Evaluating Viticulture Manipulations Effects on Glycoside Abundance and Diversity in Vancouver Island Pinot Gris
Bibliographic record
Abstract
Common techniques to modify growing conditions of wine grapes such as leaf removal, kaolin application and cluster thinning are assumed to improve grape quality. Abundance and diversity of appropriate aroma compounds are key markers of wine grape quality. Grape varietal and location specific responses to these common vineyard management techniques have not been explored on Vancouver Island. To evaluate the response of Pinot gris to common vineyard management techniques a stratified random block design encompassing three management strategies in two Vancouver Island vineyards, both growing Pinot gris over the 2018 and 2019 growing seasons was conducted. Vines were manipulated with seven treatment combinations that included reference, heavy leaf removal, kaolin application on fruit and cluster thinning. Vine physiology metrics were monitored during the growing season, while mature grapes were evaluated at harvest for total soluble solids (TSS), titratable acidity (TA), and (pH). Further, gas chromatography and mass spectrometry were used to quantify glycoside aroma compounds abundance and diversity across the treatments and vineyards. Results show heavy leaf removal decreased incidence of botrytis bunch rot and affected aroma compound abundance over the two growing seasons. Cluster thinning yielded consistent increased TSS and decreased TA at both vineyards. Kaolin did not significantly affect grape quality metrics. These results suggest heavy leaf removal and/or cluster thinning may yield significant benefit in the form of reduced botrytis pressure and improved grape quality in Vancouver Island grown Pinot Gris.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".