Circumpolar Trends of PCBs and Organochlorine Pesticides in the Arctic Marine Environment Inferred from Levels in Ringed Seals
Bibliographic record
Abstract
Geographical trends in levels of ΣPCB 10 (sum of 10 major congeners), hexachlorocyclohexanes (HCH), ΣDDT (sum of DDT-related compounds), and other persistent organochlorines (OCs) in ringed seal blubber were examined at 13 sampling locations in the Arctic over 175 deg longitude from northern Canada to the South Kara Sea (Yenisey Gulf) in Russia. Concentrations of OCs were adjusted, using analysis of covariance, for effects of the covariates, sex, age, and blubber thickness. Adjusted mean concentrations of ΣPCB 10 and ΣDDT were significantly higher in the samples from the Yenisey Gulf in the Russian Arctic, Svalbard, and East Greenland than in west Greenland or the Canadian Arctic. ΣPCB 10 and ΣDDT in Yenisey Gulf samples were 8× and 6× higher, respectively, than the average in levels from four Canadian locations. ΣPCB 10 and ΣDDT means declined significantly with increasing westerly longitude ( r 2 = 0.75 and 0.73, respectively). ΣHCH levels for sites in the Canadian Arctic were significantly higher than those from west Greenland (Qeqertarsuaq), east Greenland (Ittoqqortoormiit), and Svalbard and increased significantly from east to west. The geographical trend ΣHCH is in general agreement with observed trends of HCH in seawater where higher levels have been found in the Canadian Arctic. Higher proportions of more recalcitrant hexa- and pentachloro-PCB congeners were observed in seal blubber samples in the European/Russian Arctic. The continued use of PCBs in electrical equipment and other applications in Russia is a likely source of the more highly chlorinated congeners.
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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 | 0.001 | 0.001 |
| 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 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".