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Record W2774421620 · doi:10.1021/acs.jafc.7b04946

Quantitating Volatile Phenols in Cabernet Franc Berries and Wine after On-Vine Exposure to Smoke from a Simulated Forest Fire

2017· article· en· W2774421620 on OpenAlexafffund
Matthew Noestheden, Eric G. Dennis, Wesley F. Zandberg

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaMitacsCanada Foundation for Innovation
KeywordsVeraisonSmokeWineChemistryFood scienceBerryRipeningFermentationPhenolsHorticultureBotanyBiologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Smoke-taint is a wine defect linked to organoleptic volatile phenols (VPs) in Vitis vinifera L. berries that have been exposed to smoke from wildland fires. Herein, the levels of smoke-taint-associated VPs are reported in Cabernet Franc berries from veraison to commercial maturity and in wine after primary fermentation following on-vine exposure to simulated wildland fire smoke. VPs increased after smoke exposure were rapidly stored as acid-labile conjugates, and the levels of both free VPs and conjugated forms remained constant through ripening to commercial maturity. An increase in total VPs after primary fermentation suggested the existence of VP-conjugates other than the acid-labile VP-glycosides already reported. This conclusion was supported with base hydrolysis on the same samples. Relative to published results, the data suggested a multifactorial regional identity for smoke-taint and they inform efforts to produce a predictive model for perceptible smoke-taint in wine based on the chemical composition of smoke-exposed berries.

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 score0.723
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.225
Teacher spread0.207 · 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

Citations34
Published2017
Admission routes2
Has abstractyes

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