Q027,LexicalNotation,What is the role of silica fume in improving the filler-matrix interfacial bond for strain sensing?
+Q028,LexicalNotation,Compare the piezoresistive performance of specimens cured in water versus those cured in a high-humidity chamber.
+Q029,LexicalNotation,How do superplasticizers influence the electrical connectivity of steel fibers in a fresh mix?
+Q030,LexicalNotation,What is the impact of air-entraining agents on the air-void distribution and its effect on sensing repeatability?
+Q031,LexicalNotation,impact of Carbon Black on the percolation threshold
+Q032,LexicalNotation,analysis of Carbon Black during high-strain rate SHPB testing
+Q033,LexicalNotation,correlation between Carbon Black and the resulting Gauge Factor
+Q034,LexicalNotation,effect of Carbon Black on the interfacial transition zone (ITZ)
+Q035,LexicalNotation,comparison of Carbon Black versus standard CNT fillers for piezoresistive stability
+Q036,LexicalNotation,measurement of Carbon Black in mortar composites using 4-probe AC methods
+Q037,LexicalNotation,how Carbon Black influences the piezoresistive linearity under cyclic compression
+Q038,LexicalNotation,the role of Carbon Black in reducing signal drift over long-term monitoring
+Q039,LexicalNotation,evaluating the Carbon Black of self-sensing concrete in sub-zero environments
+Q040,LexicalNotation,detecting Carbon Black in cementitious binders via capacitive sensing arrays
+Q041,LexicalNotation,impact of Nickel Powder on the percolation threshold
+Q042,LexicalNotation,analysis of Nickel Powder during high-strain rate SHPB testing
+Q043,LexicalNotation,correlation between Nickel Powder and the resulting Gauge Factor
+Q044,LexicalNotation,effect of Nickel Powder on the interfacial transition zone (ITZ)
+Q045,LexicalNotation,comparison of Nickel Powder versus standard CNT fillers for piezoresistive stability
+Q046,LexicalNotation,measurement of Nickel Powder in mortar composites using 4-probe AC methods
+Q047,LexicalNotation,how Nickel Powder influences the piezoresistive linearity under cyclic compression
+Q048,LexicalNotation,the role of Nickel Powder in reducing signal drift over long-term monitoring
+Q049,LexicalNotation,evaluating the Nickel Powder of self-sensing concrete in sub-zero environments
+Q050,LexicalNotation,detecting Nickel Powder in cementitious binders via capacitive sensing arrays
+Q051,DomainEdgeCase,impact of Brass Fibers on the percolation threshold
+Q052,DomainEdgeCase,analysis of Brass Fibers during high-strain rate SHPB testing
+Q053,DomainEdgeCase,correlation between Brass Fibers and the resulting Gauge Factor
+Q054,DomainEdgeCase,effect of Brass Fibers on the interfacial transition zone (ITZ)
+Q055,DomainEdgeCase,comparison of Brass Fibers versus standard CNT fillers for piezoresistive stability
+Q056,DomainEdgeCase,measurement of Brass Fibers in mortar composites using 4-probe AC methods
+Q057,DomainEdgeCase,how Brass Fibers influences the piezoresistive linearity under cyclic compression
+Q058,DomainEdgeCase,the role of Brass Fibers in reducing signal drift over long-term monitoring
+Q059,DomainEdgeCase,evaluating the Brass Fibers of self-sensing concrete in sub-zero environments
+Q060,DomainEdgeCase,detecting Brass Fibers in cementitious binders via capacitive sensing arrays
+Q061,DomainEdgeCase,impact of Hybrid CNT/GNP systems on the percolation threshold
+Q062,DomainEdgeCase,analysis of Hybrid CNT/GNP systems during high-strain rate SHPB testing
+Q063,DomainEdgeCase,correlation between Hybrid CNT/GNP systems and the resulting Gauge Factor
+Q064,DomainEdgeCase,effect of Hybrid CNT/GNP systems on the interfacial transition zone (ITZ)
+Q065,DomainEdgeCase,comparison of Hybrid CNT/GNP systems versus standard CNT fillers for piezoresistive stability
+Q066,DomainEdgeCase,measurement of Hybrid CNT/GNP systems in mortar composites using 4-probe AC methods
+Q067,DomainEdgeCase,how Hybrid CNT/GNP systems influences the piezoresistive linearity under cyclic compression
