Persistency and permanency of two stages DNA splicing languages with respect to one initial string and two rules via Yusof-Goode (Y-G) approach
Bibliographic record
Abstract
The notion of Yusof-Goode (Y-G) splicing system was first schemed by Yusof to study the relationship between formal language theory and molecular biology. The splicing languages that are produced by splicing system have some important characteristics called persistent and permanent. In biological perspective, the recombinant DNA molecules can be manipulated by recombination action if they have persistent property. Thus, the persistency as well as permanency of splicing languages (recombinant DNA molecules) is considered to be an interesting topic in the field of DNA recombination, particularly when the recombination process is accomplished at second stage. Conducting a wet-lab experiment to show the mentioned properties of splicing languages are time consuming and expensive. Therefore, to overcome this problem, mathematical approach is chosen to investigate the persistency and permanency of splicing languages which will be then given as theorem and corollary. Thus, an initial string (with two recognition sites) and two rules are considered for introducing the above characteristics using Y-G approach.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".