Variation in behavioural responses of sub-tropical marine fishes to experimental longline capture
Bibliographic record
Abstract
Abstract Fishes are often caught as bycatch on longlines and subsequently discarded. The behavioural response of fishes to longline capture is poorly understood, although it may be linked to the magnitude of the physiological stress response, and, ultimately, contribute to stress-induced mortality. We used accelerometers, video cameras, and hook timers to analyse the behavioural response of 13 subtropical teleost and elasmobranch species to experimental longline capture in The Bahamas. We found that, across all species and species groups, fight intensity during a capture event was best described by a negative linear and positive quadratic response. Nurse sharks and tiger sharks had lower fight intensity values and exhibited less steepness in their quadratic response during the first 10 min of capture than other species, particularly blacktip and Caribbean reef sharks. Nurse sharks also exhibited the most consistent fight intensity during the entire capture event compared to other shark species, particularly the blacknose shark. Generally, obligate ram ventilators and mixed ventilators exhibited higher steepness in fight intensity trajectories than buccal/spiracular pumpers, which had more consistent, lower fight intensity values. Behavioural responses to longline capture are species specific but may be linked to distinct evolutionary traits such as respiratory mode.
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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) | 0.001 | 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".