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Record W2746902022 · doi:10.1111/evo.13334

Environmental complexity and the purging of deleterious alleles

2017· article· en· W2746902022 on OpenAlexafffund
Amardeep Singh, Aneil F. Agrawal, Howard D. Rundle

Bibliographic record

VenueEvolution · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of OttawaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsBiologySexual selectionMatingExperimental evolutionDrosophila melanogasterEvolutionary biologyGeneticsAlleleSelection (genetic algorithm)Sexual conflictAntagonistic CoevolutionEpistasisGene

Abstract

fetched live from OpenAlex

Sexual interactions among adults can generate selection on both males and females with genome-wide consequences. Sexual selection through males is one component of this selection that has been argued to play an important role in purging deleterious alleles. A common technique to assess the influence of sexual selection is by a comparison of experimental evolution under enforced monogamy versus polygamy. Mixed results from past studies may be due to the use of highly simplified laboratory conditions that alter the nature of sexual interactions. Here, we examine the rate of purging of 22 gene disruption mutations in experimental polygamous populations of Drosophila melanogaster in each of two mating environments: a simple, high-density environment (i.e., typical fly vials), and a lower density, more spatially complex environment. Based on past work, we expect sexual interactions in the latter environment to result in stronger selection in both sexes. Consistent with this, we find that mutations tend to be purged more quickly in populations evolving in complex environments. We discuss possible mechanisms by which environmental complexity might modulate the rate at which deleterious alleles are purged and putatively ascribe a role for sexual interactions in explaining the treatment differences in our experiment.

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 score0.841
Threshold uncertainty score0.313

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 head0.026
GPT teacher head0.218
Teacher spread0.192 · 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

Citations51
Published2017
Admission routes2
Has abstractyes

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