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Record W4313526327 · doi:10.1021/acs.jafc.2c06831

Understanding the Sensitivity of Grape Terpenes to Jasmonates Using <i>In Vitro</i> Culture and <i>In Vivo</i> Vineyard Experiments

2023· article· en· W4313526327 on OpenAlexafffund
Joshua VanderWeide, Chelsea Harris, Wesley F. Zandberg, Simone D. Castellarin

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsTerpeneBerryVeraisonMethyl jasmonateVineyardHorticultureBotanyChemistryCultivarFood scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

Terpene volatiles define the flavor of terpenic grape cultivars. However, grape terpene concentrations can vary 2- to 3-fold across seasons and vineyards, impacting vintage quality. The plant hormone methyl jasmonate (MeJA) stimulates grape terpene production but is expensive and can decrease berry weight and maturity. The synthetic jasmonate prohydrojasmon (PDJ) is cost-effective yet has not been evaluated on grape maturity and terpene production. Here, we performed in vitro (berry culture) and in vivo (vineyard) experiments using Gewürztraminer ( Vitis vinifera L.) to evaluate the time- and concentration-dependent sensitivity of maturity parameters and terpene content to MeJA and PDJ. In vitro berry weight was reduced by high MeJA and PDJ concentration across timings. Terpenes were most sensitive to low MeJA concentration at veraison (increased 24-fold) in vitro . Moderate PDJ concentration applied at veraison doubled (increased twofold) terpene concentration in vivo without impacting berry weight or maturity. In conclusion, PDJ may provide a solution to mitigate seasonal variability in terpene production in terpenic grape cultivars.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.065
GPT teacher head0.275
Teacher spread0.210 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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