Evidence for a fisher‐designed solution to manta and devil ray bycatch in tuna fisheries
Bibliographic record
Abstract
Bycatch in global tropical tuna purse seine fisheries represents a significant source of mortality for manta and devil rays (mobulids), which are globally threatened. Use of best handling and rapid release practices on purse seine vessels can substantially reduce mortality and improve vulnerability status for mobulids. However, interventions must be effective, operationally feasible, and safe for human operators if they are to be successfully implemented at a large scale. We assessed the feasibility and efficacy of an innovative mobulid bycatch release device (sorting grid) in collaboration with captains and crew of vessels in the tropical tuna purse seine fleet. We evaluated the size of individuals and duration of release when the sorting grid was used and compared these metrics with other release methods. Forty-one mobulid capture events occurred on 12 vessels. Mobulids released using the sorting grid were significantly larger than those released by other methods; yet, mean handling time remained short (∼3 min), suggesting that the device enables the rapid release of even the largest individuals. Widespread implementation and use of the mobulid sorting grid could help achieve conservation goals for threatened mobulid rays and may have broader bycatch reduction benefits. Adoption of sorting grid requirements by regional fisheries management organizations could standardize best practices and markedly improve the survival of released mobulid rays globally.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".