Why Do Organochlorine Differences between Arctic Regions Vary among Trophic Levels?
Bibliographic record
Abstract
Statistical analysis of organochlorine contaminants (OCs) in marine mammals has shown that, for most OCs, the European Arctic is more contaminated than the Canadian and U.S. Arctic. Recently, comparison of OC concentration ranges in seabirds, arctic cod (Boregadus saida), and zooplankton, found no difference between these regions. To address these inconsistencies, marine food web OC data from the European (central Barents Sea (CBS)) and Canadian Arctic (Northwater Polynya (NOW)) were simultaneously statistically analyzed. In general, concentrations of OCs were greater in seabirds and ringed seals (Phoca hispida) from the CBS as compared to the NOW; consistent with circumpolar trends observed in marine mammals. In contrast, levels of OCs were generally similar in zooplankton and arctic cod between the CBS and NOW. The main exception is HCH which had greater levels in the NOW across all trophic levels because of the greater proximity to sources in eastern Asia. The lack of differences in OC concentrations in zooplankton and Arctic cod from the European and Canadian Arctic suggest that regional differences in OC contamination in the Arctic have evened out. Reduced regional differences were not observed in marine mammals or seabirds because they are long-lived and also acquire contaminants from maternal transfer and hence reflect levels from the past when the European Arctic was more contaminated than the Canadian Arctic. In addition, seabirds may reflect exposure from other areas. This study highlights the potential problem of comparing spatial trends by using means and confidence intervals as compared to simultaneous statistical analysis of raw data. Differences in the spatial trends of OCs between trophic levels in the Arctic are important for consideration when assessing regional differences in spatial and temporal trends of discontinued and current-use 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".