| ---
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| title: PHBV Mass-Loss Reliability Explorer
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| emoji: 🧪
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| colorFrom: blue
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| colorTo: green
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| sdk: static
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| app_file: index.html
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| pinned: false
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| license: mit
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| short_description: Study-aware PHBV mass-loss prediction
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| ---
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| # PHBV Mass-Loss Reliability Explorer
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| This public research-use Space predicts degradation-induced specimen mass/weight loss (%) for PHBV profiles represented by the released modelling corpus. It provides single-time-point and trajectory modes, 0–100% bounds, a positive monotonic constraint on degradation time, marginal range/category warnings, and a conservative post-selection residual band. Inference runs locally in the browser from an exported XGBoost tree ensemble; no prediction server is required.
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| ## Scientific boundary
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| The output is **gravimetric mass loss**, not ultimate biodegradation, mineralisation, carbon conversion, environmental safety, or compliance with a biodegradability standard. Mass loss can include disintegration, recovery loss, leaching, and biological conversion.
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| ## Validation
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| - Evidence base: 1,467 observations from 129 curves in 16 source studies.
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| - Primary research assessment: nested leave-one-study-out validation with study-grouped inner tuning.
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| - Primary macro-study MAE: 23.554 percentage points (95% study-bootstrap CI 17.421–30.251).
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| - Point MAE/RMSE/$R^2$: 22.061 / 30.483 / 0.145.
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| - Random-row XGBoost $R^2$ was 0.915, but this is an optimistic interpolation benchmark because related time points cross the split.
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| - Nominal 90% post-selection grouped residual intervals covered 97.6% overall, but their mean width was 91.9 percentage points and one held-out study had only 37.5% coverage. The residual-producing inner folds also informed hyperparameter selection; this is not independent conformal calibration.
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| These results do not support high-stakes or study-independent certification use. The interface reports the strict grouped evidence rather than deployment-training fit.
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| ## Intended use
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| Use the tool to explore hypotheses, inspect plausible bounded trajectory shapes, identify marginal out-of-range inputs, and prioritise experiments. Marginal checks do not establish joint plausibility; unvalidated combinations are exploratory. Do not use the tool to replace laboratory testing, make regulatory or safety decisions, or infer causal effects of a formulation or environment.
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| ## Model
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| The model is an XGBoost regressor trained on a bounded-logit target with explicit 0–100% clipping, a positive monotonic constraint on degradation time, and equal total training weight per study. Three raw training responses slightly above 100% were clipped for bounded fitting. Categorical variables are one-hot encoded with unknown levels ignored; numeric missingness is median-imputed inside the fitted preprocessing pipeline. The interactive model is fitted on all 16 studies after the research evaluation was completed. The source table is additive-enriched and inherited global temperature completion for 210 rows, category coarsening, and zero filling of unreported additive amounts.
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| ## Version and licensing scope
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| - Model export: `phbv-bm-xgb-2026-07-26-v1`
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| - Release date: 26 July 2026
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| - The Space's MIT declaration covers the interface code and project-authored packaging. It does not relicense the source article, workbook, CSV, or notebooks in `origin_data/`; source rights remain with their holders and source artifacts are not uploaded to this Space.
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| ## Citation
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| The source data were compiled by Kotzabasaki et al. (2026), *Polymers* 18, 897: https://doi.org/10.3390/polym18070897. Cite the companion reliability-analysis manuscript and archived code/data release when their final bibliographic records are available.
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