Terroir of winter hardiness: bud LT<sub>50</sub>, water metrics, yield, and berry composition in Ontario Cabernet franc
Bibliographic record
Abstract
Winter hardiness may be influenced by vineyard terroir-driven factors, and vineyard zones with low water status [leaf water potential (ψ)] could be more winter hardy than vines with high water status (higher leaf ψ). Six Cabernet franc vineyards were chosen throughout the Niagara region in Ontario. Data were collected at fruit set, lag phase, and veraison [soil water content (SWC), leaf ψ], at harvest (yield components, berry composition), and three times during winter (LT50; the temperature at which 50% of buds die) in the 2010–2012 seasons. Interpolation by kriging and mapping of variables was completed using ArcGIS, and statistical analyses (linear correlation, k-means clustering, principal components analysis, multilinear regression) were performed. Spatial trends were observed in each vineyard for SWC, leaf ψ, yield components, berry composition, and LT50. Geographic information systems (GIS) and statistical analysis revealed that leaf ψ could predict LT50, with strong positive correlations between LT50 and leaf ψ values in most vineyards in 2010–2011. In the dry 2012 season, leaf ψ (particularly at veraison; range −1.3 to −1.6 MPa) was positively correlated to LT50, yield, titratable acidity (TA), pH, and Brix and negatively to SWC, color, anthocyanins, and phenols. Overall, vineyards in different appellations (Niagara Lakeshore, Lincoln Lakeshore, Four Mile Creek, Beamsville Bench) showed many similarities. These results suggest that there is a spatial component to winter injury, as with other aspects of terroir. Furthermore, this study allows for means by which to compare winter hardiness to other critical variables to better understand the terroir of the Niagara region.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 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".