Highly efficient universal coding with classifying to subdictionaries for text compression
Bibliographic record
Abstract
Describes a practical, locally adaptive data compression algorithm of the LZ78 class. According to the Lempel-Ziv incremental parsing rule, the boundary of a string is not related to the statistical history modeled by finite-state sources. The authors have already reported an algorithm classifying to subdictionaries (CSD), which uses multiple subdictionaries and conditions the current string by using the previous one to obtain a higher compression ratio for image compression. They present a practical implementation of this method for any kind of data, and show that CSD was more efficient than LZC when the UNIX facility for compression. The compression performance of CSD was about 10% better than the LZC with the practical dictionary size, an 8K-entry dictionary when the test data were used form Calgary Compression Corpus. Using hashing, the processing speed of the CSD became as fast as the LZC, though the CSD algorithm was more complicated than the LZC.>
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".