Bibliographic record
Abstract
Statistical coding techniques have been used for a long time in lossless data compression, using methods such as Huffman's algorithm, arithmetic coding, Shannon's method, Fano's method, etc. Most of these methods can be implemented either statically or adaptively. Canonical codes, in which the code words are arranged in a lexicographical order, are advantageous because they can be decoded extremely expediently. Although Huffman's algorithm is optimal, the generation of a canonical Huffman code is not straightforward. Conversely, while the Fano coding is sub-optimal, it can lead to canonical codes. In this paper, we resolve the dilemma by focusing on the static implementation of Fano's method. By taking advantage of the properties of the encoding schemes generated by this method, and the concept of "code word arrangement", we present an enhanced version of the static Fano's method, namely Fano/sup +/. We formally analyze Fanol by presenting some properties of Fano trees, and the theory of list rearrangements. Our enhanced algorithm achieves compression ratios arbitrarily close to those of Huffman's algorithm. Empirical results on files of the Canterbury corpus corroborate the almost-optimal efficiency of our enhanced algorithm and its canonical nature. We believe that the compression efficiency of Fano+ can be made to attain the compression ratios of the best known schemes if a structure model of the data is also incorporated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".