Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria
Bibliographic record
Abstract
Traditional toxicity testing cannot keep pace with the rapid growth of synthetic chemicals, creating major data gaps that hinder the development of water quality criteria (WQC) for emerging contaminants. This study developed a machine learning model integrating compound- and organism-related features to enable cross-compound and cross-species toxicity prediction. The model demonstrated strong robustness and generalization, outperforming the Interspecies Correlation Estimation application in cross-species prediction, particularly across large taxonomic distances. SHAP analysis identified water solubility and lipophilicity as dominant predictors, with organism-related features also contributing substantially. The model predicted the acute toxicity of 30 representative per- and polyfluoroalkyl substances (PFAS) across 181 aquatic species. Habitat-informed species selection was then used to derive ecologically relevant 5% hazardous concentrations (HC 5 ), which were generally higher in saltwater than in freshwater. Cross-regional comparisons further indicated that salinity may modulate fish sensitivity to PFAS. HC 5 estimates for China were higher than those for North America and Europe, potentially reflecting inter-regional differences in species sensitivity, with Chinese species appearing comparatively more tolerant. Finally, site-specific WQC for perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) were derived for the Great Lakes using predicted sensitivities of 76 dominant native species, providing greater ecological relevance than existing criteria.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".