Data from: Icing-related injuries in polar bears (Ursus maritimus) at high latitudes
Bibliographic record
Abstract
Climate change has broad ecological implications for wildlife, especially for species that rely on temperature-sensitive habitats. For polar bears (Ursus maritimus), loss of Arctic sea ice reduces access to prey and lengthens seasonal fasting periods leading to behavioral, nutritional, and reproductive impacts that may result in population declines. Secondary factors, such as disease and contaminants can exacerbate primary stressors and new health-related conditions are likely to emerge. For example, once unusual but now increasingly frequent warming cycles are creating unprecedented icing conditions that have demographic consequences for cold-adapted mammals. We report on icing-related lesions observed in wild polar bears during live-capture research in two high-latitude subpopulations, Kane Basin (KB) and East Greenland (EG), between 2012 and 2022. We observed ice build-up, hair loss (alopecia), and skin ulcerations primarily affecting the feet of adult bears as well as other parts of the body. The most severely affected individuals had blocks of ice up to 30 cm in diameter adhered to the foot pads, deep, bleeding ulcerations of foot pads and exhibited lameness. These injuries have not been observed during previous research in these areas or reported in the scientific literature, suggesting this may be a new phenomenon. To provide context for our observations, we conducted interviews with Indigenous polar bear subsistence hunters in West and East Greenland and Nunavut to document Indigenous knowledge about the potential causes and frequency of these injuries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".