Whistle sequences in wild killer whales (<i>Orcinus orca</i>)
Bibliographic record
Abstract
Combining different stereotyped vocal signals into specific sequences increases the range of information that can be transferred between individuals. The temporal emission pattern and the behavioral context of vocal sequences have been described in detail for a variety of birds and mammals. Yet, in cetaceans, the study of vocal sequences is just in its infancy. Here, we provide a detailed analysis of sequences of stereotyped whistles in killer whales off Vancouver Island, British Columbia. A total of 1140 whistle transitions in 192 whistle sequences recorded from resident killer whales were analyzed using common spectrographic analysis techniques. In addition to the stereotyped whistles described by Riesch et al., [(2006). "Stability and group specificity of stereotyped whistles in resident killer whales, Orcinus orca, off British Columbia," Anim. Behav. 71, 79-91.] We found a new and rare stereotyped whistle (W7) as well as two whistle elements, which are closely linked to whistle sequences: (1) stammers and (2) bridge elements. Furthermore, the frequency of occurrence of 12 different stereotyped whistle types within the sequences was not randomly distributed and the transition patterns between whistles were also nonrandom. Finally, whistle sequences were closely tied to close-range behavioral interactions (in particular among males). Hence, we conclude that whistle sequences in wild killer whales are complex signal series and propose that they are most likely emitted by single individuals.
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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 | 0.000 |
| 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 | 0.000 |
| 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".