Fungicide and Clay Treatments for Control of Powdery Mildew Influence Wine Grape Microflora
Bibliographic record
Abstract
There is very little information on the interaction of wine grape microflora with fungicides used to control grape diseases. The objective of this study was to determine how fungicides used in a standard grape pest management program and an experimental clay being developed for control of powdery mildew affect grape microflora. Grape leaves and fruit were surveyed for bacteria, fungi and yeast six times over the growing season in 2000 after treatment with clay or fungicides. In 2001 only clay was studied for control of powdery mildew in `Chancellor' grapes. The total number of propagules present on untreated leaf and fruit tissue were 76% bacteria, 14% yeast, and 9% fungi. Fungicides used for grape disease control significantly reduced epiphytic fungi ( P < 0.0001), bacteria ( P = 0.03), and yeast ( P = 0.0001) on grape berries and epiphytic fungi ( P < 0.0001), and yeast ( P = 0.03) on leaves. The clay treatment had no detectable effect on grape microflora because no significant differences were recorded between clay or untreated grape berries or leaves on any of the sampling dates. Over the growing season the fungicide spray program reduced incidence and severity of powdery mildew better than clay. Clay controlled powdery mildew on `Chancellor' fruit in 2000 and 2001.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".