Fishing activity of tuna purse seiners estimated from vessel monitoring system (VMS) data
Bibliographic record
Abstract
In the lack of fishery-independent information, catch per unit of effort (CPUE) is the conventional abundance index. In the case of the tropical tuna purse seine fisheries, a critical difficulty lies in the definition of an effective fishing effort, because fishermen use two different fishing modes (free swimming schools versus schools under fish aggregating devices) alternatively during the same trip. In this study, vessel monitoring system (VMS) data were used in an operational level to study and quantify the spatial dynamic of the tropical tuna purse seine fishing activity. A Bayesian state–space model allowed classifying VMS steps into three activities (fishing, tracking, and cruising), which were characterized by a small set of complementary spatial indicators. The dominant activity (49%) was clearly the tracking of tuna schools within areas of aggregations. A hierarchical spatial organization of the three fishing activities was also evidenced. Fishing strategies described by the triplets of proportions of time devoted to each activity and interpreted as compositional data were modelled by the sum of a vessel effect and a seasonal effect.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".