Natural soundscape variation appears to influence the structure of stereotypic calls and repertoires in killer whales
Bibliographic record
Abstract
Underwater soundscapes show dynamic variations in natural ambient sound levels at different locations and water depths and levels can vary over a course of a day. This soundscape dynamicity may pose a vocal challenge for highly mobile marine mammals whose habitat overlaps with many dynamic soundscapes. The most prominent communication tool of killer whales is a pulsated often multicomponent call which can be detected at more than 15 kilometers under quiet conditions. Social learning of call structure is a driver for stereotypic long lasting group- or population-specific call repertoires. Here we describe the potential vocal adaptations of killer whales to reduce the influence of sound propagation loss (PL) on their calls. Reliable propagation to identify groups and populations at considerable distances is important. In experimental field trials, the PL of killer whale calls and that of tones and sweeps was examined to determine if call component PL differs among soundscapes. PL is positively correlated with spatial and temporal ambient noise level variation. Furthermore, the use of burst pulses, a common feature of calls increased propagation distances at higher noise levels. We conclude that noise variation is one of the drivers of call structure and may also influence the call repertoire size.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".