Stochastic fusion of heterogeneous multisensor information for robust data-to-decision
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, based on the measure-theoretic probability theory and the theory of stochastic differential equation (SDE), a stochastic fusion framework is proposed for the heterogeneous sensor network for the purpose of robust decision making. In this framework, for each sensor, its sample space and the corresponding σ-algebra are defined. Then, random variables, which are designed to meet the requirements of the operation in the battle field, are defined over the pairs of sample space and its σ-algebra. After that, the conditional expectation is taken for those random variables conditional on the union of σ-algebras to finish the information fusion process. Furthermore, to make the decision making process more robust, the undesired uncertainty in the fused information is hedged out based on the theory of SDEs, before the fused information is used for the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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