Fine-scale catch data reveal clusters of large predators in the pelagic realm
Bibliographic record
Abstract
The management and conservation of large pelagic fish commonly rely on fisheries data and thus crucially depend on our understanding of the fish response to the fishing gear. Stock assessment of both tropical and temperate tuna strongly leans on the catch statistics derived from pelagic longline fisheries. However, the role of the spatial distribution of catches of tuna and bycatch species over the gear, which can affect the estimated tuna abundance, is still neglected. In this study, we analyzed data obtained from 147 instrumented pelagic longline sets equipped with hook timers and temperature depth recorders to characterize the distribution of hooking contacts and success at a fine temporal and spatial scale. Scientific surveys were carried out in the Central–South Pacific Ocean (French Polynesia), targeting tropical (Thunnus albacares, Thunnus obesus) and temperate (Thunnus alalunga) tuna. Data analysis based on spatial point processes and stochastic modeling demonstrate the presence of spatio-temporal clusters for both hooking contacts and hooking success. The comparative analysis of the observed spatio-temporal patterns for different oceanographic zones revealed the persistent structure of the clusters, suggesting that they are neither related to local environmental conditions nor to the spatial distribution of prey species.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".