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Record W2108523214 · doi:10.1086/657954

The Evolution of Ovule Number and Flower Size in Wind-Pollinated Plants

2011· article· en· W2108523214 on OpenAlexafffund
Jannice Friedman, Spencer C. H. Barrett

Bibliographic record

VenueThe American Naturalist · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOvulePollenPollinationBiologyAnemophilyInflorescenceBotanyPollen tubePollinator

Abstract

fetched live from OpenAlex

In angiosperms, ovules are "packaged" within individual flowers, and an optimal strategy should occur depending on pollination and resource conditions. In animal-pollinated species, wide variation in ovule number per flower occurs, and this contrasts with wind-pollinated plants, where most species possess uniovulate flowers. This pattern is usually explained as an adaptive response to low pollen receipt in wind-pollinated species. Here, we develop a phenotypic model for the evolution of ovule number per flower that incorporates the aerodynamics of pollen capture and a fixed resource pool for provisioning of flowers, ovules, and seeds. Our results challenge the prevailing explanation for the association between uniovulate flowers and wind pollination. We demonstrate that when flowers are small and inexpensive, as they are in wind-pollinated species, ovule number should be minimized and lower than the average number of pollen tubes per style, even under stochastic pollination and fertilization regimes. The model predicts that plants benefit from producing many small inexpensive flowers, even though some flowers capture too few pollen grains to fertilize their ovules. Wind-pollinated plants with numerous flowers distributed throughout the inflorescence, each with a single ovule or a few ovules, sample more of the airstream, and this should maximize pollen capture and seed production.

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 score0.195
Threshold uncertainty score0.997

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 head0.212
Teacher spread0.189 · 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

Citations57
Published2011
Admission routes2
Has abstractyes

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