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NEXT-GENERATION STUDIES OF MATING SYSTEM EVOLUTION

2012· article· en· W2104590028 on OpenAlexafffund
Michael W. Hart

Bibliographic record

VenueEvolution · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsSimon Fraser University
FundersBC Cancer AgencySimon Fraser UniversityGenome British ColumbiaCanada's Michael Smith Genome Sciences Centre
KeywordsBiologyReproductive isolationEvolutionary biologyGameteMatingMating systemSelection (genetic algorithm)Molecular evolutionGeneSexual selectionGeneticsPhylogeneticsSpermPopulation

Abstract

fetched live from OpenAlex

The specificity of mate selection can vary from wantonly indiscriminate to extraordinarily choosy, and depends in large part on molecules expressed on the surfaces of sperm and eggs. Understanding the evolution of this specificity of gamete recognition leads to important insights into the evolution of reproductive isolation and speciation. One productive area of research has focused on genes that encode gamete recognition proteins in broadcast-spawning marine invertebrates. These gene products are relatively accessible to biochemical and cellular analyses of expression and function, and they mediate almost all of the elements of mate selection and specificity between males and females of such species. However, genetic analyses of their evolution are currently limited to a few combinations of molecules and taxa, and may miss the broader view of adaptive responses to selection on mating specificity across many genes and many types of mating systems. A transcriptomic study shows how next-generation sequencing methods and analyses could relatively easily broaden such studies to more clades, deepen those studies to include more of the interacting molecular parts that mediate gamete recognition, and eventually lead to a more complete understanding of the molecular basis for mating system variation and its evolutionary response to selection.

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 score0.091
Threshold uncertainty score0.490

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 head0.063
GPT teacher head0.240
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 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
Published2012
Admission routes2
Has abstractyes

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