Post-conflict movements of polar bears in western Hudson Bay, Canada
Bibliographic record
Abstract
Human–carnivore conflicts have increased as habitat has been affected by development and climate change. Understanding how biological factors, environment, and management decisions affect the behaviour of animals may reduce conflicts. We examined how biological factors, sea ice conditions, and management decisions affected the autumn migratory movement of polar bears ( Ursus maritimus Phipps, 1774) from 2016 to 2021 following their capture near Churchill, Manitoba, Canada, and release after a mean of 20 days (SE 2) in a holding facility. We deployed eartag satellite transmitters on 63 bears (26 males, 37 females), with 49% adults (>5 years old), 48% subadults (3–5 year old), and 3% <2-year old. We compared variation in on-ice departure of bears released post-conflict (conflict) to adult females without a conflict history (non-conflict). Conflict bears departed 89 km further north (mean = 59.7°N, SE 0.2) of non-conflict bears (mean = 58.9°N, SE 0.1). Bears released later during the migratory period were less likely to re-enter a community at a rate of 5.9%–6.4% per day. Of 69 releases (6 individuals requiring multiple releases), 12 bears re-entered Churchill and 13 entered Arviat, Nunavut. We suggest that the holding facility was effective at preventing additional conflicts and individuals with a high likelihood of recidivism should be held longer.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".