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Record W2226346924 · doi:10.21273/hortsci.48.11.1358

A Five-year Study on the Effect of Cluster Thinning and Harvest Date on Yield, Fruit Composition, and Cold-hardiness of ‘Vidal Blanc’ (Vitis spp.) for Ice Wine Production

2013· article· en· W2226346924 on OpenAlexaboutno aff
Imed Dami, Said Ennahli, David Scurlock

Bibliographic record

VenueHortScience · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsHardiness (plants)BerryThinningCropHorticultureVineYield (engineering)AgronomyBiologyCultivar

Abstract

fetched live from OpenAlex

The aim of this 5-year study was to investigate the influence of cluster thinning (CT) and harvest date on yield components, fruit composition, and bud cold-hardiness in ‘Vidal blanc’ ( Vitis spp.) grapevines grown in northern Ohio. It is unknown whether delaying harvest of ‘Vidal blanc’ for ice wine production would impact negatively winter-hardiness. ‘Vidal blanc’ grapevines were cluster-thinned at post-fruit set [Eichhorn-Lorenz (EL) Stage 31] to two crop levels by retaining 40 (CT40) and 60 (CT60) clusters per vine. Each crop level was harvested at three dates: normal harvest (HD1), fall harvest (HD2) after the first killing frost, and winter harvest (HD3) corresponding to the typical commercial harvest for ice wine. Generally, and as expected, the high crop level CT60 increased crop weight and cropload and decreased total soluble solids and pH. Delayed harvest decreased crop weight, cluster weight, berry weight, and titratable acidity but increased total soluble solids and pH. Bud cold-hardiness, determined by thermal analysis and after two freezing events, was not different among all treatments. It was concluded that CT40 produced optimum vine size and cropload thus balanced vines. Furthermore, delaying fruit harvest in ‘Vidal blanc’ for ice wine production in the northeastern United States and Canada improves fruit composition but has no adverse influence on bud cold-hardiness.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.266
Teacher spread0.239 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

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