Time Trends in Fish Tissue Methylmercury in Northern Watersheds: Implications of Phosphorus Loading and Eutrophication on Subsistence Fisheries
Bibliographic record
Abstract
Subsistence fisheries for Michipicoten First Nation (MFN) in habitats across an area north of Lake Superior in Ontario, Canada were assessed. This assessment used reports by Ontario, private entities (e.g., mines), and MFN to evaluate contaminant concentrations in fishes from the 1980s to 2021; methylmercury was determined to be the contaminant of primary concern in fish tissues. Methylmercury tissue concentrations for varied fish species from four lakes and one river were used to establish contaminant-fish length relationships. Observed methylmercury tissue concentrations for these fishes allowed for the creation of updated consumption recommendations in MFN’s subsistence fisheries. This study recommended updated consumption rates for fish species including Northern Pike (Esox lucius), Walleye (Sander vitreus), Lake Trout (Salvelinus namaycush), Burbot (Lota lota), Lake Whitefish (Coregonus clupeaformis), White Sucker (Catostomus commersoni), Longnose Sucker (Catostomus catostomus), and introduced Smallmouth Bass (Micropterus dolomieu). Elevated methylmercury concentrations followed increased eutrophication in these naturally oligotrophic watersheds from loading of plant nutrients, from both diffuse and defined regional sources. Nutrient mitigation measures to control in situ methylmercury production cannot be implemented as neither the nature or extent of past or current nutrient loading from various sources has been identified or estimated in the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".