Polar Bear Conservation in a Period of Arctic Warming
Bibliographic record
Abstract
Polar bear conservation faces significant challenges under Arctic warming, especially with respect to habitat loss and the resulting impacts on their seasonal energetic uptake and maintenance. Polar bears rely on sea ice for hunting, mating, denning, and rearing of offspring, and the availability of ice, both spatially and temporally, influences their fitness and survival. The research collected in this thesis includes an assessment of the global polar bear population, identifying gaps in the knowledge, and presenting a model linking polar bear density to prey diversity, providing estimates for missing subpopulations. The majority of subpopulations are found to be vulnerable to continued Arctic warming based on decadal-scale changes to sea ice and population size. A sea ice projection model for the Canadian Arctic Archipelago provided the means to estimate how sea ice degradation and loss may affect polar bears through the 21st century. Projections suggest that, without curbing greenhouse gas emissions, ice conditions in the Archipelago will shift away from a multi-year sea ice regime, and lengthening ice-free conditions will harm polar bear reproductive success and increase starvation rates. An analysis of movement patterns of adult and subadult, males and female polar bears, in the southern Beaufort Sea suggests that the ice-free season is associated with higher movement rates, thus greater demands on energy stores during a season that is expected to get longer with future warming. An assessment of time and space use of harvest risk areas derived from historical harvest locations found that subadult males were more often in risk areas than other age and sex classes, although they avoided the highest risk areas. Landfast ice in the low-risk areas was decreasing faster over time, with the possibility to concentrate polar bears into areas of higher risk to harvest under continued Arctic warming.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".