Data fusion applications for military and civilian purposes developed on DND/LM Canada decision support testbed
Bibliographic record
Abstract
Since 1991, the research and development (R&D) group at Lockheed Martin Canada (LM Canada) has been developing and demonstrating technologies which will provide observe-orient-decide-act (OODA) decision making capabilities/tools in naval and airborne command and control (C2) for application on Canadian Patrol Frigates (CPF) and Canada's CP-140 (Aurora) fixed wing aircraft. Over the last three years LM Canada has also established a generic expert system infrastructure and has demonstrated that it is suitable for integrating these decision making technologies into real-time command and control systems (CCSs). This infrastructure has proven to be very useful not only in the defence applications like CPF and Aurora, but also in LM Canada diversification efforts, specifically in intelligent transportation systems (ITS) and remote sensing. The paper describes the infrastructure, the generic testbed for decision support and the data fusion applications that have been implemented and are under development within this testbed for military and civilian purposes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".