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Record W2732785732 · doi:10.1021/acs.jafc.7b01466

Grape Metabolic Response to Postveraison Water Deficit Is Affected by Interseason Weather Variability

2017· article· en· W2732785732 on OpenAlexaff
José Herrera, Uri Hochberg, Asfaw Degu, Paolo Sabbatini, Naftali Lazarovitch, Simone D. Castellarin, Aaron Fait, Giorgio Alberti, E. Peterlunger

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsUniversity of British Columbia
FundersMinistry of EconomyMinistero degli Affari Esteri e della Cooperazione Internazionale
KeywordsVeraisonBerryWater stressWineGrowing seasonHorticultureEnvironmental scienceComposition (language)ChemistryBotanyBiologyFood science

Abstract

fetched live from OpenAlex

Postveraison water deficit is a common strategy implemented to improve fruit composition in many wine-growing regions. However, contrasting results are often reported on fruit size and composition, a challenge for generalizing the positive impact of this technique. Our research investigated the effect of water deficit (WD) imposed at veraison on Merlot grapevines, during two experimental seasons (2014-2015). In both years WD resulted in reduced carbon assimilation rates and leaf shedding. However, the treatment effect on the analyzed berry parameters varied between seasons. Modification of skin metabolites was more evident in 2015 than in 2014, despite the similar soil water content and water stress physiological parameters (gas exchange, water potential) recorded in the two experimental years. Higher solar radiation and air temperature in 2015 than in 2014 hint for the involvement of atmospheric parameters in fulfilling the potential effect of WD. Our results suggest that the interaction between water availability and weather conditions plays a crucial role in modulating the grape berry composition.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.514

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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

Citations37
Published2017
Admission routes1
Has abstractyes

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