Bibliographic record
Abstract
Summary form only given. Recently, a technique called bit recycling (BR) was introduced to help reduce the redundancy caused by the multiplicity of encodings. It has been used to improve LZ77 compression, which is especially prone to allow for the existence of numerous different compressed files for some given original file F. The multiplicity of encodings causes redundancy. Instead of trying to eliminate or reduce the multiplicity itself, BR exploits it and extracts a compensation from it. It uses implicit communication that happens when, at some point in the compression process, we have that: 1. the compressor C has more than one option; and 2. the decompressor D is able to recognize that situation. The mere fact that C has the liberty to select one among many options allows it to implicitly send bits to D. The particularity of BR is that it avoids storing as many bits as possible in the compressed file by implicitly sending them instead. Previous work presented a technique that recycles bits based on the existence of multiple longest matches, called longest-match BR (LMBR). This work presents a more general, and more powerful, technique, called all-match BR (AMBR) that exploits shorter matches.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.251 | 0.142 |
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".