Bibliographic record
Abstract
In this paper, we propose a greedy technique for adaptive Fano coding, which is suitable for a wide range of applications, specially those in which memory constraints are tight. Our scheme has great potential in maximizing the output entropy, and thus has also indirect cryptographic implications. This paper includes the encoding and decoding algorithms, and the partitioning procedures suitable for the binary and r-ary schemes. It also includes a rigorous analysis of the properties of the algorithm. Empirical results demonstrate that our adaptive Fano scheme compresses marginally less than the adaptive Huffman scheme. In terms of speed, the former is faster in the compression phase and slower in the decompression phase. We also present the extension of the binary adaptive Fano coding method to multi-symbol code alphabets. We introduce the corresponding partitioning procedure, which deals with consecutive partitionings that satisfy the principles of Fano coding. To find the optimal partitioning, we propose a brute force algorithm that searches the entire space of all possible partitionings in O(m/sup r-1/) time. As opposed to this, we propose a greedy linear-time algorithm that finds a sub-optimal but accurate consecutive partitioning.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".