Performance of the successive coding strategy in the CEO problem
Bibliographic record
Abstract
We consider a distributed sensor network in which sensors communicate their observations to the CEO using limited transmission rate. We use successive coding strategy of S. C. Draper and G. W. Wornell (2004) and obtain the optimal distortion sum-rate tradeoff for L sensors with different noise levels. Our result is an extension of the result of S. C. Draper and G. W. Wornell (2004), where the optimal distortion sum-rate tradeoff for two equal-SNR sensors is derived. As the number of sensors increases, the achievable distortion decreases since the CEO accumulates more data and can obtain a better estimate of the source. The fraction of the total rate allocated to each sensor is approximately 1/L if the average rate per sensor node gets small or if the sum-rate _R is very large for a fixed L. Thus, we can simplify rate allocation problem in a general parallel sensor network with L sensors by assigning equal rates to sensors. We show that this scheme may not cause a large extra distortion compared with the minimum achievable distortion. Finally, we obtain a lower bound for the minimum achievable distortion in the Gaussian sensor network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".