Biological activity of entomopathogenic actinomycetes against lepidopteran insects (Noctuidae: Lepidoptera)
Bibliographic record
Abstract
Vijayabharathi, R., Kumari, B. R., Sathya, A., Srinivas, V., Abhishek, R., Sharma, H. C. and Gopalakrishnan, S. 2014. Biological activity of entomopathogenic actinomycetes against lepidopteran insects (Noctuidae: Lepidoptera). Can. J. Plant Sci. 94: 759–769. The aim of the present study was to identify an efficient broad-spectrum bio-pesticide for the control of lepidopteran insects from microbes in various ecological niches. A total of 111 microbes isolated from various herbal vermi-composts and organically cultivated fields were evaluated for their intracellular metabolites (ICM), extracellular metabolites (ECM) and whole culture (WC) against early instars of lepidopteran insects. Fifteen actinomycete isolates which showed insecticidal activity against 2nd instar Helicoverpa armigera were selected and further screened against Spodoptera litura and Chilo partellus. A significant broad spectrum insecticidal activity was found in the order ECM>ICM>WC against all the insects under laboratory conditions. All these actinomycete isolates also registered significant activity under greenhouse conditions on 2nd instar H. armigera. The actinomycete isolates were identified by 16S rDNA sequencing and matched with Streptomyces species using BLAST search. Among all the 15 isolates, SAI-25 (S. griseoplanus), CAI-155 (S. bacillaris) and BCA-698 (S. albolongus) showed consistent entomopathogenic activity against all the three insects suggesting their potential as broad-spectrum biocontrol agents against other lepidopterans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".