Temporal patterns of target catch and sea turtle bycatch in the US Atlantic pelagic longline fishing fleet
Bibliographic record
Abstract
Sea turtle bycatch in pelagic longline fishing gear is an ongoing threat to the conservation of sea turtle populations. However, these bycatch events do not occur uniformly in space or time. Leatherback ( Dermochelys coriacea ) and loggerhead ( Caretta caretta ) bycatch rates reported in large fishing regions exhibited different degrees of interannual variability. Target catch and sea turtle bycatch in most regions displayed strong periodicity that corresponded to seasons (~365 days) and/or moon phase (~29 days). When trends in catch and bycatch rates were examined by month and moon phase, the significant periods of higher and lower catch and bycatch related to swordfish ( Xiphias gladius ), yellowfin tuna ( Thunnus albacares ), and sea turtle temporal distributions in foraging and spawning/nesting, oceanographic and prey conditions, and foraging behavior. Catch and bycatch rates tended to depend more on a seasonal rather than a lunar time scale, although there is likely an interaction between the two. These findings provide insights to the susceptibility of target catch and bycatch, regional and temporal patterns of fishing effort, and potential guidance for resource management and conservation.
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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".