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Record W2897852828 · doi:10.1098/rsbl.2018.0474

Genetic trade-offs between male reproductive traits in<i>Drosophila melanogaster</i>

2018· article· en· W2897852828 on OpenAlexafffund
David C. S. Filice, Tristan A. F. Long

Bibliographic record

VenueBiology Letters · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcMaster UniversityWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyFecundityDrosophila melanogasterReproductive successOffspringGenetic variationSexual selectionSireEvolutionary biologyZoologySelection (genetic algorithm)GeneticsPopulationDemographyGenePregnancy

Abstract

fetched live from OpenAlex

, males engage in both extensive pre- and post-copulatory competition for the opportunity to mate with females and subsequently sire offspring. The selection pressure for increased male reproductive success has resulted in the evolution of a wide diversity of sexual traits. However, despite strong selection, individuals often exhibit considerable phenotypic variation in the expression of these traits, and it is unclear if any of this variation is owing to underlying genetic trade-offs. Here, using hemiclonal flies, we examine how male reproductive success covaries with their ability to induce long-term stimulation of oogenesis and oviposition in their mates, and how this relationship may change over time. We found that males from hemiclone lines with phenotypes that were more successful in a short-term reproductive 'scramble' environment were less effective at stimulating long-term fecundity in females. Furthermore, we observed that males from hemiclone lines which showed the most improvement over a longer reproductive interaction period also tended to stimulate higher long-term fecundity in females. Together, these results indicate the presence of genetic trade-offs between different male reproductive traits and offer insights into the maintenance of their variation.

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 score0.681
Threshold uncertainty score0.249

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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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
Published2018
Admission routes2
Has abstractyes

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