Antifungal Activity and Phytotoxicity of Vitex simplicifolia Oliv. Leaves Essential Oil
Bibliographic record
Abstract
Agriculture faces many difficulties related to phytopathogenic fungi leading to quantitative and qualitative losses of crops. The use of essential oils against these fungi is one of the alternatives. This study focused on the antifungal activity and phytotoxicity of Vitex simplicifolia Oliv. leaves essential oil. Firstly, we extracted the essential oil from the leaves by hydrodistillation. Subsequently, seven fungal isolates were evaluated for their sensitivity to the essential oil on Potato Dextrose Agar (PDA) media at 0.1; 0.25 and 0.50%. Evaluation of the phytotoxicity of the essential oil was performed on sorghum seeds on a sterilized sand substrate. At the seventh day after incubation, the most sensitive fungi to the essential oil are Fusarium moniliforme, Colletotrichum graminicola and Curvularia lunata. The most sensitive fungus to the essential oil at 0.50% is Colletotrichum graminicola with 77.21% inhibition rate at the seventh day after incubation. Phytotoxicity evaluation showed that for all biomass parameters measured there is no significant difference between treatment with the essential oil and treatment with the fungicide control. At the end of this study, it appears that Vitex simplicifolia essential oil has a high antifungal activity. This essential oil has not shown toxicity on sorghum growth parameters. Therefore, this oil can be used in organic farming.
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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".