COSIMAR: Continuous Operational Signature Monitoring Awareness and Recommendation:
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it