Masking escape in killer whales, stereotypical call design as a noise impact reduction strategy
Bibliographic record
Abstract
Killer whale call repertoires appear to remain relatively stable over generations because call design is considered socially transmitted between generations. Transmission errors during learning may be responsible for new call designs and repertoire size increases. Call and repertoire divergence may also be the result of adaptation to noisy environments. Temporal and spatial ambient noise variation occurs naturally in killer whale habitats and spans across the auditory frequency ranges of calls. Call components that vary in peak energy across frequency bands appear to escape auditory noise masking because peak amplitude variation per frequency is part of the stereotypical design of calls. As a result level variations in the noise spectrum rather than broadband noise dose may be the more relevant noise impact metrics for calls. Killer whale call design and repertoire divergence appears to reflect the naturally occurring variability in propagation of different frequencies. This may have led to repertoire variation among different populations allowing them to explore different acoustic niches. Presented are preliminary results from an ongoing study into call design influences on signal propagation under varying ambient noise conditions at different times of the year and different geographic locations and water depths within resident and Bigg’s killer whale habitat.
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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 | 0.001 |
| 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".