Adaptation of sperm whales to open-boat whalers: rapid social learning on a large scale?
Bibliographic record
Abstract
Animals can mitigate human threats, but how do they do this, and how fast can they adapt? Hunting sperm whales was a major nineteenth century industry. Analysis of data from digitized logbooks of American whalers in the North Pacific found that the rate at which whalers succeeded in harpooning ('striking') sighted whales fell by about 58% over the first few years of exploitation in a region. This decline cannot be explained by the earliest whalers being more competent, as their strike rates outside the North Pacific, where whaling had a longer history, were not elevated. The initial killing of particularly vulnerable individuals would not have produced the observed rapid decline in strike rate. It appears that whales swiftly learned effective defensive behaviour. Sperm whales live in kin-based social units. Our models show that social learning, in which naive social units, when confronted by whalers, learned defensive measures from grouped social units with experience, could lead to the documented rapid decline in strike rate. This rapid, large-scale adoption of new behaviour enlarges our concept of the spatio-temporal dynamics of non-human culture.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".