Epidemiology of Grape Anthracnose: Factors Associated with Defoliation of Grape Leaves Infected by<i>Elsinoë ampelina</i>
Bibliographic record
Abstract
Anthracnose is a serious disease that affects several grape cultivars. Infected leaves drop prematurely, and severe epidemics result in poor or no yield. Because the factors associated with grape defoliation in vineyards with a history of anthracnose were not well known, this study was undertaken to investigate the relationship between weather-, disease-, and host-related factors and survival of leaves. From 2006 to 2008, weather, anthracnose severity, and leaf emergence were monitored in an unsprayed experimental vineyard naturally infested with Elsinoë ampelina. Each year, two to three times weekly, the number of leaves and the proportion of leaf area diseased (PLAD) were monitored on 10 vines and 2 shoots per vine, for a total of 785 leaves. Survival analysis was used to investigate the factors influencing defoliation and to model time-to-death of grape leaves. Estimated median survival time was 117 to 121 days. Based on Kaplan-Meier estimates of survival probabilities, season type, PLAD per leaf and PLAD per shoot at first assessment, duration and amount of rain at first infection, severity of infection and leaf age at first infection and at first severe infection significantly influenced leaf survival. Based on accelerated time failure modeling, using the Weibull distribution, the most significant variables were PLAD per leaf and PLAD per shoot at first assessment, leaf age at first infection, and duration of rain. Each additional percent increase in PLAD per leaf, in PLAD per shoot, or in rainy days accelerated the time-to-death of grape leaves by 2.84, 1.02, and 0.66%, respectively, whereas for each additional day of leaf age at time of first infection, there was a 2.88% deceleration of the time to death. Results suggested that to avoid premature leaf drop, disease severity should be maintained below 25% leaf area diseased, which can be achieved by sanitation measures designed to reduce inoculum levels and by applying fungicide early in the season to prevent infection of young leaves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".