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Record W1989872929 · doi:10.4141/cjps08188

Variation in leaf and bud soluble sugar concentration among <i>Vitis</i> genotypes grown under two temperature regimes

2009· article· en· W1989872929 on OpenAlexvenueno aff
Trudi N. L. Grant, Imed Dami, Tingting Ji, David Scurlock, J Streeter

Bibliographic record

VenueCanadian Journal of Plant Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsRaffinoseCultivarShootSugarBiologyDormancyBotanyHorticultureSucroseGermination

Abstract

fetched live from OpenAlex

Soluble sugar accumulation was determined in the grape (Vitis spp.) cultivars Frontenac, Couderc 3309, Concord, Cabernet Franc, Traminette and Seyval grown under two temperature regimes. Shoot growth slowed under cold temperature regimes in all cultivars except Concord, which was the least responsive. Among all sugars, raffinose showed distinctive responses associated with the two temperature regimes. Under a non-acclimating temperature regime, raffinose concentrations were low and similar among cultivars, whereas under cold acclimating temperature regimes raffinose accumulation was generally higher, and cold-hardy cultivars accumulated higher concentrations than did cold-sensitive cultivars. Basal leaves and buds accumulated the most raffinose. Cabernet Franc vines exhibited no differences in sugar accumulation at different stages of development. The results suggest that raffinose accumulation might be an early step in the process of cold acclimation that coincides with slowed shoot growth, and may precede the onset of dormancy and freezing tolerance. Leaf raffinose concentration might be useful as a detection tool to distinguish various Vitis genotypes with contrasting freezing tolerance. Key words: Bud, cold acclimation, leaf, raffinose, Vitis

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)0.001
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 score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.226
Teacher spread0.211 · 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
Published2009
Admission routes1
Has abstractyes

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