MétaCan
Menu
Back to cohort
Record W2763728965 · doi:10.1098/rsbl.2017.0518

The purging of deleterious mutations in simple and complex mating environments

2017· article· en· W2763728965 on OpenAlexafffund
Julie Colpitts, Darla Williscroft, Harmandeep Singh Sekhon, Howard D. Rundle

Bibliographic record

VenueBiology Letters · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyMatingSexual selectionSexual conflictExperimental evolutionNatural selectionDrosophila melanogasterSelection (genetic algorithm)ReplicateEvolutionary biologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract There is a general expectation that sexual selection should align with natural selection to aid the purging of deleterious mutations, yet experiments comparing purging under monogamy versus polygamy have provided mixed results. Recent studies suggest that this may be because the simplified mating environments used in these studies reduce the benefit of sexual selection through males and hamper natural selection through females by increasing costs associated with sexual conflict. To test the effect of the physical mating environment on purging, we use experimental evolution in Drosophila melanogaster to track the frequency of four separate deleterious mutations in replicate populations that experience polygamy under either a simple or structurally complex mating arena while controlling for arena size. Consistent with past results suggesting a greater net benefit of polygamy in a complex environment, two of the mutations were purged significantly faster in this environment. The other two mutations showed no significant difference between environments.

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 score0.759
Threshold uncertainty score0.286

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 head0.037
GPT teacher head0.265
Teacher spread0.228 · 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

Citations15
Published2017
Admission routes2
Has abstractyes

Explore more

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