Impacts of natural yield variances on wine composition and sensory attributes of <i>Vitis vinifera</i> cultivars Riesling and Cabernet Franc
Bibliographic record
Abstract
Impacts of naturally-varying yields on composition and sensory attributes of Ontario Riesling and Cabernet Franc wines were investigated. The sites investigated represented five Vintners Quality Alliance sub-appellations. A grid pattern of sentinel vines was established in each vineyard for data collection. Yields were divided into categories [low, medium, or high (LY, MY, HY)] at harvest (2010, 2011) and replicate wines were made from each. Wines were subjected to sensory sorting tasks to confirm differences between yield categories and sites, and were thereafter subjected to descriptive analysis. All HY vines had higher clusters/vine, berry weights, and Ravaz indices. The HY Cabernet Franc wines had lower colour, anthocyanins, and phenols. Sensory sorting revealed differences amongst wines and descriptive analysis demonstrated several aroma/flavour attributes between yield categories. The HY Riesling wines had less fruit and honey and higher mineral and floral attributes, whereas HY Cabernet Franc wines displayed higher bell pepper, vegetal, and herbaceous characteristics and less fruit attributes. Riesling wines from Lincoln Lakeshore North and Niagara Lakeshore sub-appellations had higher mineral or vegetal attributes, Four Mile Creek had more apple/pear, and St. Davids Bench, Beamsville Bench, and Lincoln Lakeshore South displayed higher fruit and citrus. Escarpment Bench and Four Mile Creek Cabernet Franc 2010 wines had the highest bell pepper aroma, Lincoln Lakeshore North displayed the most earthiness, and Lincoln Lakeshore South had the most cooked fruit. In 2011, cooler sites adjacent to Lake Ontario displayed higher vegetal attributes. Zones of differing yields, dependent upon magnitudes of yield differences, can result in substantially different wine sensory properties.
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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 | 0.000 | 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".