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Record W2427673477 · doi:10.1002/bies.201600074

Evolution of sex: Using experimental genomics to select among competing theories

2016· review· en· W2427673477 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioEssays · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsContext (archaeology)Evolutionary biologyGenomicsSelection (genetic algorithm)Adaptation (eye)BiologyEvolution of sexual reproductionVariance (accounting)Experimental evolutionGenetic FitnessGenomeComputer scienceComputational biologyBiological evolutionGeneticsMachine learning

Abstract

fetched live from OpenAlex

Few topics have intrigued biologists as much as the evolution of sex. Understanding why sex persists despite its costs requires not just rigorous theoretical study, but also empirical data on related fundamental issues, including the nature of genetic variance for fitness, patterns of genetic interactions, and the dynamics of adaptation. The increasing feasibility of examining genomes in an experimental context is now shedding new light on these problems. Using this approach, McDonald et al. recently demonstrated that sex uncouples beneficial and deleterious mutations, allowing selection to proceed more effectively with sex than without. Here we discuss the insights provided by this study, along with other recent empirical work, in the context of the major theoretical models for the evolution of sex.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.311
Teacher spread0.293 · 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