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The size of individual<i>Delphinium</i>flowers and the opportunity for geitonogamous pollination

2006· article· en· W2158272830 on OpenAlexafffund
Hiroshi S. Ishii, Lawrence D. Harder

Bibliographic record

VenueFunctional Ecology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Calgary
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyInflorescencePerianthPollinatorDelphiniumPollinationAttractionNectarBotanyHorticultureStamenPollen

Abstract

fetched live from OpenAlex

1 Animal-pollinated plants influence their mating success through characteristics of their individual flowers and the arrangement of flowers into inflorescences. Previous studies of inflorescence function have focused on flower number, so the influences of traits of individual flowers on pollinator attraction and self-pollination between flowers remain unknown. 2 To investigate the effects of flower size and number on pollinator attraction and behaviour on inflorescences, we reduced the perianth size of flowers of Delphinium bicolor Nuttall and Delphinium glaucum S. Watson. 3 Reduction in flower size decreased the number of visits per inflorescence by bumble bees (Bombus spp.), but increased the number of probes per visit. In contrast, both attraction and probes per visit increased in a decelerating manner with number of open flowers. The average number of probes per flower, which combines the effects of pollinator attraction and behaviour on inflorescences, did not differ significantly between small- and large-flowered plants, or with flower number. 4 The absence of significant variation among plants with different floral and inflorescence characteristics in visits per flower and nectar standing crop per flower indicate that bees achieved an ideal free distribution. 5 Our results suggest that large flowers reduce the incidence of geitonogamous pollination without reducing the frequency of probes per flower.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.397

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 studies0.0010.000
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 head0.035
GPT teacher head0.202
Teacher spread0.167 · 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

Citations45
Published2006
Admission routes2
Has abstractyes

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