Detecting genetic structure in migrating bowhead whales off the coast of Barrow, Alaska
Bibliographic record
Abstract
We develop a general framework for analysing and testing genetic structure within a migratory assemblage that is based on measures of genetic differences between individuals. We demonstrate this method using microsatellite DNA data from the Bering-Chukchi-Beaufort stock of bowhead whales (Balaena mysticetus), sampled via Inuit hunting during the spring and autumn migration off Barrow, Alaska. This study includes a number of covariates such as whale ages and the time separation between captures. Applying the method to a sample of 117 bowhead whales, we use permutation methods to test for temporal trends in genetic differences that can be ascribed to age-related effects or to timing of catches during the seasons. The results reveal a pattern with elevated genetic differences among whales caught about a week apart, and are statistically significant for the autumn migration. In contrast, we find no effects of time of birth or age-difference on genetic differences. We discuss possible explanations for the results, including population substructuring, demographic consequences of historical overexploitation, and social structuring during migration.
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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 | 0.001 | 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) | 0.000 | 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".