Multi-sensor maritime monitoring for the Canadian Arctic: Case studies
Bibliographic record
Abstract
Maritime Domain Awareness (MDA) in the Canadian Arctic is limited by the scarceness or even absence of monitoring sensors. The backbone for vessel surveillance is represented by the satellite-based Automatic Identification System (S-AIS), which provides important though not comprehensive information about ships in the area. The objective of the Canadian-German research project PASSAGES is to specify the requirements and the modular architecture of a persistent maritime surveillance and risk management system adapted to arctic conditions and focused on the Northwest Passage (NWP). In order to provide a continuous vessel traffic picture in remote and local areas, real and simulated sensors from space borne and ground based platforms besides S-AIS are taken into account. The capabilities of such multi-sensor surveillance solutions in enhancing the MDA are demonstrated over two selected operationally relevant scenarios. A key element to achieve this, are robust multisource tracking and fusion algorithms able to cope with different heterogeneous and potentially delayed, imprecise or misaligned sensor data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".