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Record W2579425220

Duplications and pseudo-duplications

2016· article· en· W2579425220 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational journal of unconventional computing · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Biological Computing
Canadian institutionsQueen's University
Fundersnot available
KeywordsSubstringGene duplicationNondeterministic finite automatonNondeterministic algorithmString (physics)Computer scienceMathematicsAlgorithmAutomatonTheoretical computer scienceAutomata theoryData structureGeneticsBiologyProgramming language
DOInot available

Abstract

fetched live from OpenAlex

A duplication is basic phenomenon that occurs through molecular evolution on a biological sequence. A duplication on a string copies any substring of the string. We define k-pseudo-duplication of a string w that consists, roughly speaking, of all strings obtained from w by inserting after a substring u another substring obtained from u by at most k edit operations. We consider three variants of duplication operations, duplication, k-pseudo-duplication and reverse-duplication. First, we give the necessary and sufficient number of states that a nondeterministic finite automaton needs to recognize duplications on a string. Then, we show that regular languages and context-free languages are not closed under the duplication, k-pseudo-duplication and reverse-duplication operations. Furthermore, we show that the class of context-sensitive languages is closed under duplication, pseudo-duplication and reverse-duplication.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.217

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 head0.012
GPT teacher head0.285
Teacher spread0.272 · 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