Can vessel monitoring system data also be used to study trawling intensity and population depletion? The example of Australia's northern prawn fishery
Bibliographic record
Abstract
We explore the potential of using data from Australia's northern prawn fishery (NPF) vessel monitoring system(s) (VMS) to examine trawl track, trawling intensity, and stock depletion due to trawling. We simulate VMS data by subsampling global positioning system (GPS) fixes from the NPF fishing vessels at different polling intervals to examine their accuracy in describing trawl tracks. The results of the simulations suggest that VMS data with polling intervals longer than 30 min cannot accurately estimate trawl tracks. The analysis of high-polling-frequency VMS data collected in four (later reduced to three) 6 nautical mile × 6 nautical mile grids that historically received high levels of fishing effort showed that trawling was not random and some areas were trawled up to 28 times in the tiger prawn fishing season and the impact varied among years. The results of a catch-depletion analysis suggest that fishery catch-per-unit-effort and cumulative catch may not be proportional to overall target-species biomass in areas with highly aggregated trawl effort. The VMS data also showed a large number of trawls can occur in productive areas and that trawling impacts on benthos may be quite marked.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".