Distributed Filtering with Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
We investigate an 'inference first' (IF) approach to information retrieval from a wireless sensor network (WSN). In this method, statistical estimation pertinent to the user's application is implemented within the network (in-situ) and only the relevant sufficient statistics are exported. We formulate this procedure as a delay-free filtering problem on a spatio-temporal hidden Markov model (HMM), and propose a scalable approximate distributed filter. The algorithm is a novel application of the idea of iterated decoding, where we iteratively marginalize the joint distribution of the state of the HMM at two consecutive time epochs. We compare and contrast algorithms like the Gibbs sampler (GS), mean field decoding (MFD) and broadcast belief propagation (BBP), and discuss their suitability for in-situ marginalization. A simplified analysis of the energy gain achievable by the IF approach, relative to centralized processing, is provided.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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