Field evaluation of glutathione and glutamic acid as host marking pheromones for control of Tephritid fruit flies in a mango orchard in Kenya
Bibliographic record
Abstract
BACKGROUND: Tephritid fruit flies are a major constraint to the global production of horticultural crops. In Africa, an array of native and alien invasive fruit fly species represents a key challenge to the horticultural industry. In an effort to develop a safer management tool for these pests, we previously identified glutathione (GSH) and glutamic acid (GA), as the host marking pheromones (HMPs) of Ceratitis cosyra and Ceratitis rosa, respectively. Here, we report on the effectiveness of these compounds (GSH and GA) in reducing natural fruit fly population infestations in mango orchards. Mango trees at two different agroecological zones in Kenya were sprayed with HMPs, and the fruits sampled periodically and assessed for fruit fly emergence. Fruit fly emergence data were compared to two controls, the positive control using spot spraying of food bait (SS), and the negative control using water (W). RESULTS: The two HMPs and SS substantially decreased fruit fly emergence from the sampled mangoes. GSH and GA treated mangoes showed reduced C. cosyra and C. rosa infestation by ~70-75% relative to control (W), and with variation noted in the pheromone applied and the recovered fruit fly species. The adult emergence in pheromone-treated plants compared favorably with the positive control SS. However, the HMPs had little impact on reducing Bactrocera dorsalis infestation of mangoes. CONCLUSION: The decrease in fruit fly emergence in sampled mango fruits from HMP treated trees corroborate previous laboratory results and support the prospect of using HMPs in the management of African fruit fly species. © 2022 Society of Chemical Industry.
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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 | 0.001 | 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".