Bibliographic record
Abstract
Introduction The theory of codes is a fertile area at the intersection of formal language theory, error detection and correction, data compression and data security [6]. Theoretical research into codes is often interested with combinatorial properties of formal languages related to codes. In particular, there has been substantial recent interest in classes of codes defined by certain "finite subset" conditions. In general, given a class C of codes and m 0, we may define a class C m as follows: L 2 C m () (L ` L; jL j m ) L 2 C): Thus, for instance, a language L is an n-code if every language L ` L of size at most n is a code [5]. Also studied are n-prefix-suffix codes [3], n-infix-outfix codes [8, 9, 7], n-intercodes [6, p. 555] and others. A general framework for defining such "finite subset" classes of languages is given, e.g., by Jurgensen and Konstantinidis [6, pp. 565--567]. Decidability problems for such classes of languages appear to be very difficult. I
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".