MétaCan
Menu
Back to cohort
Record W4387164555 · doi:10.1126/sciadv.adf5559

Interplay between male quality and male-female compatibility across episodes of sexual selection

2023· article· en· W4387164555 on OpenAlexaff
Hayat Mahdjoub, Rassim Khelifa, Jeannine Roy, Sonja H. Sbilordo, Valérian Zeender, Jhoniel Perdigón Ferreira, Natalia Gourgoulianni, Stefan Lüpold

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsConcordia University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsBiologySexual selectionMate choiceTraitMating preferencesDirectional selectionEvolutionary biologyReproductive successSelection (genetic algorithm)Disruptive selectionGenetic variationMatingGeneticsNatural selectionDemographyPopulationGene

Abstract

fetched live from OpenAlex

The processes underlying mate choice profoundly influence the dynamics of sexual selection and the evolution of male sexual traits. Consistent preference for certain phenotypes may erode genetic variation in populations through directional selection, whereas divergent preferences (e.g., genetically compatible mates) provide one mechanism to maintain such variation. However, the relative contributions of these processes across episodes of selection remain unknown. Using Drosophila melanogaster , we followed the fate of male genotypes, previously scored for their overall reproductive value and their compatibility with different female genotypes, across pre- and postmating episodes of selection. When pairs of competitor males differed in their intrinsic quality and their compatibility with the female, both factors influenced outcomes from mating success to paternity but to a varying degree between stages. These results add further dimensions to our understanding of how the interactions between genotypes and forms of selection shape reproductive outcomes and ultimately reproductive trait evolution.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.074
GPT teacher head0.394
Teacher spread0.321 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicAnimal Behavior and ReproductionFrench-language works237,207