Asian Soybean Rust Severity Sowed in Different Seasons
Bibliographic record
Abstract
In on-farm trials, the foliolar severity of Asian soybean rust was evaluated in 44 areas, in three regions of Mato Grosso sown in December (2019) and February (2020). Several susceptible cultivars were used in different crop systems; insect pests and weeds were controlled with different management systems by the farmers. Forty soybean leaflets from four plots replications, demarcated at random in each field were taken. In laboratory foliolar severity was appraised. For rust control in the trials conducted in February, fungicides with efficiency greater than 60% were used consisting of DMIs, QoIs and SDHIs in double or triple mixtures, always adding multisites (chlorothalonil, mancozeb, copper oxychloride). The severity was greater in the fields sown in December (4.84% than in February 0.68%). The number of fungicides spraying/ha in December was 6.4 and February 4.6. It is discussed that through the use of multisites fungicides, the mutation potential in Phakopsora pachyrhizi is reduced and that the spores from areas cultivated in February, probably due to unfavorable environment, do not survive during the soybean free-period. Our results indicate that the sowing period can be changed from the end of December to February, since multisites fungicides are always used.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".