Efficacy of Entomopathogenic Fungi and Nematodes, and Low Risk Insecticides Against Wheat Stem Sawfly, Cephus cinctus (Hymenoptera: Cephidae)
Bibliographic record
Abstract
Entomopathogenic nematodes, fungi, and low risk insecticides were evaluated for the management of the wheat stem sawfly, Cephus cinctus Norton, in winter wheat at two locations (Devon and Western Triangle Ag Research center) in the Golden Triangle area of Montana (USA) in 2013. Two fungi (Beauveria bassiana and Metarhizium brunneum), four nematodes species (Steinernema carpocapsae, Steinernema kraussei, Steinernema feltiae, and Heterorthabditis bacteriophora), an insect growth regulator (diflubenzuron/dimilin), and a botanical-based chemical (azadirachtin/Aza-direct) were used as foliar sprays. These control agents significantly reduced damage caused by C. cinctus larvae, compared to the untreated control or treatment with water alone. No yield differences were observed among entomopathogenic fungi, nematodes, and low risk insecticides. The effectiveness of azadirachtin, diflubenzuron, the entomopathogenic fungi, and the nematodes persisted at the 28th day post application, by which time the wheat had been harvested. Stubbles collected after harvest showed significantly fewer sawfly larvae in the plots treated with entomopathogenic fungi, nematodes, diflubenzuron, and azadirachtin compared to the untreated and water spray plots, indicating that these biorational pesticides have potential to be used as alternatives to conventional pesticides for controlling the wheat stem sawfly larvae.
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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".