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Record W2849084705 · doi:10.5539/jas.v10n8p388

Climatic Variables and Their Effects on Phenolic Maturation and Potassium Uptake in Cabernet Sauvignon Wines

2018· article· en· W2849084705 on OpenAlexvenueno aff
Tiago Stein, Ivan Ricardo Carvalho, Renata Gimenez Sampaio Zocche, Suziane Antes Jacobs, Vinícius Jardel Szareski, Fernando Zocche, Keila Garcia Aloy, Lucas de Vargas dos Santos, Tiago Corazza da Rosa, 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
KeywordsPotassiumChemistryTitratable acidWineHorticulturePolyphenolBotanyFood scienceAntioxidantBiochemistryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

The aimed at identifying and understanding the relationships of phenolic maturation and potassium uptake dynamics jointly with climatic variables for Cabernet Sauvignon variety. The experiment was carried out in Dom Pedrito, Rio Grande do Sul-Brazil, in the 2016 growing season. The experimental design was randomized blocks with treatments arranged in three replicates. The physical-chemical characteristics were measured: Density, Glucometric degree, Hydrogen ionic potential, Titratable Total Acidity, Total polyphenol index, Potassium, Phenolic maturity index. The periods preceding grapes phenolic maturation directly influence the physical and chemical conformity of must and wine. Potassium, pH and total acidity directly influence the poor phenolic maturation of Cabernet Sauvignon. Minimum and maximum air temperature, thermal amplitude, incident solar radiation and accumulated rainfall interfere in the photosynthetic dynamics, potassium accumulation in the grapes and phenolic maturation of Cabernet Sauvignon.

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 score0.959
Threshold uncertainty score0.328

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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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