Bibliographic record
Abstract
This paper presents a technique that aims at reducing the expansion of the compressed files that is caused by the multiplicity of equivalent messages. It does not try to eliminate multiplicity. Instead, it takes advantage of multiplicity by converting it into useful information, which we choose to describe parts of the compressed file itself. We call this technique bit recycling. On the decompressor side, when a message M is received, the set M of messages equivalent to M is determined, and the particular choice (M Sigma M) made by the compressor is perceived as a hint, which translates into a bit sequence. Such a bit sequence is said to be recycled and the bits it contains can be omitted from the compressed file. On the compressor side, the task is more complicated because the message that is currently selected among the set of equivalent ones carries information about the following messages. To make these far- reaching selections, the compressor may use non-deterministic choices but we propose a resolution algorithm along with a greedy version that allows the compressor to proceed in a stream-like fashion. We propose two ways to obtain recycled bit sequences: flat recycling, where a constant number of bits (about log2 \M\) is recovered for any selection of M Sigma M; and proportional recycling, where the number of bits that is recovered for the selection of M Sigma M grows with the cost of encoding M. In a 2006 paper, Dube and Beaudoin showed that they obtained the best experimental results using proportional recycling. We believe this recycling method to be close to optimal.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".