Choose Your Poison—Space-Use Strategy Influences Pollutant Exposure in Barents Sea Polar Bears
Bibliographic record
Abstract
Variation in space-use is common within mammal populations. In polar bears, Ursus maritimus, some individuals follow the sea ice (offshore bears) whereas others remain nearshore yearlong (coastal bears). We studied pollutant exposure in relation to space-use patterns (offshore vs coastal) in adult female polar bears from the Barents Sea equipped with satellite collars (2000-2014, n = 152). First, we examined the differences in home range (HR) size and position, body condition, and diet proxies (nitrogen and carbon stable isotopes, n = 116) between offshore and coastal space-use. Second, we investigated how HR, space-use, body condition, and diet were related to plasma concentrations of polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs) ( n = 113), perfluoroalkyl substances (PFASs; n = 92), and hydroxylated-PCBs ( n = 109). Offshore females were in better condition and had a more specialized diet than did coastal females. PCBs, OCPs, and hydroxylated-PCB concentrations were not related to space-use strategy, yet PCB concentrations increased with increasing latitude, and hydroxylated-PCB concentrations were positively related to HR size. PFAS concentrations were 30-35% higher in offshore bears compared to coastal bears and also increased eastward. On the basis of the results we conclude that space-use of Barents Sea female polar bears influences their pollutant exposure, in particular plasma concentrations of PFAS.
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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 | 0.001 |
| 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".