Formulation of Entomopathogenic Nematodes for Above-Ground Use Against Tomato Leaf Miner, Phthorimaea absoluta
Bibliographic record
Abstract
The tomato leaf miner Phthorimaea (syn. Tuta) absoluta Meyrick (Lepidoptera: Gelechiidae) is invasive in many agricultural regions. Its larvae feed inside leaf mines or tomato fruits and are difficult to reach with plant protection products. In contrast, entomopathogenic nematodes (EPNs) are highly virulent and can search for larvae. The challenge is to formulate EPNs to remain protected on a sprayed leaf surface until they enter the mines. We tested 11 formulations, including 2 oils, 4 gels or thickeners, 2 surfactants, 2 UV protectants, water, and combinations with and without the EPN Steinernema carpocapsae (Weiser) RW14-G-R3a-2 (Rhabditida: Steinernematidae), under non-closed low-humidity conditions. Six tomato leaf experiments with 1000 EPNs sprayed per leaf showed that EPNs in 0.25 to 0.5% canola oil or in 5% alkyl polyglycoside surfactant were the most effective (26 to 37%). Other formulations and an insecticide had less or no effect. Seven other bioassays showed that most formulations did not adversely affect EPN survival or pathogenicity. We assume that formulations of EPNs can be further improved, such as with other flowable gels or combinations, and encourage investment in the development of practical and economic protective sprayable formulations of EPNs against leaf miners to reduce reliance on chemical insecticides.
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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".