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Record W6948179331 · doi:10.5061/dryad.42db3

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

2016· dataset· en· W6948179331 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2016
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsTrent University
Fundersnot available
KeywordsMatingSexual selectionSelection (genetic algorithm)Mate choiceTraitMating system

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 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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.023
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.021

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 head0.049
GPT teacher head0.214
Teacher spread0.165 · 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 designObservational
Domainnot available
GenreDataset

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

Citations0
Published2016
Admission routes1
Has abstractyes

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