Drivers of at-vessel mortality of the blue shark (<i>Prionace glauca</i>) and oceanic whitetip shark (<i>Carcharhinus longimanus</i>) assessed from monitored pelagic longline experiments
Bibliographic record
Abstract
Elasmobranchs make up a significant part of bycatch in pelagic longline fisheries, whose induced mortality can be a major threat to endangered species. It is therefore crucial to understand the drivers of at-vessel mortality (AVM) for this fishing gear to enhance postrelease survival. To this end, we analysed scientific data collected during monitored longline fishing experiments conducted in French Polynesia to ( i) estimate AVM for each species based on bootstrapped samples and ( ii) to assess AVM drivers using multivariate logistic regression models for the blue shark ( Prionace glauca) and oceanic whitetip shark ( Carcharhinus longimanus). We found that AVM varies widely between species. Oceanic whitetip sharks are more likely to die when caught in waters outside their comfort temperature range, and their odds of survival increase with body length. For the blue shark, the only driver related to AVM is hooking duration. These results indicate that to reduce the AVM of these two species, the vertical distribution of hooks and soak duration should be considered as mitigation measures related to pelagic longlining.
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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 | 0.001 | 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".