A network abstraction of multi-vessel trajectory data for detecting anomalies
Why this work is in the frame
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Bibliographic record
Abstract
The detection of anomalies in vessel trajectories is a problem of great interest for all maritime surveillance systems, since it may uncover strange, suspicious or difficult situations for vessels. All the existing works in the field examine specific aspects of the problem and propose case specific tools that can hardly generalize or scale-up to a worldwide monitoring system. In this article, we present a methodology for creating a network abstraction of the trajectories of multiple vessels, which uses only the information collected from the vessels’ Automatic Identification System (AIS). The resulting network abstraction contains rich information about the vessel behavior in an area and can be processed with network analysis and other data mining techniques in order to uncover hidden outliers, even in an unsupervised manner. Experimental results on a real dataset demonstrate some of the capabilities of the proposed network model and indicate its extension to more complex automatic surveillance tasks.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it