Integrated Management of Tomato Whitefly Under Greenhouse Conditions
Bibliographic record
Abstract
The whitefly Bemisia tabaci (Gennadius, 1889) is a major pest species in many agricultural crops worldwide. Growers from the Vale do Caí region, in Southern Brazil (Rio Grande do Sul state) have been facing severe economic losses due to the increasing damages caused by this sucking insect in their horticultural crops. Small-scale farming in the Vale do Caí region relies heavily on horticulture, with many families involved in the activity and a consumer market spread throughout the whole state. Virus transmission and other damages inflicted by B. tabaci are especially harmful to tomato plants, and the managing of the pest currently demands two thirds of all chemical insecticides used in the region. While chemical control remains the main strategy for whitefly management on tomato crops, control failures and lack of updated information have led tomato growers to raise the number of sprays per crop cycle, with selection of resistant populations and harmful effects on the environment as consequent risks. The aim of this work was to evaluate chemical compounds and bioinsecticides in the control of B. tabaci nymphs and adults on tomato plants grown in two different greenhouses and under natural infestation conditions. The most efficient treatment for the control of B. tabaci adults was cyantraniliprole + lambda-cyhalothrin (50+30 g a.i. ha-1) with 64% of average control efficiency. As for nymph control, the most efficient treatment was the Embrapa formula, a bioinsecticide composed of 0.5% flaxseed oil + 0.5% neutral detergent + 0.3% sodium bicarbonate, which resulted in 72% of whitefly control in average.
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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) | 0.000 | 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".