State of knowledge on current exposure, fate and potential health effects of contaminants in polar bears from the circumpolar Arctic
Bibliographic record
Abstract
The polar bear (Ursus maritimus) is among the Arctic species exposed to the highest concentrations of long-range transported bioaccumulative contaminants, such as halogenated organic compounds and mercury. Contaminant exposure is considered to be one of the largest threats to polar bears after the loss of their Arctic sea ice habitat due to climate change. The aim of this review is to provide a comprehensive summary of current exposure, fate, and potential health effects of contaminants in polar bears from the circumpolar Arctic required by the Circumpolar Action Plan for polar bear conservation. Overall results suggest that legacy persistent organic pollutants (POPs) including polychlorinated biphenyls, chlordanes and perfluorooctane sulfonic acid (PFOS), followed by other perfluoroalkyl compounds (e.g. carboxylic acids, PFCAs) and brominated flame retardants, are still the main compounds in polar bears. Concentrations of several legacy POPs that have been banned for decades in most parts of the world have generally declined in polar bears. Current spatial trends of contaminants vary widely between compounds and recent studies suggest increased concentrations of both POPs and PFCAs in certain subpopulations. Correlative field studies, supported by in vitro studies, suggest that contaminant exposure disrupts circulating levels of thyroid hormones and lipid metabolism, and alters neurochemistry in polar bears. Additionally, field and in vitro studies and risk assessments indicate the potential for adverse impacts to polar bear immune functions from exposure to certain 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".