Evaluating the antifungal potential of six essential oils against fungi causing wiltand dieback on olive trees
Why this work is in the frame
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Bibliographic record
Abstract
Essential oils, known for their antimicrobial properties, are being investigated as natural alternatives to synthetic fungicides in agriculture. This study aimed to assess the chemical composition of six commercial essential oils (clove, tea tree, rosemary, thyme, oregano, and garlic) and to evaluate their fungistatic and/or fungicidal activity against six phytopathogenic fungi that cause significant damage to olive trees in Tunisia. For this purpose, the essential oils' qualitative and quantitative chemical compositions were analyzed using gas chromatography-mass spectrometry. The antifungal activity was assessed using the poisoned substrate method at different concentrations (250, 500, 1000, and 4000 ppm). Results showed that the chemical composition analysis revealed that monoterpenoids were the dominant fraction in all oils except clove and garlic, which were primarily composed of eugenol (96.28%) and trisulfide (31.97%), respectively. The antifungal activity results showed that lower concentrations (250, 500, 1000 ppm) of tea tree, rosemary, thyme, and oregano oils had limited inhibitory effects on the tested fungi. However, Biscogniauxia mediterranea was highly sensitive to clove, garlic, and rosemary oils at 4000 ppm. Fusarium oxysporum, Fusarium solani, Verticillium dahliae, and Lasiodiplodia theobromae were mainly inhibited by clove oil at concentrations ranging from 500 to 4000 ppm, while Rhizoctonia bataticola was inhibited by clove and garlic oils at high concentrations. In conclusion, among the tested essential oils, clove oil demonstrated the highest antifungal efficacy, making it a promising natural alternative to synthetic fungicides for controlling olive tree phytopathogenic fungi.
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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 | 0.000 | 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) | 0.000 | 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 it