Using pre‐ and postexploitation samples to assess the impact of commercial whaling on the genetic characteristics of eastern North Pacific gray and humpback whales and to compare methods used to infer historic demography
Bibliographic record
Abstract
Abstract Many species of whales went through recent bottlenecks due to commercial whaling. These declines were rapid and recent relative to the life spans and generation times of these species, raising questions regarding to what degree commercial whaling influenced the genetic characteristics of these populations. We analyzed mitochondrial and nuclear DNA from pre‐ and postwhaling samples from two populations that have arguably shown the greatest degree of recovery: eastern North Pacific gray and humpback whales. We also compare the performance of different methods to test for historic bottlenecks and infer past demography based on genetic data. We found substantially higher levels of genetic diversity in gray than in humpback whales (for both time periods), likely due to recent connectivity between Atlantic and Pacific gray whale populations. Other than mitochondrial diversity in humpback whales, levels of diversity were not lower in contemporary samples relative to prewhaling samples, indicating that commercial whaling had a minimal impact on metrics of genetic diversity themselves. However, it did have large impacts on the patterns of diversity, as evidenced by all coalescent‐based methods showing clear evidence of a bottleneck for both populations, whereas all but one method not based on the coalescent failed to detect a bottleneck.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".