Acoustic pingers to mitigate dolphin bycatch and depredation, barely a one third octave between them.
Bibliographic record
Abstract
Bycatch mitigation alarms/pingers for dolphins/porpoises and whales are developed in the mid 1980s in Japan and Canada, respectively. Alarms are attached to nets so the acoustic warning is associated with the obstruction, avoided, and the behavior reinforced via associative learning. A continuous association between alarms (the warning) and a net (the obstruction) is essential. What constitutes an appropriate alarm is not fully understood but should result in reduced entanglement in fishery conditions, irrespective of the mammals behavior observed by human observers. Dolphins are rarely deterred with bycatch pingers; they are alerted if they were inattentive but often maintain a close association with nets. Both bycatch and depredation may be reduced. These puzzling results should be investigated. At the other end of the pinger cline are the pingers that move dolphins from the vicinity of nets or lines to mitigate depredation, and by default, bycatch. The mechanisms are not known how these pingers can be successful at sound pressure levels comparable to dolphin whistles yet the results are becoming clear. The avoidance behavior from these pinger types should be investigated with captive animals. Interactive pingers and net material/reflectors with higher target strength are also seen as other important developments.
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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 | 0.000 | 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) | 0.008 | 0.002 |
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".