Some Plants Showing Antagonism to Five Plant Pathogenic Fungi
Bibliographic record
Abstract
Fungi are among the main agents of plant diseases, being responsible for major losses in agriculture. The control of these microorganisms carried out using chemical compounds and numerous cases of resistance have already been observed, which makes it necessary to search for alternative methods of management of these pathogens. Therefore, the aim of this study, to evaluate the antifungal potential in plants. Twenty-four (24) plant extracts were tested for their antifungal potential against five plant pathogenic fungi: Sclerotinia sclerotiorum (Ss), Stromatinia cepivora (Sc), Fusarium oxysporum (Fox), Colletotrichum gloesporioides (Cg) and Verticillium dahlia (Vd). For the evaluation of the fungicidal potential, plant extracts were prepared by liquefying the plants in distilled water. The extracts were incorporated into PDA (Potato-Dextrose-Agar) culture medium to a final concentration of 35% and autoclaved. Then, PDA discs colonized by the aforementioned fungi were added to the center of each plate with the respective treatments (plant extracts). When all control treatments (PDA medium without plant extract) had colonized the entire Petri dish, the diameters of the fungal colonies were measured to calculate the Mycelial Growth Inhibition Index (MGI). Tests showed that all these plant extracts have some antifungal activity, ranging from 0 to 100% inhibition. In general, extracts of basil, lavender, guaco, rue, toxic cassava and black plum were the ones that stood out, with MGIs above 50%. New studies are being conducted to evaluate the activity of plant extracts without autoclaving, inhibition of sclerotia formation, to determine the minimum inhibitory concentration, as well as other parts of plants like roots and seeds, mixtures of plant extracts and in vivo antagonism tests.
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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) | 0.002 | 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".