Impact of the Tactical Picture Quality on the Fire Control Radar Search-Lock-On Time
Why this work is in the frame
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Bibliographic record
Abstract
Abstract : Data fusion is suitable for a broad range of decision support applications. To cope with a larger class of problems and contexts, data fusion gains to be adaptive. Adaptation in data fusion corresponds to Level 4 of the JDL model, also referred to as process refinement. The Decision Support Systems Section (DSS) at Defence Research & Development Canada (DRDC)-Valcartier has initiated research activities aiming at developing and demonstrating advanced concepts of adaptive data fusion that could apply to the current Halifax and Iroquois Class Command & Control Systems (CCS), as well as their possible future upgrades, in order to improve their performance against the predicted future threats. This document gives a brief description of the adaptive data fusion concepts. It also presents a new Measure Of Effectiveness (MOE) that serves as an adaptation trigger in the target-tracking problem in maritime Above Water Warfare (AWW) applications. The proposed MOE uses the search to lock-on time of the Fire Control Radar (FCR) and aims at establishing and quantifying the effect of the quality of the Maritime Tactical Picture (MTP) on the diminution of battle space size and reaction time. Besides adaptation of the sensing and processing operation, this MOE allows addressing the trade-off between the time dedicated to the tracking with surveillance radars versus the time spent in search and lock-on with FCR.
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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 | 0.001 | 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 | 0.001 | 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