How to Start with a Clean Crop: Biopesticide Dips Reduce Populations of Bemisia tabaci (Hemiptera: Aleyrodidae) on Greenhouse Poinsettia Propagative Cuttings
Bibliographic record
Abstract
(1) Global movement of propagative plant material is a major pathway for introduction of Bemisia tabaci (Hemiptera: Aleyrodidae) into poinsettia greenhouses. Starting a poinsettia crop with high pest numbers disrupts otherwise successful biological control programs and widespread resistance of B. tabaci against pesticides is limiting growers' options to control this pest; (2) This study investigated the use of several biopesticides (mineral oil, insecticidal soap, Beauveria bassiana, Isaria fumosorosea, Steinernema feltiae) and combinations of these products as immersion treatments (cutting dips) to control B. tabaci on poinsettia cuttings. In addition, phytotoxicity risks of these treatments on poinsettia cuttings, and effects of treatment residues on mortality of commercial whitefly parasitoids (Eretmocerus eremicus and Encarsia formosa) were determined; (3) Mineral oil (0.1% v/v) and insecticidal soap (0.5%) + B. bassiana (1.25 g/L) were the most effective treatments; only 31% and 29%, respectively, of the treated B. tabaci survived on infested poinsettia cuttings and B. tabaci populations were lowest in these treatments after eight weeks. Phytotoxicity risks of these treatments were acceptable, and dip residues had little effect on survival of either parasitoid, and are considered highly compatible; (4) Use of poinsettia cutting dips will allow growers to knock-down B. tabaci populations to a point where they can be managed successfully thereafter with existing biocontrol strategies.
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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".