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Record W2054563380 · doi:10.1139/b09-109

Floral nectary structure, nectar production, and carbohydrate composition in the<i>Lilium</i>Asiatic hybrid ‘Trésor’

2010· article· en· W2054563380 on OpenAlexaffvenue
Jessica Stolar, Arthur R. Davis

Bibliographic record

VenueBotany · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSepalNectarBiologyBotanyPetalCrassulacean acid metabolismStamenPollenPhotosynthesis

Abstract

fetched live from OpenAlex

Floral nectary structure, nectar production, and carbohydrate composition were compared from petals (“inner tepals”) and sepals (“outer tepals”) of Lilium Asiatic hybrid ‘Trésor’ (Liliaceae). The six nectaries each occupied a narrow furrow bordered by two convergent ridges extending adaxially from the petal and sepal base. Each sepal nectary furrow was shorter and more concealed. In both nectary types, many vascular bundles comprising xylem and phloem supplied 5.5–8 layers of nectariferous parenchyma cells below the epidermis, which lacked stomata. Transmission electron microscopy of sepal nectaries demonstrated that parts of the outer epidermal wall adhered to an intact but uplifted cuticle in nectar-secreting flowers. Both apoplastic and symplastic routes were continuous from the vascular bundles to the nectary epidermis. Starch breakdown from amyloplasts throughout the nectary likely augmented nectar production. Nectar solute concentration from another Asiatic hybrid, ‘Orange Pixie’, was also significantly higher in petals. In ‘Trésor’, significantly more nectar was available from sepals, possibly reflecting reduced evaporation from multiple nectar droplets within the covered nectary furrow. However, for both hybrids, the same quantity of nectar sugar was produced by petals and sepals. Nectar composition from petals and sepals also was alike, in ‘Orange Pixie’ averaging 67/19/14 (= sucrose/fructose/glucose) and 59/25/17, respectively, and in ‘Trésor’ averaging 68/23/10 and 62/27/12, respectively.

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 score0.943
Threshold uncertainty score0.491

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 head0.013
GPT teacher head0.188
Teacher spread0.174 · 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

Citations8
Published2010
Admission routes2
Has abstractyes

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