Using a geographic information system to assess local scale methylmercury exposure from fish in nine communities of the Eeyou Istchee territory (James Bay, Quebec, Canada)
Bibliographic record
Abstract
Exposure to methylmercury is a concern for those who rely on fish as a traditional food in the Eeyou Istchee territory of James Bay, Quebec, Canada, because industrial land uses overlap with community water bodies where fish are harvested. Consequently, this study assessed if traditional practices, particularly fishing, increased the risk of exposure to methylmercury from the consumption of locally harvested fish. We designed a geographic information system (GIS) that included land use and fish methylmercury tissue concentrations to assess clustering of potential hot spots. We also used generalized linear models to assess the association of fish consumption to blood organic-mercury concentrations, and logistic regression models to assess the probability of fish exceeding the safety threshold for methylmercury tissue concentrations in areas of high intensity land use. The GIS demonstrated significant clustered hot spots around regions of hydroelectric and mining land use. Our results also revealed that adult consumption of pike, lake trout and/or walleye, and child consumption of pike or walleye were significantly associated with blood organic-mercury concentrations. Further, large fish harvested in a community with high intensity land use yielded a 77% probability that the fish exceeded the safety threshold. From a human exposure perspective, our study highlights the need for further research on children who consume fish from this region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".