MétaCan
Menu
Back to cohort

DIFFERENTIAL SELECTION BETWEEN THE SEXES AND SELECTION FOR SEX

2011· article· en· W1926990393 on OpenAlexaff
Denis Roze, Sarah P. Otto

Bibliographic record

VenueEvolution · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of British Columbia
FundersAgence Nationale de la Recherche
KeywordsBiologySelection (genetic algorithm)Sexual selectionEvolutionary biologyLocus (genetics)GeneticsSexual conflictSexual dimorphismZoologyGeneMachine learning

Abstract

fetched live from OpenAlex

Anisogamy is known to generate an important cost for sexual reproduction (the famous "twofold cost of sex"). However, male-female differences may have other consequences on the evolution of sex, due to the fact that selective pressures may differ among the sexes. On the one hand, intralocus sexual conflict should favor asexual females, which can fix female-beneficial, male-detrimental alleles. On the other hand, it has been suggested repeatedly that sexual selection among males may help to purge the mutation load, providing an advantage to sexual females. However, no analytical model has computed the strength of selection acting on a modifier gene affecting the frequency of sexual reproduction when selection differs between the sexes. In this article, we analyze a two-locus model using two approaches: a quasi-linkage-equilibrium (QLE) analysis and a local stability analysis, whose predictions are verified using a multilocus simulation. We find that costly sex can be maintained when selection is stronger in males than in females, but acts in the same direction in both. Complete asexuality, however, evolves under any other form of selection. Finally, we discuss how experimental measurements of fitness variances and covariances between sexes could be used to determine the overall direction and strength on selection for sex arising from differences in selection between males and females.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.279

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 head0.234
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations24
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueEvolutionSame topicEvolution and Genetic DynamicsFrench-language works237,207