Vocal sharing and individual acoustic distinctiveness within a group of captive orcas (Orcinus orca).
Bibliographic record
Abstract
Among vocal learners, some animal species are known to develop individually distinctive vocalizations, and others clearly learn to produce group signatures. The optimal vocal sharing hypothesis suggests that vocal divergence and convergence are not compulsorily exclusive and both can be found at different levels in a given species. Being individually recognizable is socially important even in species sharing vocal badges. Acoustic divergence is not systematically controlled as it can simply be due to interindividual morphological differences. We tested that hypothesis in a species known to learn their family vocal dialect socially: the orca (Orcinus orca). We identified 13 different call types, including some shared by all group members, some shared only by 2 or 3 individuals, and others particular to 1 individual. Sharing was higher between males than between females. Three of our 4 orcas each produced a unique call type, which was preferably emitted. The call types shared by all orcas still presented individual acoustic distinctiveness that could, to some degree, be explained by morphological differences. We found evidence for strong similarities between some of the call types of our captive orcas and the call types of their ancestors, which are Canadian and Icelandic free-ranging orcas. Our findings suggest that captive orcas use a complex vocal repertoire enabling each individual to produce sounds that are similar to some of their partners', which might be used as social badges to advertise their preferential bonds, as well as individual-specific calls. Our findings open new lines of research concerning the functional value of a balanced "diverging-converging" vocal system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".