Implications of grouping per‐ and polyfluoroalkyl substances for contaminated site regulation
Bibliographic record
Abstract
Abstract Per‐ and polyfluoroalkyl substances (PFAS) are a broad group of persistent organic compounds with vastly differing physicochemical and toxicological properties. Some jurisdictions have proposed to regulate PFAS as a single class to overcome the limitations of regulating such a diverse group on a chemical‐by‐chemical basis. Implications of regulating PFAS as a single class have been discussed for PFAS production and use, but equivalent discussion of implications for managing contaminated sites is largely lacking. This opinion piece summarizes the views of a group of environmental consultants, environmental regulators, land managers, and academics with significant experience in researching or managing PFAS. The group considered that neither a single PFAS class approach nor a chemical‐by‐chemical approach is well suited to managing risks from PFAS in a contaminated site setting, and defining PFAS subgroups would have value. Second, some but not all in the group, hypothesize that PFAS properties that drive fate and transport are those that influence toxicity and bioaccumulation in animals. This may be a valuable observation for future discussions on dividing PFAS into subclasses for contaminated site regulation based on physicochemical properties rather than purely structural definitions.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| 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".