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Record W1994747863 · doi:10.1086/678117

Consequences of Multiple Inflorescences and Clonality for Pollinator Behavior and Plant Mating

2014· article· en· W1994747863 on OpenAlexaff
Wan‐Jin Liao, Lawrence D. Harder

Bibliographic record

VenueThe American Naturalist · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInflorescencePollenBiologyPollinatorPollinationPollen sourceNectarMatingBotanyEcology

Abstract

fetched live from OpenAlex

Angiosperms engage in distributed reproduction, producing sex organs in multiple flowers on one or more inflorescences, including on different physical individuals of clonal plants. We investigated the effects of alternative deployments of artificial flowers for pollinator behavior and simulated pollen dispersal. Plants presented 18 flowers on either one inflorescence (1-I plants) or three inflorescences (3-I plants) spaced either closely or widely. Bees often skipped inflorescences on 3-I plants, visiting an average of 1.5 fewer flowers overall than on 1-I plants. In simulations with all flowers receiving and donating pollen, this behavior caused 7% less geitonogamy for 3-I plants, contradicting a common supposition that clonality increases geitonogamy. Bees generally moved upward within inflorescences and downward between inflorescences. Consequently, in simulations, segregation of pollen receipt to lower flowers and pollen donation to upper flowers reduced self-pollination and enhanced pollen export much more for 1-I plants. Nectar volume per flower had little relevant influence on bee behavior. The observed bee responses and simulated mating results suggest that production of multiple inflorescences and clonality promote pollination quality when flowers simultaneously receive and donate pollen, whereas a single large inflorescence is advantageous when segregation of sex roles among flowers reduces geitonogamy effectively.

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 score0.460
Threshold uncertainty score0.366

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 studies0.0000.001
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 head0.039
GPT teacher head0.246
Teacher spread0.207 · 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

Citations35
Published2014
Admission routes1
Has abstractyes

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