Worth the dip? Polar bear predation on swimming flightless greater snow geese and estimation of energetic efficiency
Bibliographic record
Abstract
As sea-ice cover is shrinking, polar bears ( Ursus maritimus, Phipps, 1774) face decreased access to seals, their primary prey, resulting in a greater dependence on terrestrial food sources. Whether polar bears can benefit from these terrestrial food sources, however, depends on their ability to find and capture prey items without expending more energy than is acquired. Here, we report one of the northernmost observations of polar bear predation on adult birds. The bear used a dive-hunting technique, which consisted of submerging itself, approaching underwater, and catching flightless greater snow geese ( Anser caerulescens caerulescens (Linnaeus, 1758)) from beneath the surface of a tundra pond. After evaluating energy expenditures during swimming and energy intakes from consuming geese, we estimated that this rarely documented dive-hunting technique could be energetically profitable for a certain range of pursuit durations. This observation highlights the behavioral plasticity that polar bears can deploy to punctually exploit land-based food sources.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".