Predicting Asian Soybean Rust Epidemics Based on Off-Season Occurrence and El Niño Southern Oscillation Phenomenon in Paraná and Mato Grosso States, Brazil
Bibliographic record
Abstract
The study aimed to propose models to predict Asian soybean rust epidemics based on both the occurrence of the disease in the period between seasons and the climate variability index, which is influenced by the El Niño Southern Oscillation (ENSO) phenomenon. The data used to develop these models were obtained from 11 crop seasons, distributed among six regions of Paraná and twelve regions of Mato Grosso which was determined by the National Institute for Space Research (INPE). The three-dimensional model was obtained from linear and quadratic polynomial regression analyses, considering the following climatic variables as independent (Y axis): Rainfall (PP), Standardized Precipitation Index (SPI), Southern Oscillation Index (SOI) and Temperature on the sea surface (SST Niño 3.4). The independent variable (X axis) was the number of occurrences of rust in the off-season, and the dependent variable (Z axis) was defined as rust occurrences during the season, which were reported by the Anti-rust Consortium. The best model that explains the epidemic of the disease during the season in Paraná state was composed by Rainfall or SST Niño 3.4 variable as the Y axis. The best model for Mato Grosso state used SST Niño 3.4 or SOI variable. The variable number of occurrences in the off-season significantly influenced the model, indicating the potential use of this variable and meteorological variables on a macro scale to predict epidemics even before the start of the season.
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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 | 0.001 | 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) | 0.000 | 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".