Sensory Analysis of Riesling Wines from Different Sub-Appellations in the Niagara Peninsula in Ontario
Bibliographic record
Abstract
Potential terroir effects are described that might impact Riesling wine varietal character. In 2005, the Vintners Quality Alliance of Ontario created putative sub-appellations within the Niagara Peninsula based on soil and climate. The objective of this research was to determine differences that might validate sub-appellation designations. It was hypothesized that differences in fruit composition and wine sensory attributes would be found among these sub-appellations. This was tested in 10 commercial Riesling vineyards representative of each sub-appellation from which wines were made in 2005 and 2006. Vineyards were delineated using GPS and 75 to 80 sentinel vines were georeferenced within a sampling grid. An assumption was that vine water status would play a major role in the terroir effect; therefore, wines were made from vines of similar water status based on leaf water potential. A standard winemaking protocol was used to minimize enological effects. Descriptive analysis using a trained panel indicated that sub-appellation affected wine sensory profiles for both vintages. Thirteen aroma, flavor, and taste attributes differed among sub-appellations in 2005 and 11 aroma, flavor, and taste attributes differed in 2006. In both vintages, musts and wines also differed among sub-appellations in chemical composition (titratable acidity, pH, and free and potentially volatile terpenes). Through principal component analysis and partial least squares regression, specific sensory and chemical attributes and vineyard variables were associated with wines from the different sub-appellations. However, wines were grouped by their generalized regional designation (Lakeshore, Escarpment, or Lake Plain) within the Niagara Peninsula. Similar sensory profiles were found in these appellations, suggesting wines classified by their place of origin on the Niagara Peninsula should exhibit specific varietal characteristics regardless of growing season.
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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 | 0.001 |
| 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".