+Q068,DomainEdgeCase,the role of Hybrid CNT/GNP systems in reducing signal drift over long-term monitoring
+Q069,DomainEdgeCase,evaluating the Hybrid CNT/GNP systems of self-sensing concrete in sub-zero environments
+Q070,DomainEdgeCase,detecting Hybrid CNT/GNP systems in cementitious binders via capacitive sensing arrays
+Q071,DomainEdgeCase,impact of MWCNTs on the percolation threshold
+Q072,DomainEdgeCase,analysis of MWCNTs during high-strain rate SHPB testing
+Q073,DomainEdgeCase,correlation between MWCNTs and the resulting Gauge Factor
+Q074,DomainEdgeCase,effect of MWCNTs on the interfacial transition zone (ITZ)
+Q075,DomainEdgeCase,comparison of MWCNTs versus standard CNT fillers for piezoresistive stability
+Q076,DomainEdgeCase,measurement of MWCNTs in mortar composites using 4-probe AC methods
+Q077,DomainEdgeCase,how MWCNTs influences the piezoresistive linearity under cyclic compression
+Q078,DomainEdgeCase,the role of MWCNTs in reducing signal drift over long-term monitoring
+Q079,DomainEdgeCase,evaluating the MWCNTs of self-sensing concrete in sub-zero environments
+Q080,DomainEdgeCase,detecting MWCNTs in cementitious binders via capacitive sensing arrays
+Q081,DomainEdgeCase,impact of Graphene Nanoplatelets on the percolation threshold
+Q082,DomainEdgeCase,analysis of Graphene Nanoplatelets during high-strain rate SHPB testing
+Q083,DomainEdgeCase,correlation between Graphene Nanoplatelets and the resulting Gauge Factor
+Q084,DomainEdgeCase,effect of Graphene Nanoplatelets on the interfacial transition zone (ITZ)
+Q085,DomainEdgeCase,comparison of Graphene Nanoplatelets versus standard CNT fillers for piezoresistive stability
+Q086,DomainEdgeCase,measurement of Graphene Nanoplatelets in mortar composites using 4-probe AC methods
+Q087,DomainEdgeCase,how Graphene Nanoplatelets influences the piezoresistive linearity under cyclic compression
+Q088,DomainEdgeCase,the role of Graphene Nanoplatelets in reducing signal drift over long-term monitoring
+Q089,DomainEdgeCase,evaluating the Graphene Nanoplatelets of self-sensing concrete in sub-zero environments
+Q090,DomainEdgeCase,detecting Graphene Nanoplatelets in cementitious binders via capacitive sensing arrays
+Q091,DomainEdgeCase,impact of Steel Fibers on the percolation threshold
+Q092,DomainEdgeCase,analysis of Steel Fibers during high-strain rate SHPB testing
+Q093,DomainEdgeCase,correlation between Steel Fibers and the resulting Gauge Factor
+Q094,DomainEdgeCase,effect of Steel Fibers on the interfacial transition zone (ITZ)
+Q095,DomainEdgeCase,comparison of Steel Fibers versus standard CNT fillers for piezoresistive stability
+Q096,DomainEdgeCase,measurement of Steel Fibers in mortar composites using 4-probe AC methods
+Q097,DomainEdgeCase,how Steel Fibers influences the piezoresistive linearity under cyclic compression
+Q098,DomainEdgeCase,the role of Steel Fibers in reducing signal drift over long-term monitoring
+Q099,DomainEdgeCase,evaluating the Steel Fibers of self-sensing concrete in sub-zero environments
+Q100,DomainEdgeCase,detecting Steel Fibers in cementitious binders via capacitive sensing arrays
+Q101,HighPrecisionData,impact of Carbon Black on the percolation threshold
+Q102,HighPrecisionData,analysis of Carbon Black during high-strain rate SHPB testing
+Q103,HighPrecisionData,correlation between Carbon Black and the resulting Gauge Factor
+Q104,HighPrecisionData,effect of Carbon Black on the interfacial transition zone (ITZ)
+Q105,HighPrecisionData,comparison of Carbon Black versus standard CNT fillers for piezoresistive stability
+Q106,HighPrecisionData,measurement of Carbon Black in mortar composites using 4-probe AC methods
+Q107,HighPrecisionData,how Carbon Black influences the piezoresistive linearity under cyclic compression
+Q108,HighPrecisionData,the role of Carbon Black in reducing signal drift over long-term monitoring
+Q109,HighPrecisionData,evaluating the Carbon Black of self-sensing concrete in sub-zero environments
+Q110,HighPrecisionData,detecting Carbon Black in cementitious binders via capacitive sensing arrays
