Different means contributing to anchored FAD's fishing selectivity in the Lesser Antilles
Bibliographic record
Abstract
In order to improve the sustainable development of FADs fishing, it is important to reduce the capture of juveniles or species that need a decrease in fishing effort, temporarily or definitively. Through previous statistics data coming from commercial fishing trips and new experimental fishing trips, we compared different gears and techniques selectivity for the species and the size of the capture around FADs. We also compared different type of bait used, the best hours to fish for better productivity and to target adults. Finally we look at the influence of the FAD distance from shore. We observed that fishers’ strategies have a critical influence on FADs setting and targeted species. The further the FAD is deployed and the better yield the fisherman obtain. The fishers who target Dolphin fish deploy several FADs while the others exploit generally one FAD per trip. The main results from experimental fishing trips show that the jigging technique around FADs catches blackfin tuna adults. Most of the blackfin and yellowfin tuna captures happened late in the morning and we observed a drop off after 12:00 pm. Flying fish bait (live or dead) seems to be more efficient, except for the blue marlin. An analysis of FADs governance is necessary before advising some technics.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".