Efficacy Of Ficus religiosa And Curcuma longa Leaf Extracts On Heterotermes indicola Under Laboratory Conditions
Bibliographic record
Abstract
Termite infestations can cause severe damage to crops, resulting in yield loss and contamination of products. Using synthetic insecticides to manage termites often leads to environmental pollution and the development of resistance in termites. Since many plants are known to have insecticidal properties, this study aimed to evaluate the effectiveness of locally available plants at various concentrations for termite control. The current study was performed to evaluate the efficacy of ethanolic leaf extracts of Ficus religiosa, and Curcuma longa against Heterotermes indicola under laboratory conditions. Mortality of termites increased when feed on F. religiosa leaf extracts with minimum feeding rate at the maximum concentration (30%). The results showed that F. religiosa was more potent than C. longa, with LC50 values of 8.71 and 22.74, respectively. Chemical composition of plants extracts by Gas chromatography-mass spectrometry (GC-MS) revealed 14 and 18 compounds in selected ethanolic leaf extracts respectively. The largest chemical components based on percentage of sample identified from F. religiosa were n-Hexadecanoic acid, 1-Hexacoosene, 9,12,15-Octadecatrienal, Phytol, and dl-a-Tocopherol. The main components of C. longa were identified as, Benzenmethanol, a, a,4-trimethyl, 1,2-cis-1,5-trans-2,5-dihydroxy-4-methyl-1-(1-hydroxy-1-isopropyl) cyclohex-3, Cyclohexene, 1-methyl-4-1 (1-methylethylidene)- and 2-Methoxy-4-vinylphenol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".