Bit-Level Affixation Text Compression Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Algorithmic data compression is a crucial concept in computer science aimed at reducing the size of stored data to minimize storage space. Particularly significant is the ability of an algorithm to compress data to the bit level, which greatly enhances the efficiency of storing vast amounts of data today. This paper introduces a novel approach to bit-level text compression algorithms, called BATCA. It features a distinctive dictionary design based on patterns of word construction, including vowel, prefix, suffix, and article formats, facilitating word referencing during both compression and decompression phases. Theoretical findings reveal significant compression achievements: a maximum bit compression per word of 69.79% with ASCII encoding, 84.89% with Unicode, and 92.45% with UTF -8, resulting in a maximum compression ratio of 16.55 times. Additionally, empirical evaluations on real data from the Calgary and ArTechnica corpora, compared against common applications, demonstrate the algorithm's ability to significantly outperform existing alternatives in terms of saved space percentage and compression ratios.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it