Evaluation of yeast-like fungi to protect Virginia mallow (<i>Sida hermaphrodita</i>) against <i>Sclerotinia sclerotiorum</i>
Bibliographic record
Abstract
Virginia mallow is a plant from the central and eastern states of North America. A large amount of high quality biomass obtained from V. mallow led to interest in this plant for bioenergy purposes. Unfortunately, high incidence of white rot disease caused by Sclerotinia sclerotiorum destroyed plantations of this plant. In previous attempts, various control strategies were not successful and all treatments failed (including the use of Coniothurium minitans). The aim of the study was to find and evaluate yeast-like fungi for biocontrol of S. sclerotiorum, evaluate the optimal thermal condition for biocontrol efficacy, and describe possible modes of action of potential biocontrol organisms. In dual bioassay test with 19 strains of yeast-like fungi with S. sclerotiorum, antagonistic potential were obtained. At different temperatures variations in antagonistic activity of yeast-like fungi were observed. At all tested temperatures (i.e., 4, 12, and 23°C), positive results were obtained. Extracellular enzymes were produced by the majority of antagonistic yeast, such as: amylases (the most frequent), chitinases, proteases, pectinases and xylanases. In this research, reduction in white rot symptoms on V. mallow stems was significant (P < 0.05). The highest inhibition of disease was observed after treatment with strains (114/64) Candida albidus and (117/10) Pichia anomala.
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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".