Influence of Leaf Wetness Duration and Temperature on Infection of Grape Leaves by <i>Elsinoë ampelina</i> under Controlled and Vineyard Conditions
Bibliographic record
Abstract
On susceptible varieties, indirect damage to vines infected by Elsinoë ampelina range from reduced vigor to complete defoliation while, on berries, damage ranges from reduced quality to complete yield loss. Limited knowledge about the relationship between weather conditions and infection makes anthracnose management difficult and favors routine application of fungicides. The influence of leaf wetness duration and temperature on infection of grape leaves by E. ampelina was studied under both controlled and vineyard conditions. For the controlled conditions experiments, the five youngest leaves of potted vines (Vidal) were inoculated with a conidia suspension and exposed to combinations of six leaf wetness durations (from 0 to 24 h) and six constant temperatures (from 5 to 30°C). A week after each preset infection period, the percent leaf area diseased (PLAD) was assessed. At 5°C, regardless of the leaf wetness duration, no disease developed. At 10 and at 15 to 30°C, the minimum leaf wetness durations were 4 and 6 h, respectively. Above the minimum wetness duration, at temperatures from 10 to 30°C, PLAD increased linearly, with increasing leaf wetness up to 12 h, and then at a lower rate from 12 to 24 h. The optimal temperature for infection was 25°C. Relative infection was modeled as a function of both temperature and wetness duration using a Richards model (R2 = 0.93). The predictive capacity of the model was evaluated with data collected in experimental vineyard plots exposed to natural wetness durations or artificial wetness durations created using sprinklers. In total, 264 vineyard infection events were used to validate the controlled experiments model. There was a linear relationship between the risk of infection estimated with the model and the observed severity of anthracnose (R2 = 90); however, the model underestimated disease severity. A risk chart was constructed using the model corrected for vineyard observations and three levels of risk, with light, moderate, and severe risks corresponding to ≤5, >5% to ≤25, and >25% leaf area diseased, respectively. Overall, 93.9% of 132 independent observations were correctly classified, with 100, 29.4, and 9.4% of the light, moderate, and severe risks, respectively.
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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".