MétaCan
Menu
Back to cohort
Record W4232221203 · doi:10.1007/978-3-319-28755-3_7

Mutation

2016· book-chapter· en· W4232221203 on OpenAlexaff
Donald R. Forsdyke

Bibliographic record

VenueEvolutionary Bioinformatics · 2016
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeneticsGeneBiologySynonymous substitutionMutationGenomeSilent mutationAmino acidPhenotypeMutation rateCodon usage biasMissense mutation

Abstract

fetched live from OpenAlex

Anatomical or physiological variations that are inherited are due to inherited changes (mutations) in base sequences of DNA. Mutations that change genes can affect the conventional phenotype resulting in linear within-species evolution, often under the influence of natural selection (species survival). These changes associate with amino-acid-changing (non-synonymous) mutations in the first or second bases of triplet codons. DNA mutations can also result in changes in the genome phenotype . These changes associate with synonymous (non-amino-acid-changing) mutations, usually in the third bases of codons. Each gene in a genome has distinctive rates of acceptance of amino-acid-changing and synonymous mutations, which are positively correlated. A gene with few amino-acid-changing mutations also has few synonymous mutations. A gene with many amino-acid-changing mutations also has many synonymous mutations. Two genes may be closely located but differ greatly in their mutation acceptance rates. Thus, each gene is an independent mutational entity. Synonymous mutations, and correlated mutations in regions that do not encode amino acids, may be important for changing the ‘pattern’ of a genome, so sparking the onset of branching evolution (species arrival). By eliminating redundant information, oligonucleotide frequency patterns should provide rapid and more sensitive indices of species differences than direct sequence comparisons. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.072
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0720.039

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 head0.009
GPT teacher head0.207
Teacher spread0.197 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEvolutionary BioinformaticsSame topicGenomics and Phylogenetic StudiesFrench-language works237,207