A follow-up geospatial analysis and probabilistic human health risk assessment of methylmercury in fish in Eeyou Istchee (Québec, Canada)
Bibliographic record
Abstract
The Cree Peoples of the Eeyou Istchee territory (northern Québec, Canada) rely on fish as a part of their traditional and contemporary diet. Fish is a culturally significant food and a source of nutrients, but it is also the main pathway of methylmercury (MeHg) exposure for humans. Significant hydroelectric developments in this territory are responsible for increasing the concentrations of MeHg found in fish and thus increase the human exposure to this neurotoxic compound. As this is an ongoing issue, our study assessed the current MeHg fish-tissue concentrations in Eeyou Istchee and the spatial distribution of MeHg hot spots using Geographic Information Systems (GIS) to compare our results to those found in previous studies from the same region. We also performed a probabilistic hazard assessment of the exposure to MeHg from fish consumption. The GIS models indicated significant clustering of increased MeHg fish-tissue concentrations around hydroelectric reservoirs and showed higher MeHg fish-tissue concentrations around newer hydroelectric reservoirs, but a decrease in older reservoirs. Similar to past studies, we found that fish consumption continues to pose an MeHg exposure hazard for men who consume large piscivore species (i.e., lake trout, walleye, and pike), while for women, lake trout and walleye consumption constitute a hazard (any size), and pike should be consumed with caution. The hazard of exposure was mainly associated with intake rate in all cases. Lastly, we recommend monitoring MeHg fish-tissue concentrations in this region, as the MeHg tissue concentrations remain elevated, and updated consumption guidelines where and when necessitated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".