Effects of Canola Oil and Jojoba Wax Sprays on Powdery Mildew, Bunch Rot, and Vine Performance of ‘Auxerrois’ and ‘Riesling’ Grapevines
Bibliographic record
Abstract
Emulsions of jojoba wax and canola oil were tested for efficacy against grape powdery mildew (Uncinula necator) and bunch rot (Botrytis cinerea) on two Vitis vinifera grape cultivars ‘Auxerrois’ and ‘Riesling’ over a 2-year period. Jojoba-sprayed (1.0% v/v) ‘Auxerrois’ vines displayed 75-100% reductions in powdery mildew disease severity compared to water-sprayed controls. Both jojoba wax (1.0% v/v) and canola oil (0.5% and 1.0% v/v) also prevented powdery mildew infection in ‘Riesling’, and additionally reduced the incidence of bunch rot in ‘Riesling’ clusters by 68-87%. Jojoba had phytotoxic effects on greenhouse-grown ‘Auxerrois’ vines during periods where temperatures exceeded 35°C. However, vine vigor, yield, and berry composition of field-grown vines were not adversely affected. Tasters were unable to distinguish between ‘Auxerrois’ wines produced from jojoba and Kumulus (flowable sulfur) treatments. Jojoba and canola oils reduced berry and must titratable acidity and increased berry, must and wine pH. Tasters distinguished between wines from canola and Kumulus treatments, and found canola wines had more intense ‘Riesling’ aroma. Although further testing is required, jojoba and canola emulsions show promise as prophylactics of powdery mildew and bunch rot in grapevines.
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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 | 0.000 | 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".