Behavioral Responses to Underwater Noise
Bibliographic record
Abstract
Abstract This chapter presents an overview of physical and acoustic behavioral responses of marine mammals to underwater sounds. A literature review was undertaken, and data on received levels at the animal when certain types of responses were observed were compiled in an online supplementary spreadsheet. Based on this, an overview of responses was written, organized first by species and then by sound type. In-air and underwater sound sources were considered. The most studied sound types were mid-frequency sonar and acoustic deterrent devices and then impact pile driving and vessels. The most frequently reported response was avoidance, followed by changes in swim speed and surface-respiration-dive behavior. However, no response was the second most common observation, after avoidance. Easily accessible coastal or captive species (i.e., harbor porpoises, bottlenose dolphins, and humpback whales) have been comparatively well studied. There has been great variability in study design, response observation and classification, received level derivation and unit, as well as (statistical) analyses. Given the different environments, contexts, populations, and individuals that have been studied, it is not surprising that great variability has been reported in minimum received levels at which behavioral responses were observed, more than 50 dB for the most studied species and sound type combinations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".