On the precision of predicting fishing location using data from the vessel monitoring system (VMS)
Bibliographic record
Abstract
Defining fishing grounds based on data from vessel monitoring systems (VMS) has been a widely researched topic in recent years. Much of the research has focused on filtering algorithms for identifying fishing locations from VMS point data, most often supplemented with either imputed or reported vessel speed information. This study compared the precision of categorizing fishing locations from VMS data either by the most wide-spread “speed rule” approach or by a probability model. Using data from Northeast U.S. Fisheries for fishing years 2010–2014, we showed that the traditional representation of fishing activities as derived by speed rules leads to a severe misrepresentation of fishing grounds for gears other than bottom otter trawl. Predictions based on probability models outperformed gear-specific speed rules in classifying VMS polls for sink gillnet and scallop dredge trips, without adding substantial computational effort. The probability models thus provide the largest improvements in gears with complicated fishing patterns, while controlling for issues such as fleet dynamics that historically have not been dealt with in the static speed rules but which can have significant impacts on the quality of predictions.
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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 | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".