Data from: The diffusion of cooperative and solo bubble net feeding in Canadian Pacific humpback whales
Bibliographic record
Abstract
Animal culture, in which information and behaviours are acquired and shared through social networks by social learning, is a form of biodiversity with intrinsic and practical value. Cooperative foraging, a mutualistic resource acquisition behaviour observed across diverse taxa, is strongly connected to social networks via behavioural states, cues, and often social learning, as it typically involves high interaction rates. Understanding the distribution, diffusion and learning mechanisms of such cooperative behaviours is an important but understudied aspect of nonhuman culture. Bubble net feeding (‘bubble netting’) is a specialised foraging technique practised by certain humpback whale (Megaptera novaeangliae) populations globally. Over 20 years in the northern Canadian Pacific, we observed the diffusion of two forms: social cooperative and independent, or ‘solo’, bubble netting. Network-based diffusion analysis – a tool to test for social learning – finds strong evidence for social learning of bubble netting when the overall social network is used, even after accounting for traits such as site fidelity and sex (10.6 x 103 to 35.4 x 103 times more support for social versus asocial learning; p < 0.0001). A homophily check using pre-acquisition association data returned ambiguous results, likely due to the inherent sociality of this cooperative foraging behaviour. Nonetheless, the rapid diffusion of bubble netting is clearly important for population viability and should inform conservation planning for this threatened population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".