A new dictionary-based preprocessor that uses radix-190 numbering
Bibliographic record
Abstract
Various scholarly works in the literature have pointed out that placing a preprocessor in front of a standard postcompressor would help achieve higher gains while compressing natural-language text files. Ever since, there has been much research on preprocessors to improve the gain attained by concatenated systems. With the same goal in mind our paper proposes a new word-based preprocessor named METEHAN190 (M190) and contrasts its performance with four other state-of-the-art preprocessors. Throughout the experiments source files from the Wall Street Journal (WSJ) archive, and the Calgary, Canterbury, Gutenberg, and Pizza and Chili corpora were used. Postcompressors adapted were Prediction by Partial Matching compressor using method-D (PPMD) and Monstrous PPM II compressor (PPMonstr). It was observed that in all three experiments WRT and M190 would achieve the two highest compression gains. For small text and transcription files from the Calgary corpus, M190 would outperform all preprocessors including WRT. On the other hand, a look at average encoding and decoding times shows that the semistatic byte-oriented methods are much faster in comparison to the static dictionary-based methods that encode words with characters.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".