+Q111,HighPrecisionData,impact of Nickel Powder on the percolation threshold
+Q112,HighPrecisionData,analysis of Nickel Powder during high-strain rate SHPB testing
+Q113,HighPrecisionData,correlation between Nickel Powder and the resulting Gauge Factor
+Q114,HighPrecisionData,effect of Nickel Powder on the interfacial transition zone (ITZ)
+Q115,HighPrecisionData,comparison of Nickel Powder versus standard CNT fillers for piezoresistive stability
+Q116,HighPrecisionData,measurement of Nickel Powder in mortar composites using 4-probe AC methods
+Q117,HighPrecisionData,how Nickel Powder influences the piezoresistive linearity under cyclic compression
+Q118,HighPrecisionData,the role of Nickel Powder in reducing signal drift over long-term monitoring
+Q119,HighPrecisionData,evaluating the Nickel Powder of self-sensing concrete in sub-zero environments
+Q120,HighPrecisionData,detecting Nickel Powder in cementitious binders via capacitive sensing arrays
+Q121,HighPrecisionData,impact of Brass Fibers on the percolation threshold
+Q122,HighPrecisionData,analysis of Brass Fibers during high-strain rate SHPB testing
+Q123,HighPrecisionData,correlation between Brass Fibers and the resulting Gauge Factor
+Q124,HighPrecisionData,effect of Brass Fibers on the interfacial transition zone (ITZ)
+Q125,HighPrecisionData,comparison of Brass Fibers versus standard CNT fillers for piezoresistive stability
+Q126,HighPrecisionData,measurement of Brass Fibers in mortar composites using 4-probe AC methods
+Q127,HighPrecisionData,how Brass Fibers influences the piezoresistive linearity under cyclic compression
+Q128,HighPrecisionData,the role of Brass Fibers in reducing signal drift over long-term monitoring
+Q129,HighPrecisionData,evaluating the Brass Fibers of self-sensing concrete in sub-zero environments
+Q130,HighPrecisionData,detecting Brass Fibers in cementitious binders via capacitive sensing arrays
+Q131,HighPrecisionData,impact of Hybrid CNT/GNP systems on the percolation threshold
+Q132,HighPrecisionData,analysis of Hybrid CNT/GNP systems during high-strain rate SHPB testing
+Q133,HighPrecisionData,correlation between Hybrid CNT/GNP systems and the resulting Gauge Factor
+Q134,HighPrecisionData,effect of Hybrid CNT/GNP systems on the interfacial transition zone (ITZ)
+Q135,HighPrecisionData,comparison of Hybrid CNT/GNP systems versus standard CNT fillers for piezoresistive stability
+Q136,HighPrecisionData,measurement of Hybrid CNT/GNP systems in mortar composites using 4-probe AC methods
+Q137,HighPrecisionData,how Hybrid CNT/GNP systems influences the piezoresistive linearity under cyclic compression
+Q138,HighPrecisionData,the role of Hybrid CNT/GNP systems in reducing signal drift over long-term monitoring
+Q139,HighPrecisionData,evaluating the Hybrid CNT/GNP systems of self-sensing concrete in sub-zero environments
+Q140,HighPrecisionData,detecting Hybrid CNT/GNP systems in cementitious binders via capacitive sensing arrays
+Q141,HighPrecisionData,impact of MWCNTs on the percolation threshold
+Q142,HighPrecisionData,analysis of MWCNTs during high-strain rate SHPB testing
+Q143,HighPrecisionData,correlation between MWCNTs and the resulting Gauge Factor
+Q144,HighPrecisionData,effect of MWCNTs on the interfacial transition zone (ITZ)
+Q145,HighPrecisionData,comparison of MWCNTs versus standard CNT fillers for piezoresistive stability
+Q146,HighPrecisionData,measurement of MWCNTs in mortar composites using 4-probe AC methods
+Q147,HighPrecisionData,how MWCNTs influences the piezoresistive linearity under cyclic compression
+Q148,HighPrecisionData,the role of MWCNTs in reducing signal drift over long-term monitoring
+Q149,HighPrecisionData,evaluating the MWCNTs of self-sensing concrete in sub-zero environments
+Q150,HighPrecisionData,detecting MWCNTs in cementitious binders via capacitive sensing arrays
+Q151,LexicalNotation,"Advanced adversarial stress-test question 151 involving notation ambiguity, domain drift, or precise experimental data."
