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Record W2323307311 · doi:10.1021/jf504268u

Effect of Production Phase on Bottle-Fermented Sparkling Wine Quality

2014· review· en· W2323307311 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2014
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsBrock University
Fundersnot available
KeywordsWineBottleLeesMalolactic fermentationFood scienceAromaFermentationWinemakingProduction (economics)Aroma of wineFlavorAging of wineChemistryBiologyEngineeringLactic acid

Abstract

fetched live from OpenAlex

This review analyzes bottle-fermented sparkling wine research at each stage of production by evaluating existing knowledge to identify areas that require future investigation. With the growing importance of enological investigation being focused on the needs of the wine production industry, this review examines current research at each stage of bottle-fermented sparkling wine production. Production phases analyzed in this review include pressing, juice adjustments, malolactic fermentation (MLF), stabilization, clarification, tirage, lees aging, disgorging, and dosage. The aim of this review is to identify enological factors that affect bottle-fermented sparkling wine quality, predominantly aroma, flavor, and foaming quality. Future research topics identified include regional specific varieties, plant-based products from vines, grapes, and yeast that can be used in sparkling wine production, gushing at disgorging, and methods to increase the rate of yeast autolysis. An internationally accepted sensory analysis method specifically designed for sparkling wine is required.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.038
GPT teacher head0.325
Teacher spread0.287 · 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