Isolation, identification, and host range of <i>Aspergillus welwitschiae</i> causing postharvest rot on Chinese cabbage in China
Bibliographic record
Abstract
A new kind of postharvest rot on Chinese cabbage (Brassica rapa L. ssp. pekinensis) caused by a fungus was observed in vegetable cellars in Harbin, China in 2019 and 2020, causing considerable economic losses. A water-soaked spot appeared at the base of the midrib of leaves, extended into the upper part of the midrib, and ultimately turned into rot. The diseased part was dark brown or black with a few black spores on the surface. Fungal isolates were obtained from the diseased plants and identified as Aspergillus welwitschiae through morphological observation and multigene sequencing analysis of the internal transcribed spacer, β-tubulin and calmodulin genetic regions. Pathogenicity tests were conducted, and the resulting symptoms on Chinese cabbage were similar to those seen in the vegetable cellar. The isolates were also associated with rot and leaf spot on seedlings and affected the seed germination of Chinese cabbage. A host range test showed that the isolates could infect some common vegetables, including carrot, kidney bean, broccoli, radish, Chinese flowering cabbage, root-mustard, cabbage, non-heading Chinese cabbage, pepper, lettuce, oilseed rape, tomato and cucumber. The isolation, identification, and host range of the pathogen can provide a basis for the study of the occurrence, prevention and management of the disease in the future.
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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 | 0.001 | 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".