Risk versus reward: interactions, depredation rates, and bycatch mitigation of dolphins in demersal fish trawls
Bibliographic record
Abstract
An improved understanding of interaction dynamics between dolphins and trawlers is essential for improving bycatch mitigation strategies. In-situ observations using video at increasing distances from the net opening during 50 commercial fish trawls, recorded 5908 common bottlenose dolphin (Tursiops truncatus (Montagu, 1821)) interactions and provided details on their duration, depredation rates, and behaviours. Dolphin interactions with trawls were very common (98% of day-trawls, 118 ± 16 interactions per trawl), with durations and prey consumption positively correlated with the distances ventured into the net. Acoustic deterrents (pingers) had no effect on interaction numbers or durations. Based on in-situ observations, the factors that contribute toward dolphin bycatch in demersal fish trawls were likely associated with (i) risky dolphin behaviour (i.e., entering net during hauling, residing deep within trawl for extended periods or social aggression) and (or) (ii) instability of fishing gear resulting in entrapment. Given the high level of dolphin attendance during most day-trawls, mitigation strategies that focus on improving and monitoring the stability of trawl gear would be more effective than current acoustic deterrent devices aimed at modifying dolphin behaviour.
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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 | 0.001 | 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) | 0.001 | 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".