Varying Diet Composition Causes Striking Differences in Legacy and Emerging Contaminant Concentrations in Killer Whales across the North Atlantic
Bibliographic record
Abstract
) feeding on marine mammals compared to those consuming fish. Advancements in dietary studies include the use of quantitative fatty acid signature analysis (QFASA) and differentiation of feeding habits within and between populations of North Atlantic (NA) killer whales. This comprehensive study assessed the concentrations of legacy and emerging POPs in 162 killer whales from across the NA. We report significantly higher mean levels of polychlorinated biphenyls (PCBs), organochlorine pesticides, and flame retardants in Western NA killer whales compared to those of Eastern NA conspecifics. Mean ∑PCBs ranged from ∼100 mg/kg lipid weight (lw) in the Western NA (Canadian Arctic, Eastern Canada) to ∼50 mg/kg lw in the mid-NA (Greenland, Iceland) to ∼10 mg/kg lw in the Eastern NA (Norway, Faroe Islands). The observed variations in contaminant levels were strongly correlated with diet composition across locations (inferred from QFASA), emphasizing that diet and not environmental variation in contaminant concentrations among locations is crucial in assessing contaminant-associated health risks in killer whales. These findings highlight the urgency for implementing enhanced measures to safely dispose of POP-contaminated waste, prevent further environmental contamination, and mitigate the release of newer and potentially harmful contaminants.
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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) | 0.000 | 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) | 0.001 | 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".