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Record W2916480667 · doi:10.1111/jeb.13433

Males from populations with higher competitive mating success produce sons with lower fitness

2019· article· en· W2916480667 on OpenAlexafffund
Trinh T. X. Nguyen, Amanda J. Moehring

Bibliographic record

VenueJournal of Evolutionary Biology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiologyMatingReproductive successEvolutionary biologySexual selectionZoologyEcologyDemographyPopulation

Abstract

fetched live from OpenAlex

Female mate choice can result in direct benefits to the female or indirect benefits through her offspring. Females can increase their fitness by mating with males whose genes encode increased survivorship and reproductive output. Alternatively, male investment in enhanced mating success may come at the cost of reduced investment in offspring fitness. Here, we measure male mating success in a mating arena that allows for male-male, male-female and female-female interactions in Drosophila melanogaster. We then use isofemale line population measurements to correlate male mating success with sperm competitive ability, the number of offspring produced and the indirect benefits of the number of offspring produced by daughters and sons. We find that males from populations that gain more copulations do not increase female fitness through increased offspring production, nor do these males fare better in sperm competition. Instead, we find that these populations have a reduced reproductive output of sons, indicating a potential reproductive trade-off between male mating success and offspring quality.

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 score0.107
Threshold uncertainty score0.976

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 head0.025
GPT teacher head0.248
Teacher spread0.223 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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