Efficacy of Oil and Photosensitizer against Frankliniella occidentalis in Greenhouse Sweet Pepper
Bibliographic record
Abstract
Many common insect pests have developed resistance against the pesticides currently available, to the point where pest and disease management has become extremely difficult and expensive, increasing pressure on agriculture and food production. There is an urgent need to explore and utilize alternatives. Due to their unique mode of action, photosensitizers may be able to control insect pests effectively, especially in combination with oil-based products, without the risk of resistance build-up. In this study, the efficacy of a mineral oil-based horticultural spray oil, PureSpray™ Green (PSG), and a sodium magnesium chlorophyllin photosensitizer formulation, SUN-D-06 PS, were evaluated and compared to a registered cyantraniliprole insecticide (as positive control) and a negative control against western flower thrips (WFT), Frankliniella occidentalis. In detached leaf ingestion assays, PSG at high concentration was more effective than low concentration, causing >70% WFT mortality, whilst SUN-D-06 PS + PSG caused higher mortality than cyantraniliprole after five days of feeding. The same combination was as effective as cyantraniliprole in the contact assay. In greenhouse pepper, the photosensitizer decreased the WFT more than mineral oil applied alone, whilst a combination treatment of SUN-D-06 PS + PSG was most effective, decreasing the WFT population to fewer than four WFT per plant. SUN-D-06 PS + PSG shows promise as a sustainable, economical way of controlling WFT, with the potential to be incorporated into existing integrated pest (and disease) management (IPM) programs with ease.
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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".