Derivation of an ambient water quality criterion for mercury: Taking account of site-specific conditions
Bibliographic record
Abstract
Mercury is considered to be a serious risk to wildlife. As a result, the Great Lakes Water Quality Initiative and others have developed ambient water quality criteria (AWQC) for the protection of wildlife. These AWQC have been controversial, however, because the AWQC were single values that did not account for site-specific conditions, derivation of the AWQC relied on a single no-observed-adverse-effect level, and the AWQC had an unknown level of conservatism because of reliance on both average and conservative assumptions and uncertainty factors. Rather than develop a single-value AWQC for total mercury, we derived an AWQC model that explicitly incorporates factors controlling bioavailability, methylation rates, and bioaccumulation in the aquatic environment (e.g., pH, dissolved organic carbon). To derive our AWQC model, field data were collected from 31 lakes in Ontario and an additional 10 lakes in Nova Scotia (North America). In the field study, levels of total and methylmercury in water and fish as well as levels of key water quality variables were determined. We conducted multiple-regression analysis to derive a model that estimates mercury levels in prey of mink. Mink are very sensitive to mercury exposure. An independent dataset consisting of 51 water bodies in the United States was then used to confirm the validity and robustness of the AWQC model. Next, we combined the results of chronic-feeding studies with similar protocols and endpoints in a meta-analysis to derive a dose-response curve for mink exposed to mercury in the diet. In the final step, we used a probabilistic risk model to estimate the concentrations of methylmercury in water that would lead to levels in fish sufficient for a 10% probability of exceeding the lethal dose affecting 5% of the mink population. The result is an AWQC equation for mercury for the protection of wildlife that can be used with a variety of site-specific conditions.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".