Use of Subsistence-Harvested Whale Carcasses by Polar Bears in the Southern Beaufort Sea
Bibliographic record
Abstract
The availability of a food subsidy has the potential to influence the condition, behavior, fitness, and population dynamics of a species. Since the early 2000s, monitoring efforts along the coast of northern Alaska have indicated a higher proportion of polar bears (Ursus maritimus) of the southern Beaufort Sea (SB) subpopulation coming onshore to feed on subsistence-harvested bowhead whale (Balaena mysticetus) carcasses during the fall and early winter seasons. Concurrently, Indigenous communities annually hunt bowhead whale and deposit the unused remains at localized “bone piles,” creating the potential for human-bear interactions. Our objective was to determine the annual number of polar bears feeding at the bone pile near Kaktovik, Alaska. Using a hair snag surrounding the bone pile, we collected hair samples to identify individual bears via microsatellite genotypes during 2011 – 14. We used capture-mark-recapture data in the POPAN open-population model to estimate the number of bears visiting the bone pile. We estimated that as many as 72 (SE = 9) and 76 (SE = 10) male and female polar bears, respectively, used the bone pile located at Kaktovik, Alaska, in 2012, which represents approximately 16% of the SB polar bear subpopulation. It will be important to monitor the number of bears using the bone pile and subsequent human-bear interactions and conflicts along the northern coast of Alaska, if sea ice continues to recede.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".