Spatial variability in Ontario Riesling vineyards. II. Berry composition
Bibliographic record
Abstract
Spatial variability of berry composition was studied over a 3-yr period in 10 Riesling vineyards in the Niagara Peninsula in Ontario. Vineyards were delineated using global positioning systems (GPS), and 75–80 sentinel vines were georeferenced within a sampling grid for data collection. During 2005–2007, vine water status measurements [leaf water potential (ψ)] were collected biweekly from a subset of these sentinel vines. Data were collected on soil texture and composition, soil water content (SWC; %), leaf ψ, and fruit composition. These variables were mapped using GIS software, and relationships between them were elucidated. Temporal stability in spatial patterns of soil texture and composition, SWC, leaf ψ, soluble solids (Brix), titratable acidity, and monoterpenes were examined. Spatial trends in leaf ψ and (or) SWC showed widespread evidence of temporal stability. Fruit composition variables were not as stable over a 3-yr period. Spatial trends in Brix were temporally stable in seven vineyards, free volatile terpenes were temporally stable in three vineyards, and potentially volatile terpenes were temporally stable in two vineyards. Consistent leaf ψ zones were identified, and these were temporally stable despite different climatic conditions. Furthermore, some soil variables, and particularly vine water status, may contribute significantly to the terroir effect through their effects on vine size and fruit composition. For some vineyards, many viticulture and fruit composition variables were also temporally stable. There was evidence of strong spatial relationships between leaf ψ and fruit composition, suggesting a strong relationship between berry composition and vine water status.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".