Influence of water status on sensory profiles of Ontario Pinot noir wines
Bibliographic record
Abstract
Relationships between vine water status and wine sensory attributes were investigated in four Ontario Pinot noir vineyards in 2008–2009. Vineyards were divided into water status zones based on leaf water potential, and fruit from each zone was vinified. Sensory analysis included multidimensional scaling (MDS) and descriptive analysis (DA). MDS (2008) revealed that at least two of three fermentation replicates from individual water status categories were grouped together. MDS (2009) fully discriminated water status zones in one of four vineyards, and discriminated two of three fermentation replicates from water status categories in the other three vineyards. This pattern was confirmed by DA; there were few differences in 2008 between wines from water status zones within vineyards. In 2009 there were nine aroma and four retronasal/taste/mouthfeel differences attributable to low water status, including increased black currant, beet root, and earthy aromas plus acidity, and decreased floral and spicy aromas and earthy, red fruit and spicy flavors. There were differences in both vintages between vineyards in pepper spice and vegetal aroma (2008), earthy aroma, plus seven retronasal/taste/mouthfeel terms (2009). These data suggest that Pinot noir is responsive to vine water status on a “micro-terroir” scale, but this is highly dependent upon vintage.
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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) | 0.001 | 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 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".