Using bit recycling to reduce the redundancy in plurally parsable dictionaries
Bibliographic record
Abstract
Tunstall proposed an efficient algorithm for constructing the optimal dictionary of any particular size to obtain a variable-to-fixed code. More accurately, the algorithm constructs the optimal uniquely parsable dictionary. In fact, Savari showed that, if one allows herself to consider plurally parsable dictionaries, better codes may be constructed. Savari found a class of plurally parsable dictionaries that outperform the Tunstall code for memoryless, highly skewed, binary sources. This work addresses the redundancy in plurally parsable dictionaries and proposes the use of bit recycling as the means to reduce this redundancy, extending the range of random binary sources that may benefit from a plurally parsable dictionary at the same time. We present a theoretical analysis that evaluates the performance of variable-to-fixed codes based on the Tunstall dictionary and ones based on plurally parsable dictionaries, using Savari's coding on the one hand and coding with bit recycling on the other hand.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".