MétaCan
Menu
Back to cohort
Record W2183677378

Polyphenol content and browning of Canadian icewines

2007· article· en· W2183677378 on OpenAlexaboutno aff
Paul A. Kilmartin, Andrew G. Reynolds, Vinay Pagay, Canan Nurgel, Robin M. Johnson

Bibliographic record

VenueJournal of Food Agriculture & Environment · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBrowningPolyphenolChemistryComposition (language)HorticultureGrowing seasonChemical compositionBotanyFood scienceBiologyAntioxidantBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

The polyphenol composition of a range of Ontario icewines and late harvest wines was quantified. For 1999-2000 experimental wines, the FAUX icewines (harvested above -8°C and before 18 December) had high concentrations of hydroxycinnamates (10 to 37 mg/L in Riesling; 53 to 82 mg/ L in Vidal) which dropped to concentrations below 10 mg/L for the REAL icewines (harvested at temperatures -8°C or below and after 28 December). However, in 2002 experimental icewines, high concentrations of hydroxycinnamates were seen in both REAL and FAUX wines (40 to 84 mg/L in Riesling; 106 to 129 mg/L in Vidal), but all of the grapes were harvested before 11 December. Low to moderate concentrations of total hydroxycinnamates were observed in commercial icewines (10 to 40 mg/L) and late harvest wines (5 to 60 mg/L). Freeze concentration during icewine making appears to have substantial impact upon polyphenol composition and concentration, but time of harvest and growing season appear to be equally important.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.128
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.179
Teacher spread0.148 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Food Agriculture & EnvironmentSame topicFermentation and Sensory AnalysisFrench-language works237,207