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Record W2748250270 · doi:10.5344/ajev.2017.17019

Crop Level and Harvest Date Impact Composition of Four Ontario Winegrape Cultivars. I. Yield, Fruit, and Wine Composition

2017· article· en· W2748250270 on OpenAlexaffabout
Luis Hugo Moreno Luna, Andrew G. Reynolds, Frederick Di Profio

Bibliographic record

VenueAmerican Journal of Enology and Viticulture · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsBrock University
Fundersnot available
KeywordsComposition (language)CultivarYield (engineering)WineCropAgronomyHorticultureBiologyFood scienceArt

Abstract

fetched live from OpenAlex

Pinot gris, Riesling, Cabernet franc, and Cabernet Sauvignon vines from a single vineyard in Virgil, Ontario were subjected to two crop levels, full crop (FC) and half crop (HC), in which crop was reduced in HC to one basal cluster per shoot at veraison. Crop level treatments were combined with three harvest dates: T0 (commercial harvest), T1 (three weeks after T0), and T2 (six weeks after T0), all with subsequent wine production. Berries, must, and wine were analyzed. Reductions in crop led to an increase in Brix, reduced yield, and cluster number in all cultivars, and increased cluster weight in Cabernet franc. Delayed harvest date also increased Brix and pH and reduced titratable acidity (TA) and berry weight. Effect of harvest date in berries carried over to musts and wines: increased pH and TA in T2 treatments was associated with reduced anthocyanins, phenols, and color intensity in red cultivars. Delayed harvest date had a greater magnitude of effect than crop reduction; thus, maintaining a full crop with a later harvest date might have a greater beneficial impact on potential wine quality than reducing crop level.

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 score0.926
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.056
GPT teacher head0.304
Teacher spread0.248 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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