Bibliographic record
Abstract
Abstract This study was motivated by the problem of optimally encoding information on DNA for biocomputational purposes. Our formalization of intermolecular hybridization (binding) with bulges led to the notion, interesting in its own right, of k-involution codes. An involution code refers to any of the generalizations of the classical notion of codes in which the identity function is replaced by an involution function. (An involution function θ is such that θ2 equals the identity. An antimorphic involution is the natural formalization of the notion of DNA complementarity.) We namely define and study the notions of k-θ-prefix, k-θ-suffix and k-θ-bifix codes. We also extend the notion of k-insertion set and k-deletion set of a language to incorporate the notion of an involution function. Thus, to an involution map θ and a language L, we associate a set k-θ-ins(L) (k-θ-del(L)) with the property that its k-insertion (k-deletion) into any word of L yields words which belongs to θ(L). We study the properties of these languages and their connection to involution codes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".