Text compression using lexicographic permutation of binary strings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Text messages are generally encoded by performing table look-up on fixed length code tables. In this paper, a lossless text compression algorithm which works on the principle of entropy reduction is proposed. Characters in a text message in any language are generally encoded using a binary string with a Unique Lexicographical Rank (ULR). A corresponding Maximum Rank(MR) for any binary string can be computed using lexicographical permutation. Reducing the MR of the binary string results in considerable reduction in the number of bits to be transmitted. MR reduction is achieved in this work by using character frequency based encoding models. Uni-gram, bi-gram and tri-gram models are used herein. Experiments on text compression are conducted on the Calgary Corpus and Project Gutenberg databases. Experiments on text compression are conducted on the Calgary and The Project Gutenberg corpus. Results indicate a significant increase in compression ratio.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it