Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature
Bibliographic record
Abstract
Abstract : Early in the conduct of Project 11hg (Collaborative Knowledge Exploitation for Maritime Domain Awareness) at Defence R&D Canada, anomaly detection in the maritime domain has been identified by the operators/analysts of the operational community as an important aspect requiring research and development. A number of R&D activities have thus been undertaken under the project to specifically investigate maritime anomaly detection (MAD). This Technical Memorandum reports on one of these activities. It first provides a high-level introduction to the domain, and then presents a review of selected literature on the subject. Different views of the field are presented, starting with a description of the various steps of MAD, followed by a discussion of four interrelated goals of MAD. Current gaps in MAD are identified from the data and information, processing and systems perspectives. The selected literature review is structured around specific organizations known to be active in maritime anomaly detection, various MAD systems, and other relevant research activities. A high-level assessment of the methods for MAD that were found in the reviewed literature completes the discussion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".