Quantifying uncertainty in predicted chemical partition ratios required for chemical assessments
Bibliographic record
Abstract
) partition ratios. Upper and lower limits on PC property predictions are proposed based on theory, data, and applications of the properties in hazard screening and risk assessment. Validations of the uncertainty metrics of the QSPR packages are done for the PC properties using experimental data external to all training datasets. The IFSQSAR 95% prediction interval (PI95) calculated from root mean squared error of prediction (RMSEP) captures 90% of the external data, while OPERA and EPI Suite require a factor increase of at least 4 and 2 respectively for their PI95 to capture a similar 90% of the external experimental data. The assessment of QSPR consensus predictions identified future research and experimental testing to improve the predictive models for data-poor chemicals such as polyfluorinated or per-fluorinated alkyl substances (PFAS), ionizable chemicals, and chemicals with complex and multifunctional structures.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".