+Q152,LexicalNotation,"Advanced adversarial stress-test question 152 involving notation ambiguity, domain drift, or precise experimental data."
+Q153,LexicalNotation,"Advanced adversarial stress-test question 153 involving notation ambiguity, domain drift, or precise experimental data."
+Q154,LexicalNotation,"Advanced adversarial stress-test question 154 involving notation ambiguity, domain drift, or precise experimental data."
+Q155,LexicalNotation,"Advanced adversarial stress-test question 155 involving notation ambiguity, domain drift, or precise experimental data."
+Q156,LexicalNotation,"Advanced adversarial stress-test question 156 involving notation ambiguity, domain drift, or precise experimental data."
+Q157,LexicalNotation,"Advanced adversarial stress-test question 157 involving notation ambiguity, domain drift, or precise experimental data."
+Q158,LexicalNotation,"Advanced adversarial stress-test question 158 involving notation ambiguity, domain drift, or precise experimental data."
+Q159,LexicalNotation,"Advanced adversarial stress-test question 159 involving notation ambiguity, domain drift, or precise experimental data."
+Q160,LexicalNotation,"Advanced adversarial stress-test question 160 involving notation ambiguity, domain drift, or precise experimental data."
+Q161,LexicalNotation,"Advanced adversarial stress-test question 161 involving notation ambiguity, domain drift, or precise experimental data."
+Q162,LexicalNotation,"Advanced adversarial stress-test question 162 involving notation ambiguity, domain drift, or precise experimental data."
+Q163,LexicalNotation,"Advanced adversarial stress-test question 163 involving notation ambiguity, domain drift, or precise experimental data."
+Q164,LexicalNotation,"Advanced adversarial stress-test question 164 involving notation ambiguity, domain drift, or precise experimental data."
+Q165,LexicalNotation,"Advanced adversarial stress-test question 165 involving notation ambiguity, domain drift, or precise experimental data."
+Q166,LexicalNotation,"Advanced adversarial stress-test question 166 involving notation ambiguity, domain drift, or precise experimental data."
+Q167,LexicalNotation,"Advanced adversarial stress-test question 167 involving notation ambiguity, domain drift, or precise experimental data."
+Q168,LexicalNotation,"Advanced adversarial stress-test question 168 involving notation ambiguity, domain drift, or precise experimental data."
+Q169,LexicalNotation,"Advanced adversarial stress-test question 169 involving notation ambiguity, domain drift, or precise experimental data."
+Q170,LexicalNotation,"Advanced adversarial stress-test question 170 involving notation ambiguity, domain drift, or precise experimental data."
+Q171,LexicalNotation,"Advanced adversarial stress-test question 171 involving notation ambiguity, domain drift, or precise experimental data."
+Q172,LexicalNotation,"Advanced adversarial stress-test question 172 involving notation ambiguity, domain drift, or precise experimental data."
+Q173,LexicalNotation,"Advanced adversarial stress-test question 173 involving notation ambiguity, domain drift, or precise experimental data."
+Q174,LexicalNotation,"Advanced adversarial stress-test question 174 involving notation ambiguity, domain drift, or precise experimental data."
+Q175,LexicalNotation,"Advanced adversarial stress-test question 175 involving notation ambiguity, domain drift, or precise experimental data."
+Q176,LexicalNotation,"Advanced adversarial stress-test question 176 involving notation ambiguity, domain drift, or precise experimental data."
+Q177,LexicalNotation,"Advanced adversarial stress-test question 177 involving notation ambiguity, domain drift, or precise experimental data."
+Q178,LexicalNotation,"Advanced adversarial stress-test question 178 involving notation ambiguity, domain drift, or precise experimental data."
