Managing Whitefly on Soybean
Bibliographic record
Abstract
The whitefly Bemisia tabaci (Gennadius, 1889) is a major pest species in soybean, leading to severe economic losses on this crop due to the difficulties involved on its management. Previously restricted to the Middle-west and Southeast regions of Brazil, whitefly infestations have steadily increased in the Southern state of Rio Grande do Sul, the third biggest soybean growing region of the country. Control failures and scarcity of updated information have led Brazilian soybean growers to raise excessively the number of sprays per crop season, increasing control costs and jeopardizing the long-term sustainbility of this strategy due to selection of resistant strains and potential harmful effects on the environment. The aim of this work was to evaluate the performance of different chemical insecticides in the control of B. tabaci nymphs and adults on soybean crops in two different sites, under the field conditions faced by the growers in the state of Rio Grande do Sul. The most efficient treatment for the control of B. tabaci adults was cyantraniliprole + lambda-cyhalothrin, at the doses of 100 + 7.5 g a.i. ha-1, which provided 65% of average control efficiency. As for nymph control, the most efficient treatment was acetamiprid + pyriproxyfen, at the doses of 60 + 30 g a.i. ha-1, which resulted in 67% of whitefly control in average. Two sequential sprays beginning at the infestation onset are recommended in order to enhance control efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".