The Effects of Extracts of Lantana camara (L.) and Azadirachta indica (A. Juss) on the Population Dynamics of Plutella xylostella, Brevicoryne brassicae and Hellula undalis on Cabbage
Bibliographic record
Abstract
The effects of ethanolic extract of neem, Azadirachta indica (Meliaceae), seeds and petroleum ether extract of Lantana camara leaves (Verbenaceae) on the populations of three cabbage pests, Plutella xylostella, Brevicoryne brassicae and Hellula undalis were studied. The study was conducted between January and April 2008. Extracts of the two plants were sprayed on cabbage plants to control these pests. A standard synthetic chemical insecticide (Mektin) was used as reference product. The experiment was conducted in a randomized complete block design and each treatment was replicated four times. The effects of plant extracts on the population dynamics of the pests’ species, the level of infestation and yield were assessed. Significantly more of the pests infested the control plants than the treated plants (P< 0.01). The mean weight of cabbage heads on the sprayed plots was significantly heavier than that of the control unsprayed plots. The use of A. indica seeds and L. camara leaf extracts increased yield by 37.05% and 25.80%, respectively. Spraying the cabbage plants with the plant extracts significantly reduced the numbers of pests compared with the control plants. The use of these plant extracts can be incorporated into an overall control programme of these pests.
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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".