Spatial variability in Ontario Cabernet franc vineyards III. Relationships among berry composition variables and soil and vine water status
Bibliographic record
Abstract
The possible influence of vine water status upon berry composition was studied in ten commercial vineyard blocks of Vitis vinifera L. cv. Cabernet franc in the Niagara Peninsula, Ontario from 2005 to 2007. Soil texture, soil chemical composition, soil moisture and leaf water potential (?), as an indicator of vine water status, were determined on ? 80 sentinel vines in each vineyard. In each block, water status zones were identified in GIS-generated maps using leaf ? and soil moisture measurements. Areas of low soil and vine water status were positively correlated linearly and spatially with areas of high Brix, color intensity, anthocyanins and phenols, and were negatively correlated with titratable acidity. In most vineyards, areas of high and low color intensity were positively correlated linearly and spatially with areas of high and low anthocyanins and phenols. Temporal stability was also noticeable for several variables including soil moisture, yield, berry weight, Brix, anthocyanins, and phenols. These data suggest that low soil moisture and low vine water status zones in vineyards are related to corresponding areas of superior berry composition. These data further suggest that precision viticulture techniques may be utilized in this region to delineate vineyard sub-zones of differing quality levels.
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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 | 0.000 | 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".