Bowhead whale Balaena mysticetus seasonal selection of sea ice
Bibliographic record
Abstract
Highly mobile large-bodied organisms are adapted to seasonal variation associated with polar environments.We used satellite tracking data from 27 bowhead whales Balaena mysticetus of the Eastern Canada-West Greenland population to test for movement and habitat selection of the highly variable sea ice landscape that encompasses near-complete coverage in winter to nearcomplete absence in summer.We demarcated 2 bowhead whale seasons based on movement behaviour identified from inflection points of polynomial regression analysis of movement rate: winter (28 December to 15 March, 16.6 ± 2.65 km d -1 ) and summer (27 June to 27 December, 31.9 ± 1.05 km d -1 ).Resource selection functions were used to evaluate bowhead whale seasonal selection of sea ice landscape (coverage, thickness, and floe size).Movement and habitat use differed between Nunavut tagging sites likely as a consequence of sexual and reproductive segregation.Whales selected relatively low ice coverage, thin ice, and small floe areas in winter close to the maximum ice extent, presumably to reduce risk of ice entrapment while remaining within ice.In contrast, whales selected high ice coverage, thick ice, and large floe size areas in summer, presumably to reduce risk of killer whale predation while providing enriched feeding opportunities.Our results indicate that this largebodied animal can moderate use of the large-scale fluctuations in seasonal sea ice typical of polar environments.
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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".