MétaCan
Menu
Back to cohort
Record W2074225376 · doi:10.1021/jf020689m

Effects of Cultivar and Processing Method on the Contents of Catechins and Procyanidins in Grape Juice

2002· article· en· W2074225376 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 · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMaceration (sewage)CultivarCatechinChemistryFood scienceProanthocyanidinPasteurizationPolyphenolVintagePressingBotanyBiologyAntioxidantBiochemistry

Abstract

fetched live from OpenAlex

The aim of the presented work was to study the effect of pressing method, pasteurization, cultivar, and vintage on the content of (+)-catechin, (-)-epicatechin, and nine procyanidins in grape juice. The results showed that the concentration of these flavan-3-ols in the juice was influenced, in decreasing order of importance, by pressing method, cultivar, pasteurization, and vintage. Cold pressing without maceration was the least and hot pressing after maceration at 60 degrees C for 60 min the most effective method for extracting the flavan-3-ols. Pasteurization increased the concentration of catechins in cold-pressed juices, but it decreased concentrations in hot-pressed juices. The concentration of most procyanidins was increased by pasteurization. Among the white cultivars, Seyval and Niagara were highest in procyanidins and Elvira and Chardonnay were highest in catechins. Vincent, Foch, and Baco were the red cultivars highest in catechins, and Vincent also had the highest content of procyanidins.

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)0.000
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 score0.344
Threshold uncertainty score0.092

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 head0.020
GPT teacher head0.216
Teacher spread0.196 · 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