Windscapes and olfactory foraging among polar bears (Ursus maritimus)
Bibliographic record
Abstract
Understanding strategies for maximizing foraging efficiency is central to behavioural ecology. The theoretical optimal olfactory search is crosswind, however empirical evidence of anemotaxis (orientation to wind) among carnivores is sparse. Polar bear (Ursus maritimus) is a sea ice dependent species that relies on olfaction to locate prey. We examined adult female polar bear movement data, corrected for sea ice drift, from Hudson Bay, Canada, in relation to modelled winds to examine olfactory search. The predicted crosswind movement was most frequent at night during winter, when most hunting occurs. Movement was predominantly downwind during fast winds (>10 m/s), which impede olfaction. Migration during freeze-up and break-up also was correlated with wind. Lack of orientation during summer, a period with few food resources, reflects energy conservation and reduced active search. We suggest windscapes be used as a habitat feature in habitat selection models by changing what is considered available habitat. The presented methods are widely applicable to olfactory predators (e.g., canids, felids, and mustelids) and prey avoiding predators. These findings represent the first known quantitative description of anemotaxis for olfactory foraging for any large carnivore.
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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".