Agroclimatic Risk Zoning of Avocado (Persea americana) in the Hydrographic Basin of Paraná River III, Brazil
Bibliographic record
Abstract
Fruticulture is a prominent component of Brazilian agriculture. Studies have shown that climatic variability and its impact on the physiological development of plant species are fundamental for planning the agricultural calendar, resource conservation, and sustainable management of production. In this context, one of the first criterion for planting a crop is agroclimatic zoning, since it provides information on climate-related risks and aids decision-making and agricultural planning. The objective of this study was to carry out climatic risk zoning for avocado (Persea americana Mill.) in the basin of Paraná River III, Paraná State, Brazil. Meteorological data from 43 stations, from 1976 to 2018, were used. The climatic risk analysis was based on the requirements of the avocado for precipitation, water balance, average annual temperature, and frost tolerance. Statistical and geoprocessing techniques ensured full regional coverage of data and contributed to decision-making. The results identified favorable climatic conditions for all climatic variables in the western part of the river basin. Despite water deficits in some months, rainfall and water balance were not restrictive for avocado production in the region. Avocado tree cultivation is not recommended in the eastern part of the basin, where there is a considerable risk of frost.
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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 | 0.000 | 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 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".