MétaCan
Menu
Back to cohort
Record W2889230785 · doi:10.1098/rsbl.2018.0443

The effects of male harm vary with female quality and environmental complexity in <i>Drosophila melanogaster</i>

2018· article· en· W2889230785 on OpenAlexafffund
Alison Macpherson, Yun Li, Tania S. Barrera, Aneil F. Agrawal, Howard D. Rundle

Bibliographic record

VenueBiology Letters · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of TorontoUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologySexual conflictSexual selectionHarmCompetition (biology)Mate choiceSelection (genetic algorithm)Natural selectionDrosophila melanogasterSexual dimorphismEcologyDemographyEvolutionary biologyZoologyGeneticsSocial psychologyPsychologyMatingPopulation

Abstract

fetched live from OpenAlex

Mate competition provides the opportunity for sexual selection which often acts strongly on males, but also the opportunity for sexual conflict that can alter natural selection on females. Recent attention has focused on the potential of sexual conflict to weaken selection on females if male sexual attention, and hence harm, is disproportionately directed towards high- over low-quality females, thereby reducing the fitness difference between these females. However, sexual conflict could instead strengthen selection on females if low-quality females are more sensitive to male harm than high-quality females, thereby magnifying fitness differences between them. We quantify the effects of male exposure on low- versus high-quality females in Drosophila melanogaster in each of two environments (‘simple’ and ‘complex’) that are known to alter behavioural interactions. We show that the effects of male harm are greater for low- compared to high-quality females in the complex but not the simple environment, consistent with mate competition strengthening selection on females in the former but not in the latter environment.

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 score0.584
Threshold uncertainty score0.314

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 studies0.0000.001
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 head0.038
GPT teacher head0.254
Teacher spread0.217 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueBiology LettersSame topicAnimal Behavior and ReproductionFrench-language works237,207