Aseasonal Migration of a Northern Bottlenose Whale Provides Support for the Skin Molt Migration Hypothesis
Bibliographic record
Abstract
ABSTRACT Why animals migrate is a fundamental question in biology. While the adaptive significance of some animal migrations is well understood (e.g., to find food, to pursue more‐favorable habitats, to spawn, or to give birth), others remain unknown. The adaptive significance of whale migration, for example, is unresolved and multiple hypotheses have been proposed to explain it. One recently proposed hypothesis that challenges the long‐standing “feeding‐breeding” whale migration model is a “feeding‐molting” model, where whales undertake latitudinal migrations to warmer waters to molt skin. In July 2019, we attached satellite‐tracking tags to northern bottlenose whales ( Hyperoodon ampullatus ) in the Canadian Arctic. One of these tagged whales completed a round‐trip movement between the Arctic and the temperate western North Atlantic, traveling 7281 km in 67 days (and spanning 27° of latitude). The whale was tagged in sea‐surface temperatures of ~4°C, but migrated south, reaching ~23°C surface waters, where it remained for 7 days before returning to the Arctic. The whale's occupancy of warm water was accompanied by a distinct shift in dive behavior, remaining near the ocean's surface. Four other tagged whales initiated similar long‐distance movements. We conclude that feeding or breeding were unlikely reasons for this movement and that northern bottlenose whales migrate to warmer latitudes to molt skin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".