Credit-based variable-to-variable length coding: Key concepts and preliminary redundancy analysis
Bibliographic record
Abstract
A new coding concept called credit-based variable-to-variable length (cbv2v) coding is proposed in this paper. A binary cbv2v code is constructed, and analysis of its performance shows that cbv2v coding can achieve much better trade-off among the coding delay, redundancy, and space complexity than does variable-to-variable length (v2v) coding. Specifically, let L be the total number of source words. With finite coding delay, the redundancy of our proposed cbv2v code decreases in the order of O(L-0.5) while the redundancy of binary v2v coding is lower bounded by Ω ((log L)-5-ε) where ε is an arbitrary positive real number. Furthermore, we also show that under mild conditions, the redundancy of any cbv2v code can be lower bounded by Ω(L-2|χ|-1-ε), where |χ| is the size of source alphabet.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".