COSIMAR: Continuous Operational Signature Monitoring Awareness and Recommendation
Bibliographic record
Abstract
Crews of naval vessels lack an up-to-date awareness of those aspects of a ship’s susceptibility to threats that are related to the actual ship signatures (acoustic, magnetic, infrared, etc.). The ship’s susceptibility depends among others on the current configuration of the ship, the environment, the enemy sensor capabilities and the related ship signature levels. For operational purposes, it is desirable that crews have a tool which informs and advises them on the ship signatures, on ways of managing them and on the consequential detection ranges of adversary sensors in the current tactical situation. A functional demonstrator for such a support tool, called COSIMAR (Continuous Operational Signature Monitoring Awareness and Recommendation), has been developed and tested in a laboratory environment in an international project. The background and approach of this international cooperation between Canada, Germany, Norway, Belgium and The Netherlands had been presented at the INEC conference 2014 in Amsterdam. This year's presentation will show the result of this joint effort. The architecture, human machine interface, signature and susceptibility models will be addressed, including the laboratory environment simulating all required platform and environmental input.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".