Identification of Arctic Food Fish Species for Anthropogenic Contaminant Testing Using Geography and Genetics
Bibliographic record
Abstract
The identification of food fish bearing anthropogenic contaminants is one of many priorities for Indigenous peoples living in the Arctic. Mercury (Hg), arsenic (As), and persistent organic pollutants including polychlorinated biphenyls (PCBs) are of concern, and these are reported, in some cases for the first time, for fish sampled in and around King William Island, located in Nunavut, Canada. More than 500 salmonids, comprising Arctic char, lake trout, lake whitefish, and ciscoes, were assayed for contaminants. The studied species are anadromous, migrating to the ocean to feed in the summers and returning to freshwater before sea ice formation in the autumn. Assessments of muscle Hg levels in salmonids from fishing sites on King William Island showed generally higher levels than from mainland sites, with mean concentrations generally below guidelines, except for lake trout. In contrast, mainland fish showed higher means for As, including non-toxic arsenobetaine, than island fish. Lake trout were highest in As and PCB levels, with salmonid PCB congener analysis showing signatures consistent with the legacy of cold-war distant early warning stations. After DNA-profiling, only 4-32 Arctic char single nucleotide polymorphisms were needed for successful population assignment. These results support our objective to demonstrate that genomic tools could facilitate efficient and cost-effective cluster assignment for contaminant analysis during ocean residency. We further suggest that routine pollutant testing during the current period of dramatic climate change would be helpful to safeguard the wellbeing of Inuit who depend on these fish as a staple input to their diet. Moreover, this strategy should be applicable elsewhere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".