Statistical Modelling of Air-Ground Remotely Sensed Geo-Intelligence Information Using Naïve Bayesian Classification: A Decision-Making Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the increasing complexity of warfighter scenarios with respect to adversarial engagement and non-traditional environments of contention, there is an increasing need to process geo-intelligence information where the aim is the optimal understanding of state for high level decision making. Bayesian statistical modelling Two problem areas suggesting a need for statistical modelling for state estimation-based decision making are air-ground information fusion between drone and satellite and assault riflehuman information fusion for hostile ground engagements. Two fictitious model problems are explored where the aim is to explore how basic Bayesian statistical information processing would occur in these geo-intelligence scenarios which could assist human decision making.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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