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Record W2737198121 · doi:10.1111/eth.12649

What is driving male mate preference evolution in Jamaican field crickets?

2017· article· en· W2737198121 on OpenAlexaff
Susan M. Bertram, Sarah Harrison, Genevieve L. Ferguson, Ian R. Thomson, Michelle J. Loranger, Mykell L. Reifer, Deborah H. Corlett, Patricia Adair Gowaty

Bibliographic record

VenueEthology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsCarleton University
Fundersnot available
KeywordsOffspringMatingBiologyMate choiceMating preferencesSexual selectionLongevityPreferenceReproductive successZoologyDemographyEcologyPopulationPregnancy

Abstract

fetched live from OpenAlex

ABSTRACT Male mating preferences are often a neglected aspect of studies on sexual selection. Male mating preferences may evolve if they provide males with direct‐fitness benefits such as increased opportunity to fertilize more eggs or indirect‐fitness benefits such as enhanced offspring survival. We tested these ideas using Jamaican field crickets, G ryllus assimilis , previously shown to exhibit male mating preferences. We randomly mated males to either their preferred or non‐preferred potential mates and then asked whether mating treatment influenced egg oviposition or offspring viability. Preferred females were not significantly more fecund and did not produce more viable eggs or offspring than non‐preferred females. Male mate preferences were therefore inconsistent with both the direct‐ and indirect‐fitness benefits hypotheses under the conditions of our experiment. Our null results leave us with an open question about what is driving the evolution of mating preferences in male crickets. Future research should explore the whether the offspring of preferred females are more attractive, have stronger immune systems, and/or experience higher adult longevity.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.864

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)0.0010.000

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 head0.058
GPT teacher head0.299
Teacher spread0.241 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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