Unraveling the genetic legacy of commercial whaling in bowhead whales and narwhals
Bibliographic record
Abstract
Abstract Commercial whaling decimated many whale populations over several centuries. Bowhead whales ( Balaena mysticetus ) and narwhal ( Monodon monoceros ) have similar habitat requirements and are often seen together in the Canadian Arctic. Although their ranges overlap extensively, bowhead whales experienced significantly greater whaling pressure than narwhals. The different harvest histories but similar habitat requirements of these two species provide an opportunity to examine the demographic and genetic consequences of commercial whaling. We whole-genome resequenced Canadian Arctic bowhead whales and narwhals to delineate population structure and reconstruct demographic history. Bowhead whale effective population size sharply declined contemporaneously with the intense commercial whaling period. Narwhals instead exhibited recent growth in effective population size, reflecting limited opportunistic commercial harvest. Although the genetic diversity of bowhead whales and narwhals was similar, bowhead whales had more genetic diversity prior to commercial whaling and will likely continue to experience significant genetic drift in the future. In contrast, narwhals appear to have had long-term low genetic diversity and may not be at imminent risk of the consequences of the erosion of genetic diversity. This work highlights the importance of considering population trajectories in addition to genetic diversity when assessing the genetics of populations for conservation and management purposes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".