Morphological and molecular characterization of <i>Neoerysiphe galeopsidis</i> causing powdery mildew on <i>Lagopsis supina</i> in central China
Bibliographic record
Abstract
Lagopsis supina (Xia Zhi Cao) is a widely distributed and cultivated herbal plant that has been extensively used as a traditional Chinese medicine, Tibetan medicine and Mongolian medicine for disease treatments for centuries. While the bioactive compounds of L. supina are extensively studied, the fungal pathogens of this plant are still obscure. In 2021, powdery mildew signs and symptoms were observed on leaves of L. supina in Xinxiang City, Henan Province, China. However, the causal pathogen had not been identified. Therefore, morphological characteristics and molecular analysis was applied to identify the powdery mildew pathogen. The sequence of the internal transcribed spacer (ITS) region of the fungus was obtained and showed 99.83% identity with the previously reported Neoerysiphe galeopsidis. The virulence of this phytopathogen on L. supina was confirmed by utilizing Koch’s postulates. Therefore, we identified the causal pathogen as N. galeopsidis. Based on biological and molecular characterization, this is the first comprehensive report of powdery mildew caused by N. galeopsidis on L. supina in central China. The occurrence of powdery mildew disease on L. supina may detract from the plant’s medical and ornamental value. The identification and confirmation of N. galeopsidis on L. supina expands the knowledge of this causal agent and will support efforts towards future control and management of powdery mildews.
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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 | 0.001 | 0.001 |
| 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 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".