Collaborative distributed data fusion architecture using multi-level Markov decision processes
Why this work is in the frame
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Bibliographic record
Abstract
Decentralized multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, we present a solution to one of the main problems of decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. This paper presents a hierarchial multi-level decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. The proposed approach, which is based on hierarchial Markov decision processes and decentralized lookup substrate, will control the information exchange and data fusion process based, among the other parameters, on maximizing performance metrics of individual platforms, thereby enhancing the whole distributed system's reliability as well as that of each participating platform. Simulation examples demonstrate the operation and the performance results of the system.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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