Comparative Severity Analysis of Asian Soybean Rust in Soybean Farms Sown in December and February in Mato Grosso, 2018/19 Growing Season
Bibliographic record
Abstract
The objective of this work was to compare the leaflet severity of Asian soybean rust in farms sown in December and February in the state of Mato Grosso. In the survey, 28 fields were sampled in 14 counties in the North, West and South regions of the state. A total of 40 leaflets were collected per plot, in randomized treatments with four replication and three crop phenological stages. Leaflet severity was assessed according to a diagrammatic scale. The data were expressed as leaflet severity, submitted to linear regression analysis, calculated the area under the disease progress curve (AUDPC) considering the three phenological stages sampled, and the means compared by the Tukey’s test. Leaflet severity was significantly higher in the fields sown in December than in February, as well the number of fungicides sprayings. Our results indicate that the proposed change in seeding time from December to February can be implemented by significantly reducing risks and in compliance with the principles of IN 002/2015.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".