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THE EVOLUTION OF DOMINANCE IN SPOROPHYTIC SELF-INCOMPATIBILITY SYSTEMS. II. MATE AVAILABILITY AND RECOMBINATION

2009· article· en· W2145028019 on OpenAlexafffund
Daniel J. Schoen, Jeremiah W. Busch

Bibliographic record

VenueEvolution · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Reproductive Biology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyAllelePollenLocus (genetics)Dominance (genetics)GeneticsEvolutionary biologyOutcrossingGeneEcology

Abstract

fetched live from OpenAlex

Sporophytic self-incompatibility (SSI) is a self-pollen recognition system that enforces outcrossing in plants. Recognition in SSI systems is typically controlled by a complex locus (S-locus) with separate genes that determine pollen and stigma specificity. Experimental studies show that S-alleles can be dominant, recessive, or codominant, and that the dominance level of a given S-allele can depend upon whether pollen or stigma specificity is examined. Here and in the companion paper by Llaurens and colleagues, the evolution of dominance in single-locus SSI is explored using numerical models and simulation. Particular attention is directed at factors that can cause S-allele dominance to differ in pollen versus stigma. The effect of recombination between the S-locus and modifier locus is also examined. The models predict that limitation in the number of compatible mates is required for the evolution of S-allele dominance in the stigma but not in the pollen. Tight linkage between the S-locus and modifier promotes the evolution of S-allele dominance hierarchies. Model results are interpreted with respect to published information on the molecular basis of dominance in SSI systems, and reported S-allele dominance relationships in a variety of species. These studies show that dominant S-alleles are more common in the pollen than in the stigma, a pattern that when interpreted in light of model predictions, suggests that mate limitation may be relatively infrequent in natural populations with SSI.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.212
Teacher spread0.208 · 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

Citations21
Published2009
Admission routes2
Has abstractyes

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