Property Estimation of Per- and Polyfluoroalkyl Substances: A Comparative Assessment of Estimation Methods
Bibliographic record
Abstract
To accurately predict the environmental fate of per- and polyfluoroalkyl substances (PFAS), high-quality physicochemical property data are required. Because such data are often not available from experiments, assessment of the accuracy of existing property estimation models is essential. The quality of predicted physicochemical property data for a set of 25 PFAS was examined using COSMOtherm, EPI Suite, the estimation models accessible through the US Environmental Protection Agency's CompTox Chemicals Dashboard, and Linear Solvation Energy Relationships (LSERs) available through the UFZ-LSER Database. The results showed that COSMOtherm made the most accurate acid dissociation constant and air-water partition ratio estimates compared with literature data. The OPEn structure-activity/property Relationship App (OPERA; developed through the CompTox Chemicals Dashboard) estimates of vapor pressure and dry octanol-air partition ratios were the most accurate compared with other models of interest. Wet octanol-water partition ratios were comparably predicted by OPERA and EPI Suite, and the organic carbon soil coefficient and solubility were well predicted by OPERA and COSMOtherm. Acid dissociation of the perfluoroalkyl acids has a significant impact on their physicochemical properties, and corrections for ionization were included where applicable. Environ Toxicol Chem 2020;39:775-786. © 2020 SETAC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".