Entomopathogenic fungi in greenhouse ecosystems:present and future roles
Bibliographic record
Abstract
There are several biopesticides based on entomopathogenic fungi available in the market for use against insect pests in greenhouse ecosystems. Although most are compatible for use with predators and parasitoids in greenhouse ecosystems, much more research is needed to determine the intraguild interactions for each combination of host, pathogen, predator, and parasitoid. Our research has demonstrated that, although direct effects on the predators could be demonstrated in laboratory bioassays, different results were found under greenhouse conditions, indicating that results obtained in the laboratory may be a poor predictor of what occurs in the greenhouse. In both cases, additive effects were obtained under greenhouse conditions, demonstrating compatibility. In addition, there is increasing evidence that entomopathogenic fungi have significant potential for dual management of invertebrate pests and plant pathogens. Our studies demonstrated that 3 species of Lecanicillium had significant effects on both aphids and cucumber powdery mildew, Sphaerotheca fuliginea; that the fungus Paecilomyces fumosoroseus was compatible with a mind predator, Disyphus hesperus, when used concurrently against greenhouse whitefly, Trialeurodes vaporariorum; and that Lecaniciulliurn longisporum was compatible with a predatory midge, Aphidoletes aphidimyza when used concurrently against green peach aphids.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".