Mutagenic hazards of complex polycyclic aromatic hydrocarbon mixtures in contaminated soil
Bibliographic record
Abstract
The objective of the present study was to evaluate hazard/risk assessment methods for complex environmental mixtures that involve a targeted, priority chemical approach based on the cumulative hazard/risk of known mixture components or analyses of sufficiently similar mixtures. Ten polycyclic aromatic hydrocarbon (PAH)-contaminated soils were separated into nonpolar and semipolar fractions, and both fractions elicited positive responses on the Salmonella reverse mutation assay. Targeted and nontargeted methods of hazard prediction routinely overestimated mutagenic activities for the nonpolar soil fractions, suggesting nonadditive interactions of PAHs in complex mixtures. This suggests that current risk assessment methods for complex mixtures may provide conservative estimates regarding soils contaminated with priority PAHs alone. Significant underestimations of total risk, however, will be obtained if the soils also contain unidentified PAHs as well as polycyclic aromatic compounds and related compounds that contribute to the total mutagenic activity. Furthermore, estimates of excess lifetime cancer risk associated with the nondietary ingestion of the PAH-contaminated soils studied here indicate that a traditional risk assessment model based on identified priority PAHs and an assumption of additivity generally underestimates the risk associated with the nonpolar soil fractions (in comparison to bioassay-derived risk estimates). Additional cancer risk may be associated with the more polar compounds that also are found at these contaminated sites and that rarely are included in the standard risk assessment methodology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".