On the joint source-channel coding error exponent for systems with memory
Bibliographic record
Abstract
We establish an upper bound for the joint source-channel coding (JSCC) error exponent E/sub J/(Q, W) for a discrete stationary ergodic Markov (SEM) source Q and a discrete channel W with additive SEM noise. This bound, which is expressed in terms of the Renyi entropy rates of the source and noise processes, admits an identical form to Csiszar's sphere-packing upper bound for the JSCC error exponent for memoryless systems (I. Csiszar, Nov. 1982). In this regard, our result is a natural extension of Csiszar's upper bound of the JSCC error exponent from the case of memoryless systems to the case of SEM systems. We also investigate the analytical computation of E/sub J/(Q,W) by comparing our bound with Gallager's random-coding lower bound (R. G. Gallager, 1968), when the latter one is specialized to the SEM source-channel system.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".