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Record W2553284976 · doi:10.1111/jeb.13013

Correlated paternity measures mate monopolization and scales with the magnitude of sexual selection

2016· article· en· W2553284976 on OpenAlexafffund
Marcel E. Dorken, Laura E. Perry

Bibliographic record

VenueJournal of Evolutionary Biology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologySexual selectionMatingSelection (genetic algorithm)TraitMate choiceMating systemMonopolizationEvolutionary biologyEcologyComputer scienceMachine learning

Abstract

fetched live from OpenAlex

Indirect measures of sexual selection have been criticized because they can overestimate the magnitude of selection. In particular, they do not account for the degree to which mating opportunities can be monopolized by individuals of the sex that compete for mates. We introduce a measure of mate monopolization (m) based on the magnitude of correlated paternity and evaluate its ability to track changes in the magnitude of sexual selection. Simulation models were used to compare how well m tracked changes in the selection differential (s) for a trait regulating mating success. We further evaluated the association between m and s using ten replicate mating arrays of Sagittaria latifolia in which plants with contrasting alleles at microsatellite loci competed for siring opportunities. The computer models and mating arrays both demonstrated a positive linear association between m and s, supporting the utility of m as an index of sexual selection. Commonly used measures of sexual selection are not easily applied to organisms, such as the flowering plants, for which mating events are difficult to observe. The measure of mate monopolization introduced here could prove to be a useful addition to studies of sexual selection in these organisms.

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 score0.494
Threshold uncertainty score0.085

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 head0.024
GPT teacher head0.199
Teacher spread0.175 · 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

Citations18
Published2016
Admission routes2
Has abstractyes

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