Behavioral learning of vessel types with fuzzy-rough decision trees
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
A reliable and efficient characterization of vessel activities along coastal regions is of crucial importance for maritime domain awareness. With increased navigational flows across all waterways and the worldwide dissemination of active and passive vessel tracking modalities, learning a vessel’s behavior is becoming a strategic priority for maritime operators and decision makers. In this paper, we propose an interpretable computational model based on fuzzy-rough decision trees (FRDTs) to predict the vessel type given a summary vector in the form of descriptive track features that include kinematic, static and environmental information. The track summaries are generated from the fusion of Automatic Identification System (AIS), Synthetic Aperture Radar (SAR) and Canada weather reports. Our methodology uses fuzzy rough sets to discard irrelevant features on the basis of their dependency of the vessel type, prior to the iterative construction of the FRDT. Empirical results with a real-world data set in the east coast of North America confirm that the proposed approach is able to accurately assign the correct label (i.e., type) to previously unseen vessels in over 80% of the cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it