Assessment of persistent organic pollutants in killer whales (Orcinus orca) of the Canadian Arctic: Implications for subsistence consumption and conservation strategies
Bibliographic record
Abstract
Killer whales (Orcinus orca) historically restricted to certain Arctic regions due to extensive sea ice have recently been documented farther north and for longer durations in the Canadian Arctic. These apex predators accumulate high levels of persistent organic pollutants (POPs). The objective of this study was to evaluate the concentrations and profiles of POPs in killer whales of the Canadian Arctic, thus determining potential risks for Inuit communities if consumed. Biopsies were collected from 33 killer whales across areas of the Canadian Arctic between 2009 and 2021. Significant variability in POP concentrations was observed among whales. The cumulative POP concentrations ranged from 12 to >2270 mg/kg lw, representing ∼200-fold increase from the least to the most contaminated individual. The rank order of concentrations of the top five contaminant classes was ∑DDT, ∑PCB, ∑CHL, ∑Toxaphene, and Dieldrin. Several emerging Arctic contaminants were detected, including chlorpyrifos, endosulfan, pentachloroanisole, and polychlorinated naphthalenes, although at relatively lower concentrations than legacy POPs. Considering the elevated blubber POP levels in killer whales, recommended daily consumption thresholds, established based on human tolerable daily intake (TDI) values, were notably restricted for ∑PCB (<0.14 g), ∑DDT (<6.9 g), ∑CHL (<13 g), dieldrin (<8 g) and heptachlor epoxide (<5 g). Killer whales in the Canadian Arctic exhibited higher POP concentrations than other commonly hunted species such as polar bears, ringed seals, and Arctic char. We acknowledge that a more holistic risk assessment of diet is required to assess the cumulative impacts of contaminant mixtures as well as nutritional quality of tissues commonly consumed by northern communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".