Fishing on floating objects (FOBs): how French tropical tuna purse seiners split fishing effort between GPS-monitored and unmonitored FOBs
Bibliographic record
Abstract
Fishing on floating objects (FOBs) dominates catch in tropical tuna purse seine fisheries. One frequently cited advantage of deploying GPS-monitored FOBs is that the position information can be used for directed fishing to reduce search time for tuna. However, purse seiners also fish on foreign objects for which position information is not available. It is critical to quantify the prevalence of fishing on GPS-monitored versus unmonitored FOBs to understand how they impact fishing effort and catch per unit effort. We analyzed French commercial, observer, and FOB trajectory data in the Atlantic and Indian oceans to determine how often purse seine vessels fish on GPS-monitored FOBs. Only 2.7%–20.4% of French FOB fishing sets over 2007–2013 in both oceans were made on GPS-monitored FOBs. Though increasing over time, the low percentage suggests that French vessels do not primarily use GPS-monitored FOBs to reduce search time for tuna. We hypothesize that fishery-wide FOB deployments have important collective consequences for overall fishing effort and recommend that future effort metrics should be based on fishery-wide FOB activities.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".