Fishing methods to reduce sea turtle mortality associated with pelagic longlines
Bibliographic record
Abstract
Changes in hook design and bait type were investigated as measures to reduce the bycatch of sea turtles on pelagic longlines in the western North Atlantic Ocean. Specifically, the effectiveness of 18/0 circle hooks and mackerel (Scomber scombrus) bait was evaluated with respect to reducing sea turtle interactions and maintaining swordfish (Xiphias gladius) and tuna (Thunnus spp.) catch rates. Individually, circle hooks and mackerel bait significantly reduced both loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) sea turtle bycatch. Circle hooks also significantly reduced the rate of hook ingestion by the loggerheads, potentially reducing postrelease mortality. The combination of circle hooks and mackerel bait was even more effective for loggerhead turtles and had no negative effect on swordfish catch. These modifications in fishing methods, in conjunction with tools developed to remove hooks and line from the turtles, significantly reduced the capture rate of sea turtles and potentially the post-hooking mortality of those that were caught and did not negatively impact the primary target species catch rate. In addition, these mitigation measures have the potential to reduce mortality of sea turtles and other bycatch species worldwide.
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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".