Utilizing wide area maritime domain awareness (MDA) data to cue a remote surveillance system
Bibliographic record
Abstract
Defence Research and Development Canada – Atlantic (DRDC Atlantic) is currently involved in research on the topic of northern Maritime Domain Awareness (MDA). One project, entitled Situational Information for Enabling Development of Northern Awareness (SEDNA), includes research on the exploitation of MDA data in northern areas. One aspect of this research is to utilize wide area MDA data to provide awareness to an unattended, land-based system. Wide area MDA is attained through the use of space-based AIS (SAIS) data, a data feed used by the Canadian Department of National Defence and supplied by the commercial provider exactEarth Ltd. The land-based surveillance system used is the remote northern system constructed within the DRDC Northern Watch Technology Demonstration Project. Northern Watch is a multi-year project intended to show state-of-the-art, unattended, surveillance capabilities in the Canadian north. The link between the SAIS and Northern Watch is provided by a research infrastructure that consists of an assembly of data sources, users, applications, and product management techniques that collectively support research in areas such as information management and MDA data exploitation. High-level descriptions of the systems are provided along with elaboration on the alerting algorithm, the notifications that would be sent to the Northern Watch southern command site, and the resulting actions that could be taken by the Northern Watch surveillance system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".