What to eat now? Shifts in polar bear diet during the ice‐free season in western Hudson Bay
Bibliographic record
Abstract
Under current climate trends, spring ice breakup in Hudson Bay is advancing rapidly, leaving polar bears (Ursus maritimus) less time to hunt seals during the spring when they accumulate the majority of their annual fat reserves. For this reason, foods that polar bears consume during the ice-free season may become increasingly important in alleviating nutritional stress from lost seal hunting opportunities. Defining how the terrestrial diet might have changed since the onset of rapid climate change is an important step in understanding how polar bears may be reacting to climate change. We characterized the current terrestrial diet of polar bears in western Hudson Bay by evaluating the contents of passively sampled scat and comparing it to a similar study conducted 40 years ago. While the two terrestrial diets broadly overlap, polar bears currently appear to be exploiting increasingly abundant resources such as caribou (Rangifer tarandus) and snow geese (Chen caerulescens caerulescens) and newly available resources such as eggs. This opportunistic shift is similar to the diet mixing strategy common among other Arctic predators and bear species. We discuss whether the observed diet shift is solely a response to a nutritional stress or is an expression of plastic foraging behavior.
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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 | 0.001 |
| 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".