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Record W2850419145 · doi:10.5539/jas.v10n8p380

Quality of Cabernet Sauvignon Wines Determined by the Variability of Climatic Attributes

2018· article· en· W2850419145 on OpenAlexvenueno aff
Tiago Stein, Ivan Ricardo Carvalho, Vinícius Jardel Szareski, Renata Gimenez Sampaio Zocche, Fernando Zocche, Keila Garcia Aloy, Lucas de Vargas dos Santos, Francine Lautenchleger, Velci Queiróz de Souza

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsWineTerroirPolyphenolChemistryGrape wineHorticultureFood scienceEnvironmental scienceAntioxidantBiology

Abstract

fetched live from OpenAlex

Essential to understand the dynamics responsible for the quality of red wines, the objective of revealing the physico-chemical and climatologic relationships that determine the quality of Cabernet Sauvignon wines. The experiment was carried out in the municipality of Dom Pedrito, Rio Grande do Sul, Brazil, in the agricultural crop of 2016. The experiment was conducted in a randomized block design where the treatments corresponded to nine microvinifications arranged in three replicates. The concentration of total polyphenols in Cabernet Sauvignon red wines is associated with anthocyanins, indices of absorbances of 420 and 520, as well as total acidity. However, the tannins are directly proportional to the total acidity of the wine. Environmental conditions with high rainfall, minimum oscillations in thermal amplitude and incident solar radiation tend to increase the hydrogenionic potential and the absorbance indices of 420 (yellow) and 620 (bluish) red wines of Cabernet Sauvignon. The thermal amplitude was preponderant to elevate levels of anthocyanins in Cabernet Sauvignon wines.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.802
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.053
GPT teacher head0.321
Teacher spread0.268 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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