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Record W2087143193 · doi:10.1063/1.4907513

Persistency and permanency of two stages DNA splicing languages with respect to one initial string and two rules via Yusof-Goode (Y-G) approach

2015· article· en· W2087143193 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAIP conference proceedings · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Biological Computing
Canadian institutionsnot available
FundersUniversiti Malaysia PahangCentre in Green Chemistry and Catalysis
KeywordsCorollaryRNA splicingString (physics)Formal languageComputer scienceRecombinant DNAPerspective (graphical)RecombinationRecursively enumerable languageMathematicsTheoretical computer scienceComputational biologyBiologyGeneticsDiscrete mathematicsAlgorithmArtificial intelligenceGene

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.291
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it