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Record W2070445126 · doi:10.1139/b07-131

Nectar secretion strategy in three Japanese species: changes in nectar volume and sugar concentration dependent on flower age and flowering order

2008· article· en· W2070445126 on OpenAlexvenueno aff
Shinya Kato, Satoki Sakai

Bibliographic record

VenueBotany · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsNectarBiologyPollinatorBotanySugarStamenJaponicaPollinationHorticulturePollenFood science

Abstract

fetched live from OpenAlex

We explored how changes in nectar volume and nectar sugar concentration depend on flower age and flowering order in Gentiana triflora Pallas var. japonica (Kusnez.) Hara, Lobelia sessilifolia Lamb., and Hemerocallis middendorffii Trautv. et Mey. var. esculenta (Koidz.) Ohwi. In G. triflora var. japonica and H. middendorffii var. esculenta, change in nectar volume was small, whereas larger changes in nectar sugar concentration occurred depending on flower age. In L. sessilifolia, both the mean nectar volume and nectar sugar concentration clearly decreased with flower age. In all species, the mean nectar volume of the early-flowering group per plant was high. In terms of the change in sex allocation, the investment in male and female organs of the early-flowering group was high in G. triflora var. japonica and L. sessilifolia. We suggest that plants of G. triflora var. japonica and H. middendorffii var. esculenta secrete floral nectar for up to several days to sustain nectar volume to keep attracting pollinators while simultaneously reducing resource usage. Greater nectar secretion in the early-flowering group, in which flowers have more pollen and ovules, may contribute to greater reproductive success and may be effective for pollinators in learning the location of the plants and flowers. In addition, learning by pollinators should result in increased reproductive success of the later blooming flowers.

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 score0.686
Threshold uncertainty score0.862

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 head0.039
GPT teacher head0.205
Teacher spread0.166 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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