Analysis of Climatic Risk Favorability of Grapevine Fungal Disease Occurrence for Santa Teresa, Espírito Santo State, Brazil
Bibliographic record
Abstract
The State of Espírito Santo, Brazil, has micro-regions with different climatic and soil conditions, which promote grapevine cultivation vine in several municipalities. However, the grape production process is strongly threatened by foliar fungal diseases, and its control increases the cost of production significantly. In turn, the use of models of prediction of disease occurrence allows the identification of regions with climatic risk potential for grapevine. Hence, the objective of this work was to analyze the agro-climatic favorability of climatic risk for occurrence of fungal diseases of downy mildew (Plasmopara viticola) and Botrytis cinerea on the grapevine for the municipality of Santa Teresa, in the state of Espírito Santo. Predictive models of favorability of downy mildew and B. cinerea were used. The number of sprayings was determined by the calendar system and by the rainfall system, according to the length of the cycle. Therefore, a series of meteorological data from 2007 to 2016 was used. The results showed that the frequency of days with low risk of mildew was 2%, medium risk 5%, high risk 93%. For B. cinerea, these values were 32%, 68%, and 0%, with low, medium and high risk, respectively. The number of required sprayings, according to the weather conditions, was lower than the number of sprayings recommended by the calendar system. The relationship between the risk of occurrence of the evaluated diseases showed a higher agro-meteorological favorability of occurrence of mildew in relation to B. cinerea.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".