Lemon Grass (<i>Cymbopogon citratus</i> Stapf) Methanol and Ethanol Extracts, a Repellant with Less Insecticidal Effect on Maize Weevils (<i>Sitophilus zeamais</i> Motschulsky) Infesting Maize (<i>Zea mays</i>) Grains
Bibliographic record
Abstract
The insecticidal efficacy and repellant activity of lemon grass ( Cymbopogon citratus ) extracts were investigated within laboratory conditions of 28±2°C and 72±5% temperature and relative humidity respectively, against maize weevil Sitophilus zeamais . The results showed that both methanol and ethanol extracts of C. citratus have no contact toxicity against adult S. zeamais . At the highest concentrations (25.00%) of the extracts used, none of the S. zeamais died after 7 days of post treatments. The percentage damages on the treated seeds of maize also revealed that the percentage damages done on the treated seeds decreases as the concentration of the extracts used increases. The highest and lowest percentage damages of 17.17% and 0.67% were observed in the untreated control grains and grains treated with 25.00% concentration of methanol extract. The same pattern was observed in the ethanol extract treated grains; only with slight increases in the percentage damages of the untreated control grains and grains treated with 25.00% concentration of ethanol extract. The repellant activity of the extracts showed that at 10% concentration and above, produced 100% repellant effect on S. zeamais in both methanol and ethanol extracts. C. citratus extracts is a good repellant with less contact toxicity on S. zeamais .
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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".