Reliable Prediction of the Octanol–Air Partition Ratio
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Bibliographic record
Abstract
Abstract The octanol–air equilibrium partition ratio (KOA) is frequently used to describe the volatility of organic chemicals, whereby n-octanol serves as a substitute for a variety of organic phases ranging from organic matter in atmospheric particles and soils, to biological tissues such as plant foliage, fat, blood, and milk, and to polymeric sorbents. Because measured KOA values exist for just over 500 compounds, most of which are nonpolar halogenated aromatics, there is a need for tools that can reliably predict this parameter for a wide range of organic molecules, ideally at different temperatures. The ability of five techniques, specifically polyparameter linear free energy relationships (ppLFERs) with either experimental or predicted solute descriptors, EPISuite's KOAWIN, COSMOtherm, and OPERA, to predict the KOA of organic substances, either at 25 °C or at any temperature, was assessed by comparison with all KOA values measured to date. In addition, three different ppLFER equations for KOA were evaluated, and a new modified equation is proposed. A technique's performance was quantified with the mean absolute error (MAE), the root mean square error (RMSE), and the estimated uncertainty of future predicted values, that is, the prediction interval. We also considered each model's applicability domain and accessibility. With an RMSE of 0.37 and a MAE of 0.23 for predictions of log KOA at 25 °C and RMSE of 0.32 and MAE of 0.21 for predictions made at any temperature, the ppLFER equation using experimental solute descriptors predicted the KOA the best. Even if solute descriptors must be predicted in the absence of experimental values, ppLFERs are the preferred method, also because they are easy to use and freely available. Environ Toxicol Chem 2021;40:3166–3180. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it