Grape Production Diagnosis in the Highlands Region of the State of Espírito Santo, Brazil
Bibliographic record
Abstract
Espírito Santo was the eighth largest Brazilian grape producing state and the sixth largest exporter. The objective of this study was to carry out a grape production diagnosis in the highlands region of Espírito Santo. The methodology consisted of field research in rural farms. The municipalities with the greatest participation in state grape production were selected. Field research was conducted in 2019 and 2020 in rural properties in the municipalities of Santa Teresa, Domingos Martins, Venda Nova do Imigrante, Alfredo Chaves and Vargem Alta, which are representative in production. Were interviewed 86 vinegrowers and this sample represented 13.7% of the number of grape producing centers in the State. The need to improve management practices, yield and grape quality was identified, with a focus on sustainability, reducing the use of pesticides, in addition to certification for organic/biodynamic grape production, increasing the quality of the material. It is necessary that Technical Assistance and Rural Extension (ATER) works also concentrate efforts to develop production systems with lower implementation and production costs, mainly related to the supporting structure of the vines and the development of cultivars more resistant and/or tolerant to pests and diseases, mainly vine downy mildew (Plasmopara viticola), which will consequently reduce production costs for the winegrower.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".