Analysis of Adaptive Data Fusion Approaches within LM Canada's Technology Demonstrator
Bibliographic record
Abstract
Lockheed Martin Canada (LM Canada) has developed a Technology Demonstration environment, which over the last decade has been used to demonstrate initial proof-of-concept and then analyse various approaches for enhancing overall Data Fusion system performance for applications to Canada’s defence programs. It has a blackboard-based architecture that permits mix of rule-based and algorithmic approaches, specifically useful in the implementation of higher-level fusion capabilities. Various aspects of these efforts, such as fusion architectures, algorithms and information sharing strategies between multiple collaborating platforms have been presented previously [1,2]. This paper presents currently on-going efforts towards the analyses of concepts for level 4 fusion, i.e. methods for adapting the fusion processes based on the tactical and environmental factors. Over the last 13 years, LM Canada’s Research and Development (R&D) department in collaboration with Defence Research and Development Canada (DRDC) has been developing data fusion capabilities in support of Canada’s defence programs. The initial efforts started with the development of a data fusion capability that fused Above Water Warfare (AWW) onboard sensor data of the Halifax Class frigate [3]. Then Image Fusion
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".