Effect of climatic conditions on organic acid composition of some wines obtained from different sources
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".