False killer whale (Pseudorca crassidens) echolocation and acoustic disruption: implications for longline bycatch and depredation
Bibliographic record
Abstract
False killer whales ( Pseudorca crassidens (Owen, 1846)) depredate fish caught by the North Pacific pelagic longline fishery, resulting in loss of target species catch and the whales themselves becoming bycaught. This incidental take of false killer whales exceeds sustainable levels. In an effort to address a potential solution to reducing this depredation and bycatch, we tested an acoustic device designed to deter false killer whales from approaching longlines by reducing the whales’ echolocation performance capabilities. The device produced a series of complex, broadband signals (1–250 kHz) at high intensity levels (up to 182 dB). In the experiment, a trained false killer whale was asked to detect a target in the presence or absence of the acoustic device. Baseline performance capabilities were 95% correct responses. Initially, the device reduced the whale’s echolocation performance to chance levels. However, subsequent sessions demonstrated improvement in echolocation performance up to 85%. This improvement was likely a result of behaviorally adapting to the task and a decrease in the source level of the echolocation “disruptor”. The results underscore the challenges in using acoustic devices to reduce depredation and bycatch, and demonstrate the need for concern regarding anthropogenic noise levels and effects on odontocete echolocation capabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".