Sperm whales exhibit variation in echolocation tactics with depth and sea state but not naval sonar exposures
Bibliographic record
Abstract
Abstract Auditory masking by anthropogenic noise may impact marine mammals relying on sound for important life functions, including echolocation. Animals have evolved antimasking strategies, but they may not be completely effective or cost‐free. We formulated seven a priori hypotheses on how odontocete echolocation behavior could indicate masking. We addressed six of them using data from 15 tagged sperm whales subject to experimental exposures of pulsed and continuous active sonar (PAS and CAS). Sea state, received single‐pulse sound exposure level (SELsp), whale depth and orientation towards surface, and sonar were considered as candidate covariates representing different masking conditions. Echolocation behavior, including buzz duration and search range, varied strongly with depth. After controlling for depth and angle to the surface, the likelihood of buzzing following a click train decreased with sea state (t = −7.3, p < .001). There was little evidence for changes in 10 tested variables with increasing sonar SELsp, except reduced buzzing consistent with previously reported feeding cessation (t = −2.26, p = .02). A potential Lombard effect was detected during echolocation with sea state and SELsp, despite off‐axis measurement and right‐hand censoring due to acoustic clipping. The results are not conclusive on masking effects on sperm whale echolocation, highlighting challenges and opportunities for future anthropogenic masking studies.
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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".