Beluga whale, Delphinapterus leucas, vocalizations and their relation to behaviour in the Churchill River, Manitoba, Canada
Bibliographic record
Abstract
The investigation of a species’ repertoire and the contexts in which different calls are used is central to understanding vocal communication among animals. Beluga whale, Delphinapterus leucas, calls were classified and described in association with behaviours, from recordings collected in the Churchill River, Manitoba, during the summers of 2006-2008. Calls were subjectively classified based on sound and visual analysis into whistles (64.2% of total calls; 22 call types), pulsed or noisy calls (25.9%; 15 call types), and combined calls (9.9%; seven types). A hierarchical cluster analysis, using six call measurements as variables, separated whistles into 12 groups and results were compared to subjective classification. Beluga calls associated with social interactions, travelling, feeding, and interactions with the boat were described. Call type percentages, relative proportions of different whistle contours (shapes), average frequency, and call duration varied with behaviour. Generally, higher percentages of whistles, more broadband pulsed and noisy calls, and shorter calls (<0.49s) were produced during behaviours associated with higher levels of activity and/or apparent arousal. Information on call types, call characteristics, and behavioural context of calls can be used for automated detection and classification methods and in future studies on call meaning and function.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".