How does social behavior differ among sperm whale clans?
Bibliographic record
Abstract
Abstract When individuals primarily associate with and learn from those who behave similarly, society and culture become closely tied. Sperm whales (Physeter macrocephalus) exhibit multilevel social structure, the levels of which are differentiated in part by characteristic cultural behaviors. Sperm whales are organized into sympatric clans, with distinctive vocal repertoires that are socially learned. Other behaviors, such as movement patterns and foraging, also differ among clans. Here we ask whether the clan partition also includes divergences in social behavior. Off the Galápagos Islands, members of two clans differed consistently in diving synchrony, heterogeneity, and temporal stability of social relationships. While number of associates (indicated by social unit, group, and cluster sizes) were similar between clans, Regular clan members dived more synchronously and had more homogeneous relationships than the Plus‐One clan members. Plus‐One social units had generally longer associations than those of the Regular clan. Differences in surface‐time coordination and quality of social relationships are likely byproducts of the clan segregation, which could affect alloparental care giving, therefore scaling up to differential calf survival rates between clans. This new dimension of behavioral divergence between sperm whale clans indicates that sympatric, socio‐cultural entities of nonhumans can also display characteristic social behavior.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".