Legacy contaminants in the eastern Beaufort Sea beluga whales (<i>Delphinapterus leucas</i>): are temporal trends reflecting regulations?
Bibliographic record
Abstract
Once deposited onto Arctic ecosystems, persistent organic pollutants (POPs) biomagnify in foodwebs such that relatively high levels have been detected in predators like beluga whales (Delphinapterus leucas). Our study aimed at assessing temporal trends of legacy POPs in eastern Beaufort Sea beluga blubber collected during traditional harvests in the Mackenzie Estuary area, Northwest Territories. Concentrations of polychlorinated biphenyls (PCBs) and 14 pesticides were quantified in 185 blubber samples collected between 1989 and 2015. The majority of legacy POPs analyzed showed no significant changes during the study period (ΣPCBs, Σchlordanes, Σdichlorodiphenyltrichloroethane, hexachlorobenzene, dieldrin, and mirex) and, therefore, did not reflect regulations put into place over the past decades. Although α- (82.6 ± 12.6 and 15.9 ± 1.9 ng/g in 1989 and 2015, respectively) and γ-hexachlorocyclohexane (γ-HCH) (126.6 ± 23.9 and 13.9 ± 0.9 ng/g, respectively) showed significant decrease between 1989 and 2015, β-HCH showed a more complex trend with concentrations increasing between 1989 (71.2 ± 13.4 ng/g) and 2004 (276.8 ± 13.5 ng/g) before decreasing until 2015 (174.7 ± 8.3 ng/g). Differences in trends likely reflect physico-chemical properties affecting transport to the Arctic. With climate change and melting sea ice potentially affecting the transport and release of legacy POPs, continuous monitoring is necessary.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".