Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature
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
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.
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 | 0.001 |
| 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