Composition and Wine Sensory Attributes of Chardonnay Musqué from Different Viticultural Treatments
Bibliographic record
Abstract
‘Chardonnay Musqué’ vines in Beamsville, Ontario, were subjected in one trial to three treatments: (1) control (hedged only); (2) basal leaf removal (BLR) and cluster thinning and; (3) cluster thinning during five stages of berry development. Berries and musts from each treatment were analyzed for soluble solids (Brix), pH, titratable acidity (TA), as well as free and potential monoterpenes (FVT and PVT). Wines produced from each treatment were evaluated by descriptive analysis for aroma and flavor intensities. FVT and PVT of berries were higher in thinned vines compared to non-thinned vines. Cluster thinning at veraison yielded fruit with the highest FVT and PVT concentrations. Leaf-pulled vines produced fruit with increased pH, reduced TA and highest FVT and PVT. Wines from BLR and thinning treatments generally had higher muscat and floral/perfumy aromas, and could be separated based on overall quality. The chemical and sensory data were incorporated into a multiple regression model used to construct a grape quality model for aromatic white Vitis vinifera grape cultivars in the Niagara Peninsula. The model developed was able to predict overall quality based on Brix, pH, and berry FVT and PVT concentration. The model was partially validated by correlations between berry FVT and PVT vs. floral and muscat aromas in wines from three previous vintages. This model has potential for use to create a more equitable payment schedule for growers contracted to wineries for the purchase of high-quality grapes.
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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".