Trapping whiteflies? A comparison of greenhouse whitefly (<i>Trialeurodes vaporariorum</i>) responses to trap crops and yellow sticky traps
Bibliographic record
Abstract
BACKGROUND: Greenhouse whiteflies, Trialeurodes vaporariorum (Westwood), are notorious pests that, through the reduction of crop yields and excretion of honeydew, cause significant economic losses for sweet pepper (Capsicum annuum L.) growers. Chemical and biological controls are the two most common forms of whitefly management in greenhouses. Consequently, insecticide resistance and inadequate control have rejuvenated interest in alternative tactics. In the present study, whitefly responses to trap crops (eggplant and squash) and yellow sticky traps were compared in order to identify the most effective traps for dispersing and resident adults. RESULTS: Results indicated that yellow sticky traps were most effective at trapping adult whiteflies. Significantly more dispersing whiteflies were recorded on eggplant than on squash trap crops. None of the traps significantly reduced adult populations on the main crops (peppers) compared with the control. However, yellow sticky traps did significantly reduce oviposition on peppers. CONCLUSIONS: Adult whiteflies were most effectively trapped on yellow sticky traps followed by eggplant trap crops. Further study of whitefly trapping using visual cues may enhance trapping management. Specifically, research combining yellow sticky traps with other control strategies is recommended.
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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".