+Q179,LexicalNotation,"Advanced adversarial stress-test question 179 involving notation ambiguity, domain drift, or precise experimental data."
+Q180,LexicalNotation,"Advanced adversarial stress-test question 180 involving notation ambiguity, domain drift, or precise experimental data."
+Q181,LexicalNotation,"Advanced adversarial stress-test question 181 involving notation ambiguity, domain drift, or precise experimental data."
+Q182,LexicalNotation,"Advanced adversarial stress-test question 182 involving notation ambiguity, domain drift, or precise experimental data."
+Q183,LexicalNotation,"Advanced adversarial stress-test question 183 involving notation ambiguity, domain drift, or precise experimental data."
+Q184,LexicalNotation,"Advanced adversarial stress-test question 184 involving notation ambiguity, domain drift, or precise experimental data."
+Q185,LexicalNotation,"Advanced adversarial stress-test question 185 involving notation ambiguity, domain drift, or precise experimental data."
+Q186,LexicalNotation,"Advanced adversarial stress-test question 186 involving notation ambiguity, domain drift, or precise experimental data."
+Q187,LexicalNotation,"Advanced adversarial stress-test question 187 involving notation ambiguity, domain drift, or precise experimental data."
+Q188,LexicalNotation,"Advanced adversarial stress-test question 188 involving notation ambiguity, domain drift, or precise experimental data."
+Q189,LexicalNotation,"Advanced adversarial stress-test question 189 involving notation ambiguity, domain drift, or precise experimental data."
+Q190,LexicalNotation,"Advanced adversarial stress-test question 190 involving notation ambiguity, domain drift, or precise experimental data."
+Q191,LexicalNotation,"Advanced adversarial stress-test question 191 involving notation ambiguity, domain drift, or precise experimental data."
+Q192,LexicalNotation,"Advanced adversarial stress-test question 192 involving notation ambiguity, domain drift, or precise experimental data."
+Q193,LexicalNotation,"Advanced adversarial stress-test question 193 involving notation ambiguity, domain drift, or precise experimental data."
+Q194,LexicalNotation,"Advanced adversarial stress-test question 194 involving notation ambiguity, domain drift, or precise experimental data."
+Q195,LexicalNotation,"Advanced adversarial stress-test question 195 involving notation ambiguity, domain drift, or precise experimental data."
+Q196,LexicalNotation,"Advanced adversarial stress-test question 196 involving notation ambiguity, domain drift, or precise experimental data."
+Q197,LexicalNotation,"Advanced adversarial stress-test question 197 involving notation ambiguity, domain drift, or precise experimental data."
+Q198,LexicalNotation,"Advanced adversarial stress-test question 198 involving notation ambiguity, domain drift, or precise experimental data."
+Q199,LexicalNotation,"Advanced adversarial stress-test question 199 involving notation ambiguity, domain drift, or precise experimental data."
+Q200,LexicalNotation,"Advanced adversarial stress-test question 200 involving notation ambiguity, domain drift, or precise experimental data."
+Q201,DomainEdgeCase,"Advanced adversarial stress-test question 201 involving notation ambiguity, domain drift, or precise experimental data."
+Q202,DomainEdgeCase,"Advanced adversarial stress-test question 202 involving notation ambiguity, domain drift, or precise experimental data."
+Q203,DomainEdgeCase,"Advanced adversarial stress-test question 203 involving notation ambiguity, domain drift, or precise experimental data."
+Q204,DomainEdgeCase,"Advanced adversarial stress-test question 204 involving notation ambiguity, domain drift, or precise experimental data."
+Q205,DomainEdgeCase,"Advanced adversarial stress-test question 205 involving notation ambiguity, domain drift, or precise experimental data."
+Q206,DomainEdgeCase,"Advanced adversarial stress-test question 206 involving notation ambiguity, domain drift, or precise experimental data."
+Q207,DomainEdgeCase,"Advanced adversarial stress-test question 207 involving notation ambiguity, domain drift, or precise experimental data."
+Q208,DomainEdgeCase,"Advanced adversarial stress-test question 208 involving notation ambiguity, domain drift, or precise experimental data."
+Q209,DomainEdgeCase,"Advanced adversarial stress-test question 209 involving notation ambiguity, domain drift, or precise experimental data."
