Canopy Management and Enzyme Impacts on Merlot, Cabernet franc, and Cabernet Sauvignon. II. Wine Composition and Quality
Bibliographic record
Abstract
Merlot, Cabernet franc, and Cabernet Sauvignon vines in Niagara-on-the-Lake, Ontario, were subjected to four treatments in a randomized complete block experiment: hedged control, cluster thinning at veraison (CT), basal leaf removal (BLR), and CT+BLR. Musts from each treatment replicate (CT+BLR excepted) were thereafter either left untreated or treated with one of ColorPro or Color X enzymes. In most cases, CT and CT+BLR treatments had the highest wine anthocyanin and phenol concentrations and the highest color intensities (A<sub>420</sub> + A<sub>520</sub>). Leaf removal resulted in small increases in wine color intensity and anthocyanin and phenol concentrations. Cluster thinned and BLR treatments both reduced titratable acidity (TA) and increased pH relative to controls, but BLR tended to be more effective than CT. The CT+BLR treatments usually resulted in the lowest TA and the highest pH. Enzyme treatments increased wine TA and reduced pH and typically increased color intensity, total anthocyanins, and phenols. Both viticultural and enological treatments had noteworthy impacts on individual wine phenolic compounds and anthocyanins, although the viticultural treatments were more efficacious. The viticultural treatments enhanced intensities of several aroma and retronasal descriptors (e.g., black fruit, black pepper, tobacco) and reduced those of others (e.g., bean/pea, mushroom). The CT+BLR treatment has the potential to substantially improve fruit and wine composition in cool-climate regions; negatively, excessive leaf removal could result in lowered ethanol and undesirable increases in pH. Enzyme treatment has the potential for increased color intensity, but with occasional increases in TA.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".