Deriving high-resolution spatiotemporal fishing effort of large-scale longline fishery from vessel monitoring system (VMS) data and validated by observer data
Bibliographic record
Abstract
Estimating geo-referenced fishing effort is vital to develop advice for effective fisheries management. Many studies in recent decades have attempted to obtain complete, high-resolution effort data from vessel monitoring systems (VMSs). The main challenge in this regard is to develop a classification method for differentiating fishing activities (e.g., fishing days) from nonfishing activities in VMS data. This study developed a simple, novel classification criterion for a large-scale tuna longline (LTLL) fishery that has not been studied before. LTLL operations were first explored using observer data. Three approaches were designed for developing fishing-day classification criteria, using maximizing sum of sensitivity and specificity (SS) as the major performance measure and minimizing difference of SS as a reference. At least one VMS report with speed in the range of 2–5 kn (1 kn = 1.852 km·h–1) detected during the time-of-day period of 14:00–23:00 h was recommended as the criterion for defining a fishing day. Possible explanations for the differences between the estimated fishing days from VMS data and those reported on logbooks are discussed; most causes were related to specific features of the fishery.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".