+Q210,DomainEdgeCase,"Advanced adversarial stress-test question 210 involving notation ambiguity, domain drift, or precise experimental data."
+Q211,DomainEdgeCase,"Advanced adversarial stress-test question 211 involving notation ambiguity, domain drift, or precise experimental data."
+Q212,DomainEdgeCase,"Advanced adversarial stress-test question 212 involving notation ambiguity, domain drift, or precise experimental data."
+Q213,DomainEdgeCase,"Advanced adversarial stress-test question 213 involving notation ambiguity, domain drift, or precise experimental data."
+Q214,DomainEdgeCase,"Advanced adversarial stress-test question 214 involving notation ambiguity, domain drift, or precise experimental data."
+Q215,DomainEdgeCase,"Advanced adversarial stress-test question 215 involving notation ambiguity, domain drift, or precise experimental data."
+Q216,DomainEdgeCase,"Advanced adversarial stress-test question 216 involving notation ambiguity, domain drift, or precise experimental data."
+Q217,DomainEdgeCase,"Advanced adversarial stress-test question 217 involving notation ambiguity, domain drift, or precise experimental data."
+Q218,DomainEdgeCase,"Advanced adversarial stress-test question 218 involving notation ambiguity, domain drift, or precise experimental data."
+Q219,DomainEdgeCase,"Advanced adversarial stress-test question 219 involving notation ambiguity, domain drift, or precise experimental data."
+Q220,DomainEdgeCase,"Advanced adversarial stress-test question 220 involving notation ambiguity, domain drift, or precise experimental data."
+Q221,DomainEdgeCase,"Advanced adversarial stress-test question 221 involving notation ambiguity, domain drift, or precise experimental data."
+Q222,DomainEdgeCase,"Advanced adversarial stress-test question 222 involving notation ambiguity, domain drift, or precise experimental data."
+Q223,DomainEdgeCase,"Advanced adversarial stress-test question 223 involving notation ambiguity, domain drift, or precise experimental data."
+Q224,DomainEdgeCase,"Advanced adversarial stress-test question 224 involving notation ambiguity, domain drift, or precise experimental data."
+Q225,DomainEdgeCase,"Advanced adversarial stress-test question 225 involving notation ambiguity, domain drift, or precise experimental data."
+Q226,DomainEdgeCase,"Advanced adversarial stress-test question 226 involving notation ambiguity, domain drift, or precise experimental data."
+Q227,DomainEdgeCase,"Advanced adversarial stress-test question 227 involving notation ambiguity, domain drift, or precise experimental data."
+Q228,DomainEdgeCase,"Advanced adversarial stress-test question 228 involving notation ambiguity, domain drift, or precise experimental data."
+Q229,DomainEdgeCase,"Advanced adversarial stress-test question 229 involving notation ambiguity, domain drift, or precise experimental data."
+Q230,DomainEdgeCase,"Advanced adversarial stress-test question 230 involving notation ambiguity, domain drift, or precise experimental data."
+Q231,DomainEdgeCase,"Advanced adversarial stress-test question 231 involving notation ambiguity, domain drift, or precise experimental data."
+Q232,DomainEdgeCase,"Advanced adversarial stress-test question 232 involving notation ambiguity, domain drift, or precise experimental data."
+Q233,DomainEdgeCase,"Advanced adversarial stress-test question 233 involving notation ambiguity, domain drift, or precise experimental data."
+Q234,DomainEdgeCase,"Advanced adversarial stress-test question 234 involving notation ambiguity, domain drift, or precise experimental data."
+Q235,DomainEdgeCase,"Advanced adversarial stress-test question 235 involving notation ambiguity, domain drift, or precise experimental data."
+Q236,DomainEdgeCase,"Advanced adversarial stress-test question 236 involving notation ambiguity, domain drift, or precise experimental data."
+Q237,DomainEdgeCase,"Advanced adversarial stress-test question 237 involving notation ambiguity, domain drift, or precise experimental data."
+Q238,DomainEdgeCase,"Advanced adversarial stress-test question 238 involving notation ambiguity, domain drift, or precise experimental data."
+Q239,DomainEdgeCase,"Advanced adversarial stress-test question 239 involving notation ambiguity, domain drift, or precise experimental data."
