Genetic profiling links changing sea-ice to shifting beluga whale migration patterns
Bibliographic record
Abstract
There is increasing concern over how Arctic fauna will adapt to climate related changes in sea-ice. We used long-term sighting and genetic data on beluga whales ( Delphinapterus leucas ) in conjunction with multi-decadal patterns of sea-ice in the Pacific Arctic to investigate the influence of sea-ice on spring migration and summer residency patterns. Substantial variations in sea-ice conditions were detected across seasons, years and sub-regions, revealing ice–ocean dynamics more complex than Arctic-wide trends suggest. This variation contrasted with a highly consistent pattern of migration and residency by several populations, indicating that belugas can accommodate widely varying sea-ice conditions to perpetuate philopatry to coastal migration destinations. However, a number of anomalous migration and residency events were detected and coincided with anomalous ice years, and in one case with an increase in killer whale ( Orcinus orca ) sightings and reported predation on beluga whales. The behavioural shifts were likely driven by changing sea-ice and associated changes in resource dispersion and predation risk. Continued reductions in sea-ice may result in increased predation at key aggregation areas and shifts in beluga whale behaviour with implications for population viability, ecosystem structure and the subsistence cultures that rely on them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".