Identification and characterization of a cost-effective combination of systems for Arctic surveillance: The Northern Watch project
Bibliographic record
Abstract
This thesis discusses a new stream of research and analysis which will form part of the Northern Watch Technology Demonstration project. The objective of the thesis is to develop and illustrate a procedure for identifying and characterizing combinations of sensors and systems that will provide cost-effective options for Arctic maritime surveillance. A ship detection simulation is constructed, the results of which are used to produce a set of ranked options for combinations of sensors used to conduct maritime surveillance at a strategic choke point located at the Barrow Strait, in Canada's Northwest Passage. The overall objective of the surveillance is to improve on the detection, classification, and identification of maritime vessels. The modeled performance and effectiveness of each sensor is evaluated in relation to the multiple objectives using the Analytical Hierarchy Process (AHP) to rank alternative sensor effectiveness and performance. The results indicate that the most cost-effective solution is to install an Automatic Identification System (AIS) sensor at the Northern Watch station. However, due to practical concerns, alternatives to this approach are presented and discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".