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Record W1974457311 · doi:10.1111/jeb.12322

Variation and evolution of sex ratios at the northern range limit of a sexually polymorphic plant

2014· article· en· W1974457311 on OpenAlexafffund
Sarah B. Yakimowski, Spencer C. H. Barrett

Bibliographic record

VenueJournal of Evolutionary Biology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Ontario
KeywordsBiologyHermaphroditeDioecySex allocationContext (archaeology)Sex ratioRange (aeronautics)Sexual dimorphismEvolutionary biologyGynodioecyPhenotypic plasticityVariation (astronomy)EcologyZoologyGeneticsDemographyPopulation

Abstract

fetched live from OpenAlex

Gender strategies involve three fundamental sex phenotypes - female, male and hermaphrodite. Their frequencies in populations typically define plant sexual systems. Patterns of sex-ratio variation in a geographical context can provide insight into transitions among sexual systems, because environmental gradients differentially influence sex phenotype fitness. Here, we investigate sex-ratio variation in 116 populations of Sagittaria latifolia at the northern range limit in eastern N. America and evaluate mechanisms responsible for the patterns observed. We detected continuous variation in sex phenotype frequencies from monoecy through subdioecy to dioecy. There was a decline in the frequency and flower production of females in northerly populations, whereas hermaphrodite frequencies increased at the range limit, and in small populations. Tests of a model of sex-ratio evolution, using empirical estimates of fitness components, indicated that the relative female and male contribution of males and hermaphrodites to fitness is closer to equilibrium expectations than female frequencies. Plasticity in sex expression and clonality likely contribute to deviations from equilibrium expectations.

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 score0.465
Threshold uncertainty score0.096

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 head0.022
GPT teacher head0.194
Teacher spread0.172 · 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

Citations30
Published2014
Admission routes2
Has abstractyes

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