On the error exponent to redundancy ratio of interactive encoding and decoding
Bibliographic record
Abstract
The concept of error exponent to redundancy ratio (EERR) of interactive encoding and decoding (IED), as well as Slepian-Wolf coding (SWC), is defined and investigated in this paper. The EERR of universal IED is determined. In the non-universal coding case, it is shown that for any stationary ergodic source-side information pair, a two stage IED scheme with 3 rounds of interactions or less can be constructed such that its EERR ≥1. Meanwhile, for any memoryless source-side information pair, the EERR of SWC is strictly less than 1 in the region where the error exponent of SWC is determined. Furthermore, practical two stage IED schemes are proposed and implemented by using LDPC codes and Belief Propagation (BP) Decoding, and simulation shows that the error probability of the proposed two stage IED schemes is indeed significantly lower than that of SWC schemes.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".