Defining Niagara Terroir by Chemical and Sensory Analysis of Chardonnay Wines from Various Soil Textures and Vine Sizes
Bibliographic record
Abstract
Five Chardonnay vineyards in the Niagara Peninsula were delineated according to soil texture using global positioning systems and geographic information systems (GPS/GIS). For three vintages, sentinel vines were classified based on weight of cane prunings (vine size) and the soil texture in which they were grown. Wines were made from vine size × soil texture combinations for the five sites and three vintages. Musts were analyzed for soluble solids, titratable acidity (TA), and pH, and wines analyzed for ethanol, TA, pH, and total phenols. Several within-site differences between vine size or soil texture were observed for must and wine composition, but there were few consistent trends. Wines were analyzed sensorially by descriptive analysis. Soil texture effects were evident at three sites (1999), four sites (2000), and three sites (2001). Three sites in 1999 and two sites in 2000 and 2001 demonstrated vine size effects. No consistent soil texture or vine size effects were observed within any site. Principal component analysis was used to discern trends in sensory data. No clear trends with respect to soil texture or vine size were obvious in 1999, but 2000 wines separated on the basis of site (Lakeshore versus Lake Plain/Escarpment) and 2001 wines separated based on soil textures, whereby wines from Lakeshore sites (2000) or clay textures (2001) were associated with citrus, vegetal, earthy, and astringency descriptors. Vintage and site effects were evident when comparing the 2000 and 2001 vintages; 2000 wines were characterized by citrus, vegetal, and earthy descriptors, while 2001 wines were associated with floral and melon descriptors. Results suggest that although soil texture and vine size impact wine sensory attributes, site and vintage appear to play the major roles.
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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) | 0.001 | 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".