Mapping AIS coverage for trusted surveillance
Bibliographic record
Abstract
Automatic Identification System (AIS) is an unattended vessel reporting system developed for collision avoidance. Shipboard AIS equipment automatically broadcasts vessel positional data at regular intervals. The real-time position and identity data from a vessel is received by other vessels in the area thereby assisting with local navigation. As well, AIS broadcasts are beneficial to those concerned with coastal and harbour security. Land-based AIS receiving stations can also collect the AIS broadcasts. However, reception at the land station is dependent upon the ship's position relative to the receiving station. For AIS to be used as a trusted surveillance system, the characteristics of the AIS coverage area in the vicinity of the station (or stations) should be understood. This paper presents some results of a method being investigated at DRDC Atlantic (Canada) to map the AIS coverage characteristics of a dynamic AIS reception network. The method is shown to clearly distinguish AIS reception edges from those edges caused by vessel traffic patterns. The method can also be used to identify temporal changes in the coverage area, an important characteristic for local maritime security surveillance activities. Future research using the coverage estimate technique is also proposed to support surveillance activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".