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Record W1976409934 · doi:10.4061/2011/908735

The Molecular Evolution of Animal Reproductive Tract Proteins: What Have We Learned from Mating-System Comparisons?

2011· article· en· W1976409934 on OpenAlexafffund
Alex Wong

Bibliographic record

VenueInternational Journal of Evolutionary Biology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsBiologySexual selectionReproductive tractMatingEvolutionary biologyReproductive successMating systemSelection (genetic algorithm)Female Reproductive TractExperimental evolutionPopulationGeneticsGeneZoologyDemography

Abstract

fetched live from OpenAlex

Postcopulatory sexual selection is thought to drive the rapid evolution of reproductive tract genes in many animals. Recently, a number of studies have sought to test this hypothesis by examining the effects of mating system variation on the evolutionary rates of reproductive tract genes. Perhaps surprisingly, there is relatively little evidence that reproductive proteins evolve more rapidly in species subject to strong postcopulatory sexual selection. This emerging trend may suggest that other processes, such as host-pathogen interactions, are the main engines of rapid reproductive gene evolution. I suggest that such a conclusion is as yet unwarranted; instead, I propose that more rigorous analytical techniques, as well as multigene and population-based approaches, are required for a full understanding of the consequences of mating system variation for the evolution of reproductive tract genes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0030.008
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.260
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations36
Published2011
Admission routes2
Has abstractyes

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