Developing Hazard Rating Calculation Methodologies for Per- and Polyfluoroalkyl Substances
Bibliographic record
Abstract
Historical and present-day use of per- and polyfluoroalkyl substances (PFAS) have been linked to environmental and human health impacts that prove challenging to address. Therefore, prioritization of PFAS management based on observed or predicted toxicological properties and environmental fate is critical in the development of effective risk management practices. Hazard rating calculations use a range of literature-derived quantitative data to identify and rank the potential risk posed by a contaminant of concern associated with an activity or land use. The present study describes the use of hazard rating calculation methodologies to evaluate PFAS at federally owned properties across Canada. The hazard rating calculations assess potential site impacts from the use of PFAS, including application of PFAS-containing aqueous film-forming foam (AFFF) at fire-fighting training areas (FFTAs). Eleven PFAS were evaluated based on their prevalence or use in AFFFs and the availability of established chemical data. The hazard rating evaluated 4 properties: human health, environmental fate, deleterious quantity, and release and impact modifier. In the present study, hazard ratings calculated for perfluorohexane sulfonate, perfluoroheptanoate, and 8:2 fluorotelomer sulfonate were greater than for the remaining evaluated PFAS. This hazard rating system is beneficial for selecting and/or developing site management or remediation strategies. The methodology supports a comprehensive, site-scale approach for prioritizing PFAS management, and can be integrated into future regulatory framework and contaminant prioritization. Environ Toxicol Chem 2021;40:937-946. © 2021 SETAC.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".