Anthropogenic food: an emerging threat to polar bears
Bibliographic record
Abstract
Abstract Supplemental food from anthropogenic sources is a source of conflict with humans for many wildlife species. Food-seeking behaviours by black bears Ursus americanus and brown bears Ursus arctos can lead to property damage, human injury and mortality of the offending bears. Such conflicts are a well-known conservation management issue wherever people live in bear habitats. In contrast, the use of anthropogenic foods by the polar bear Ursus maritimus is less common historically but is a growing conservation and management issue across the Arctic. Here we present six case studies that illustrate how negative food-related interactions between humans and polar bears can become either chronic or ephemeral and unpredictable. Our examination suggests that attractants are an increasing problem, exacerbated by climate change-driven sea-ice losses that cause increased use of terrestrial habitats by bears. Growing human populations and increased human visitation increase the likelihood of human–polar bear conflict. Efforts to reduce food conditioning in polar bears include attractant management, proactive planning and adequate resources for northern communities to reduce conflicts and improve human safety. Permanent removal of unsecured sources of nutrition, to reduce food conditioning, should begin immediately at the local level as this will help to reduce polar bear mortality.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".