+Q240,DomainEdgeCase,"Advanced adversarial stress-test question 240 involving notation ambiguity, domain drift, or precise experimental data."
+Q241,DomainEdgeCase,"Advanced adversarial stress-test question 241 involving notation ambiguity, domain drift, or precise experimental data."
+Q242,DomainEdgeCase,"Advanced adversarial stress-test question 242 involving notation ambiguity, domain drift, or precise experimental data."
+Q243,DomainEdgeCase,"Advanced adversarial stress-test question 243 involving notation ambiguity, domain drift, or precise experimental data."
+Q244,DomainEdgeCase,"Advanced adversarial stress-test question 244 involving notation ambiguity, domain drift, or precise experimental data."
+Q245,DomainEdgeCase,"Advanced adversarial stress-test question 245 involving notation ambiguity, domain drift, or precise experimental data."
+Q246,DomainEdgeCase,"Advanced adversarial stress-test question 246 involving notation ambiguity, domain drift, or precise experimental data."
+Q247,DomainEdgeCase,"Advanced adversarial stress-test question 247 involving notation ambiguity, domain drift, or precise experimental data."
+Q248,DomainEdgeCase,"Advanced adversarial stress-test question 248 involving notation ambiguity, domain drift, or precise experimental data."
+Q249,DomainEdgeCase,"Advanced adversarial stress-test question 249 involving notation ambiguity, domain drift, or precise experimental data."
+Q250,DomainEdgeCase,"Advanced adversarial stress-test question 250 involving notation ambiguity, domain drift, or precise experimental data."
+Q251,HighPrecisionData,"Advanced adversarial stress-test question 251 involving notation ambiguity, domain drift, or precise experimental data."
+Q252,HighPrecisionData,"Advanced adversarial stress-test question 252 involving notation ambiguity, domain drift, or precise experimental data."
+Q253,HighPrecisionData,"Advanced adversarial stress-test question 253 involving notation ambiguity, domain drift, or precise experimental data."
+Q254,HighPrecisionData,"Advanced adversarial stress-test question 254 involving notation ambiguity, domain drift, or precise experimental data."
+Q255,HighPrecisionData,"Advanced adversarial stress-test question 255 involving notation ambiguity, domain drift, or precise experimental data."
+Q256,HighPrecisionData,"Advanced adversarial stress-test question 256 involving notation ambiguity, domain drift, or precise experimental data."
+Q257,HighPrecisionData,"Advanced adversarial stress-test question 257 involving notation ambiguity, domain drift, or precise experimental data."
+Q258,HighPrecisionData,"Advanced adversarial stress-test question 258 involving notation ambiguity, domain drift, or precise experimental data."
+Q259,HighPrecisionData,"Advanced adversarial stress-test question 259 involving notation ambiguity, domain drift, or precise experimental data."
+Q260,HighPrecisionData,"Advanced adversarial stress-test question 260 involving notation ambiguity, domain drift, or precise experimental data."
+Q261,HighPrecisionData,"Advanced adversarial stress-test question 261 involving notation ambiguity, domain drift, or precise experimental data."
+Q262,HighPrecisionData,"Advanced adversarial stress-test question 262 involving notation ambiguity, domain drift, or precise experimental data."
+Q263,HighPrecisionData,"Advanced adversarial stress-test question 263 involving notation ambiguity, domain drift, or precise experimental data."
+Q264,HighPrecisionData,"Advanced adversarial stress-test question 264 involving notation ambiguity, domain drift, or precise experimental data."
+Q265,HighPrecisionData,"Advanced adversarial stress-test question 265 involving notation ambiguity, domain drift, or precise experimental data."
+Q266,HighPrecisionData,"Advanced adversarial stress-test question 266 involving notation ambiguity, domain drift, or precise experimental data."
+Q267,HighPrecisionData,"Advanced adversarial stress-test question 267 involving notation ambiguity, domain drift, or precise experimental data."
+Q268,HighPrecisionData,"Advanced adversarial stress-test question 268 involving notation ambiguity, domain drift, or precise experimental data."
+Q269,HighPrecisionData,"Advanced adversarial stress-test question 269 involving notation ambiguity, domain drift, or precise experimental data."
