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Record W4221035187 · doi:10.3390/plants11060825

Do Genetic Drift and Gene Flow Affect the Geographic Distribution of Female Plants in Gynodioecious Lobelia siphilitica?

2022· article· en· W4221035187 on OpenAlexaff
Hannah J. Appiah-Madson, Eric B. Knox, Christina M. Caruso, Andrea L. Case

Bibliographic record

VenuePlants · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Guelph
FundersU.S. Department of EnergyNational Institutes of HealthNational Science Foundation
KeywordsGynodioecyBiologyPopulationGene flowGenetic driftRange (aeronautics)Genetic variationSex ratioGenetic diversityEvolutionary biologyEcologyDioecyDemographyGeneticsGene

Abstract

fetched live from OpenAlex

Variation in population sex ratio is particularly pronounced in gynodioecious angiosperms. Extremely high female frequencies in gynodioecious populations cannot be readily explained by selective forces alone. To assess the contributions of drift and gene flow to extreme sex-ratio variation, we documented sex ratio and population size in 92 populations of Lobelia siphilitica across its range and genotyped plants using plastid and nuclear genetic markers. Similarity in spatial patterns of genetic and demographic variables may suggest that drift and/or gene flow have contributed to population sex-ratio variation in L. siphilitica. We found strong spatial structuring of extremely high female frequencies: populations with >50% female plants are restricted to the south−central portion of the range. However, we did not detect any spatial structuring in population size nor metrics of genetic diversity, suggesting that extreme variation in female frequency is not strongly affected by drift or gene flow. Extreme sex-ratio variation is frequently observed in gynodioecious plants, but its causes are difficult to identify. Further investigation into mechanisms that create or maintain the spatial structure of sex ratios in gynodioecious species will provide much needed insight.

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 score0.087
Threshold uncertainty score0.226

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 head0.023
GPT teacher head0.200
Teacher spread0.177 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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