Characterization of Niagara Peninsula Cabernet franc Wines by Sensory Analysis
Bibliographic record
Abstract
Chemical and descriptive sensory analysis was conducted on nine (2005) and eight (2006) experimental Niagara Peninsula Cabernet franc wines to illustrate differences that might support the subappellation system in Niagara. Twelve trained judges evaluated six aroma and flavor (red fruit, black cherry, black currant, black pepper, bell pepper, and green bean) and three mouthfeel (astringency, bitterness, and acidity) sensory attributes plus color intensity. Data were analyzed using analysis of variance (ANOVA), principal component analysis (PCA), and discriminant analysis. ANOVA of sensory data showed regional differences for all sensory attributes. In 2005, wines from Château des Charmes (CDC), Henry of Pelham (HOP), and Hernder sites showed highest red fruit aroma and flavor. Wines from Lakeshore and Niagara River sites (Harbour, Reif, George, and Buis) showed higher bell pepper and green bean aroma and flavor due to the cool growing conditions in proximity to the large bodies of water. In 2006, all sensory attributes except black pepper aroma were different. PCA revealed that wines from HOP and CDC sites were higher in red fruit, black currant, and black cherry aroma and flavor, and black pepper flavor, while wines from Hernder, Morrison, and George sites were high in green bean aroma and flavor. Buis wines were high in bell pepper aroma and flavor and acidity due to cooler conditions within the proximity of Lake Ontario. ANOVA of chemical data in 2005 indicated that hue, color intensity, and titratable acidity were different across the sites, while in 2006, hue, color intensity, and ethanol were different. These data indicate that there is the likelihood of substantial chemical and sensory differences between clusters of subappellations within the Niagara Peninsula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".