Comparative and Synergistic Influence of Extracts of Two Tropical Plants on the Activity of the Cowpea Weevil, <i>Callosobruchus chinensis</i>
Bibliographic record
Abstract
The chemical composition of ethanolic extracts of Zingiber officinale rhizome and Moringa oleifera seeds was examined and their individual and combined toxicity assayed against some aspects of the developmental biology of Callosobruchus chinensis . GC-MS revealed forty-one (41) chemical components in ethanolic extract of Z. officinale rhizome and thirteen (13) chemical components in ethanolic extract of M. oleifera seed. 1-(4-Hydroxy-3-methoxyphenyl)-dec-en-3-one (12.40%) and 1-(4-Hydroxy-3-methoxyphenyl) tetradec-4-en-3-one (9.05%) were the most abundant components ethanolic extract of Z. officinale accounting for about 22% of the total oil. Hexadecanoic acid, ethyl ester (22.33%) and 11-Octadecenoic acid, methyl ester (18.09%) were the most abundant components in M. oleifera accounting for about 40% of the total oil. Z. officinale oil was more toxic to C. chinensis than M. oleifera oil (LC 50 : Z. officinale = 24.00 µl; M. oleifera = 38.00 µl). By contrast, the median lethal time (LT 50 ) required to kill 50% of C. chinenesis by extract mixture ( Z . offcinale and M. oleifera ) (36.36 h) was significantly lower (p < 0.05) than those gotten at singular exposure of Z. officinale (64.61 h) and M. oleifera (76.44 h). Thus, the results exemplify the individual applicability of ethanolic extracts of Zingiber officinale and Moringa oleifera as C. chinensis biocide. When combined, the results confirm the synergistic potentials of the oils. This knowledge may facilitate the discovery of components that are essential in the design of an effective cum sustainable biopesticide with multiple modes of actions.
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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".