+Q270,HighPrecisionData,"Advanced adversarial stress-test question 270 involving notation ambiguity, domain drift, or precise experimental data."
+Q271,HighPrecisionData,"Advanced adversarial stress-test question 271 involving notation ambiguity, domain drift, or precise experimental data."
+Q272,HighPrecisionData,"Advanced adversarial stress-test question 272 involving notation ambiguity, domain drift, or precise experimental data."
+Q273,HighPrecisionData,"Advanced adversarial stress-test question 273 involving notation ambiguity, domain drift, or precise experimental data."
+Q274,HighPrecisionData,"Advanced adversarial stress-test question 274 involving notation ambiguity, domain drift, or precise experimental data."
+Q275,HighPrecisionData,"Advanced adversarial stress-test question 275 involving notation ambiguity, domain drift, or precise experimental data."
+Q276,HighPrecisionData,"Advanced adversarial stress-test question 276 involving notation ambiguity, domain drift, or precise experimental data."
+Q277,HighPrecisionData,"Advanced adversarial stress-test question 277 involving notation ambiguity, domain drift, or precise experimental data."
+Q278,HighPrecisionData,"Advanced adversarial stress-test question 278 involving notation ambiguity, domain drift, or precise experimental data."
+Q279,HighPrecisionData,"Advanced adversarial stress-test question 279 involving notation ambiguity, domain drift, or precise experimental data."
+Q280,HighPrecisionData,"Advanced adversarial stress-test question 280 involving notation ambiguity, domain drift, or precise experimental data."
+Q281,HighPrecisionData,"Advanced adversarial stress-test question 281 involving notation ambiguity, domain drift, or precise experimental data."
+Q282,HighPrecisionData,"Advanced adversarial stress-test question 282 involving notation ambiguity, domain drift, or precise experimental data."
+Q283,HighPrecisionData,"Advanced adversarial stress-test question 283 involving notation ambiguity, domain drift, or precise experimental data."
+Q284,HighPrecisionData,"Advanced adversarial stress-test question 284 involving notation ambiguity, domain drift, or precise experimental data."
+Q285,HighPrecisionData,"Advanced adversarial stress-test question 285 involving notation ambiguity, domain drift, or precise experimental data."
+Q286,HighPrecisionData,"Advanced adversarial stress-test question 286 involving notation ambiguity, domain drift, or precise experimental data."
+Q287,HighPrecisionData,"Advanced adversarial stress-test question 287 involving notation ambiguity, domain drift, or precise experimental data."
+Q288,HighPrecisionData,"Advanced adversarial stress-test question 288 involving notation ambiguity, domain drift, or precise experimental data."
+Q289,HighPrecisionData,"Advanced adversarial stress-test question 289 involving notation ambiguity, domain drift, or precise experimental data."
+Q290,HighPrecisionData,"Advanced adversarial stress-test question 290 involving notation ambiguity, domain drift, or precise experimental data."
+Q291,HighPrecisionData,"Advanced adversarial stress-test question 291 involving notation ambiguity, domain drift, or precise experimental data."
+Q292,HighPrecisionData,"Advanced adversarial stress-test question 292 involving notation ambiguity, domain drift, or precise experimental data."
+Q293,HighPrecisionData,"Advanced adversarial stress-test question 293 involving notation ambiguity, domain drift, or precise experimental data."
+Q294,HighPrecisionData,"Advanced adversarial stress-test question 294 involving notation ambiguity, domain drift, or precise experimental data."
+Q295,HighPrecisionData,"Advanced adversarial stress-test question 295 involving notation ambiguity, domain drift, or precise experimental data."
+Q296,HighPrecisionData,"Advanced adversarial stress-test question 296 involving notation ambiguity, domain drift, or precise experimental data."
+Q297,HighPrecisionData,"Advanced adversarial stress-test question 297 involving notation ambiguity, domain drift, or precise experimental data."
+Q298,HighPrecisionData,"Advanced adversarial stress-test question 298 involving notation ambiguity, domain drift, or precise experimental data."
+Q299,HighPrecisionData,"Advanced adversarial stress-test question 299 involving notation ambiguity, domain drift, or precise experimental data."
+Q300,HighPrecisionData,"Advanced adversarial stress-test question 300 involving notation ambiguity, domain drift, or precise experimental data."