Correlation of Mercury Occurrence with Age, Elemental Composition, and Life History in Sea-Run Food Fish from the Canadian Arctic Archipelago’s Lower Northwest Passage
Bibliographic record
Abstract
As mercury emissions continue and climate-mediated permafrost thaw increases the burden of this contaminant in northern waters, Inuit from a Northwest passage community in the Canadian Arctic Archipelago pressed for an assessment of their subsistence catches. Sea-run salmonids (n = 537) comprising Arctic char (Salvelinus alpinus), lake trout (S. namaycush), lake whitefish (Coregonus clupeaformis), and cisco (C. autumnalis, C. sardinella) were analyzed for muscle mercury. Methylmercury is a neurotoxin and bioaccumulated with fish age, but other factors including selenium and other elements, diet and trophic level as assessed by stable isotopes of nitrogen (δ15N) and carbon (δ13C), as well as growth rate, condition, and geographic origin, also contributed depending on the species, even though all the fish shared a similar anadromous or sea-run life history. Although mean mercury concentrations for most of the species were ~0.09 µg·g−1 wet weight (ww), below the levels described in several jurisdictions for subsistence fisheries (0.2 µg·g−1 ww), 70% of lake trout were above this guideline (0.35 µg·g−1 ww), and 19% exceeded the 2.5-fold higher levels for commercial sale. We thus urge the development of consumption advisories for lake trout for the protection of pregnant women and young children and that additionally, periodic community-based monitoring be initiated.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".