Novel Bayesian Method to Derive Final Adjusted Values of Physicochemical Properties: Application to 74 Compounds
Bibliographic record
Abstract
Accurate values of physicochemical properties are essential for screening semivolatile organic compounds for human and environmental hazard and risk. In silico approaches for estimation are widely used, but the accuracy of these and measured values can be difficult to ascertain. Final adjusted values (FAVs) harmonize literature-reported measurements to ensure consistency and minimize uncertainty. We propose a workflow, including a novel Bayesian approach, for estimating FAVs that combines measurements using direct and indirect methods and in silico values. The workflow was applied to 74 compounds across nine classes to generate recommended FAVs (FAV R s). Estimates generated by in silico methods (OPERA, COSMOtherm, EPI Suite, SPARC, and polyparameter linear free energy relationships (pp-LFER) models) differed by orders of magnitude for some properties and compounds and performed systematically worse for larger, more polar compounds. COSMOtherm and OPERA generally performed well with low bias although no single in silico method performed best across all compound classes and properties. Indirect measurement methods produced highly accurate and precise estimates compared with direct measurement methods. Our Bayesian method harmonized measured and in silico estimated physicochemical properties without introducing observable biases. We thus recommend use of the FAV R s presented here and that the proposed Bayesian workflow be used to generate FAV R s for SVOCs beyond those in this study.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".