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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".