Why are PFOS ecological surface water quality criteria so different between countries? A review of differences in regulatory guidance
Bibliographic record
Abstract
Ecological surface water quality criteria (SWQC) for perfluorooctane sulfonic acid (PFOS) vary several orders of magnitude between jurisdictions. Such differences can undermine confidence in the SWQC and their scientific basis. The current study undertakes a sensitivity analysis to investigate the factors that drive the differences observed in the PFOS SWQC published by the United States, Australia, and Canada. Each jurisdiction follows a broadly similar three-step procedure when deriving SWQC: (1) selecting reliable ecotoxicological data from the literature (Variable 1, Study Selection); (2) extracting a suite of values that are protective of individual aquatic taxa (Variable 2, Data Reduction); and (3) deriving a final singular value that is protective of aquatic ecosystems (Variable 3, SWQC Derivation). We found substantial differences between the studies deemed reliable in each jurisdiction (Variable 1). Applying each jurisdiction's data reduction and SWQC derivation procedures (Variables 2 and 3) to the other jurisdictions' datasets showed generally comparable outcomes, except for Australia. Aspects of Australia's data reduction and SWQC derivation approach were unique and resulted in materially lower (i.e., greater than an order of magnitude difference) SWQC values. We suggest clarification of the scientific rationale behind the decision making for difference-driving steps and greater alignment between jurisdictions, based on sound scientific reasoning, to increase regulatory consistency and transparency and decrease overall uncertainty in promulgated SWQC.
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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 | 0.060 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".