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GENETIC AND ENVIRONMENTAL CONTROL OF TEMPORAL AND SIZE-DEPENDENT SEX ALLOCATION IN A WIND-POLLINATED PLANT

2011· article· en· W2112427200 on OpenAlexafffund
Jannice Friedman, Spencer C. H. Barrett

Bibliographic record

VenueEvolution · 2011
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
KeywordsBiologySex allocationEvolutionary biologyEcologyGenetics

Abstract

fetched live from OpenAlex

Sex allocation in hermaphrodites can be affected by spatial and temporal variation in resources, especially in plants where size-dependent gender modification is commonplace. The evolution of sex allocation will depend on the relative importance of genetic and environmental factors governing patterns of investment in female and male function. In wind-pollinated plants, theoretical models predict a positive relation between size and male investment because of the fitness advantages associated with more effective pollen dispersal. Theory also predicts that the timing and allocation to each sex function should depend on available resources. We grew maternal half-sibling families of annual, wind-pollinated, Ambrosia artemisiifolia in sun and shade treatments to investigate these predictions. There was significant genetic variation for female and male flower production in both sun and shade treatments. Size-dependent sex allocation occurred in the direction predicted by theory, with male flower production increasing more rapidly in larger plants. The timing of sex function also varied, with significant genetic variation for dichogamy within environments and plasticity of this trait between environments. Protandry was expressed more commonly in the sun and protogyny in the shade. The occurrence of dynamic sex allocation with changing size and experimental treatment indicates the potential for adaptive responses under different ecological conditions.

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 score0.208
Threshold uncertainty score0.110

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 head0.017
GPT teacher head0.164
Teacher spread0.147 · 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

Citations31
Published2011
Admission routes2
Has abstractyes

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