Mining Vessel Trajectories for Illegal Fishing Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we propose a data-driven approach to detection and tracking of dark fishing in high-volume marine traffic datasets from vessel tracking services. Dark fishing refers to stealthy fishing operations by vessels trying to hide their illicit activities related to various forms of illegal fishing-one of the most serious threats to world fisheries and fish populations worldwide as well as to global food security. Our approach builds on profiling and ranking fishing vessels by analyzing their routine operations over extended time periods to uncover abnormal activity patterns associated with dark fishing. The focus is on vessel movement patterns rendered as a trajectory with defined starting and endpoints such as ports and known anchorage locations. Specifically, we analyze scenarios where the fishing pattern, with the fishing gear in the water, is obscured in a vessel's reported trip data. Our experimental evaluation, using a large dataset of fishing vessel trajectories from coastal waters of North America, shows the effectiveness and efficiency of the proposed method in differentiating between suspicious and normal fishing vessels irrespective of the vessel type.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it