Historical and potential future importance of large whales as food for polar bears
Bibliographic record
Abstract
Polar bears (Ursus maritimus) are expected to be adversely impacted by a warming Arctic due to melting of the sea‐ice platform from which they hunt ice‐breeding seals. We evaluated the hypothesis that scavenging on stranded large whale carcasses may have facilitated polar bear survival through past interglacial periods during which sea‐ice was limited by analyzing: (1) present‐day scavenging by polar bears on large whale carcasses; (2) energy values of large whale species; and (3) the ability of polar bears, like the brown bears (Ursus arctos) from which they evolved, to quickly store large amounts of lipids and to fast for extended periods. We concluded that scavenging on large whale carcasses likely facilitated survival of polar bears in past interglacial periods when access to seals was reduced. In a future, ice‐impoverished Arctic, whale carcasses are less likely to provide nutritional refuge for polar bears because overharvesting by humans has greatly reduced large whale populations, carcass availability is geographically limited, and climate‐induced sea‐ice loss is projected to occur at a more rapid pace than polar bears have experienced at any previous time in their evolutionary history.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".