Fish behaviour from fishers’ knowledge: the case study of tropical tuna around drifting fish aggregating devices (DFADs)
Bibliographic record
Abstract
Purse-seining for tropical tuna is one of the most technologically advanced fisheries in the world. The purpose of this study was to apply local ecological knowledge (LEK) to assist in the planning of future in situ studies of fish behaviour around drifting fish aggregating devices (DFADs) by prioritizing research topics, thereby reducing the number of potential hypotheses to explore. Interviews of fishing masters of the purse-seine fleets working in the western Indian Ocean provided an alternate, independent, and previously unexplored source of behavioural information, specifically on the attraction, retention, and departure behaviours of tuna schools in relation to DFADs. Most fishing masters agreed that the maximum attraction distance of a DFAD is approximately 10 km and generally agreed to the following statements. Tuna form distinct schools under FADs, commonly segregated by species and size. The main reasons for the departure of tuna aggregations from FADs are changes in currents or FAD movements and location in relation to physical or oceanographic features. The number of actively monitored DFADs at sea in the western Indian Ocean was estimated at approximately 2100. Incorporating fishers into the planning and design stages of future research projects will facilitate collaborative and integrated approaches.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".