An Estimator for Processing UAV-Reconnaissance Data in Support of Urban Operations
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Bibliographic record
Abstract
The development of small and micro UAVs for sensing purposes, combined with the complex structure of urban combat, has led to both an ability and a requirement to generate estimates of force distributions in an urban setting. Such force distributions are needed as inputs to other tools being developed as computational aids for C 2 . Although the UAVs can produce voluminous data, conversion of that data into meaningful information is not computationally feasible with standard machinery (i.e., tools that propagate probability distributions over the set of potential opponent force distributions). We discuss an alternative estimator which requires only a small fraction of the computational power of a laptop computer when operating on realistically-sized problems. The algorithm is developed, error bounds are obtained, and an example of the UAV observation-based estimator operating in conjunction with an urban combat C 2 simulator is presented.
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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 | 0.001 |
| 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 it