On Using Goldbach G0 Codes and Even-Rodeh Codes for Text Compression on Using Goldbach G0 Codes and Even-Rodeh Codes for Text Compression
Why this work is in the frame
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Bibliographic record
Abstract
This research aims to study the efficiency of two variants of variable-length codes (i.e., Goldbach G0 codes and Even-Rodeh codes) in compressing texts. The parameters being examined are the ratio of compression, the space savings, and the bit rate. As a benchmark, all of the original (uncompressed) texts are assumed to be encoded in American Standard Codes for Information Interchange (ASCII). Several texts, including those derived from some corpora (the Artificial corpus, the Calgary corpus, the Canterbury corpus, the Large corpus, and the Miscellaneous corpus) are tested in the experiment. The overall result shows that the Even-Rodeh codes are consistently more efficient to compress texts than the unoptimzed Goldbach G0 codes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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 it