MétaCan
Menu
Back to cohort
Record W2908007170 · doi:10.3934/agrfood.2019.1.27

Effect of climatic conditions on organic acid composition of some wines obtained from different sources

2019· article· en· W2908007170 on OpenAlexaff
Soleil Chahine, Anthony Z. Tong

Bibliographic record

VenueAIMS Agriculture and Food · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsAcadia University
Fundersnot available
KeywordsTitratable acidWineChemistryFood scienceAcetic acidLactic acidOrganic acidRank correlationSpearman's rank correlation coefficientComposition (language)White WineMathematicsBiologyBiochemistryBacteriaStatistics

Abstract

fetched live from OpenAlex

Concentrations of ten important organic acids, titratable acidity, volatile acidity and pH in 41 wines were determined. This study included various types of wine produced in different wine-making climates. The data was analyzed through one-way ANOVA, Spearman’s correlation rank and principal component analysis (PCA). Significant differences of major organic acids were found among types of wine, with the <em>p</em>-values of the parameters below 0.05. Cooler-climate wines were significantly higher in titratable acidity (<em>p</em> = 3.9 × 10<sup>−5</sup>) and lactic acid (<em>p</em> = 0.0037), compared to warmer-climate wines. Spearman’s correlation analysis showed 2 pairs of parameters with moderate correlation: Lactic acid and pyruvic acid, and volatile acidity and acetic acid. PCA on types of wine revealed strong and moderate separation of groups. PCA on wines from Nova Scotia versus wines from warmer locations produced a strong separation among the red wines and no apparent separation among the white and rose wines.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.390

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 head0.005
GPT teacher head0.195
Teacher spread0.189 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAIMS Agriculture and FoodSame topicFermentation and Sensory AnalysisFrench-language works237,207