Screening of Disease and Pest Resistant Hangbaiju (<i>Chrysanthemum morifolium</i>) Varieties and Their Application Prospects in Green Cultivation
Bibliographic record
Abstract
Hangbaiju (Chrysanthemum morifolium) is popular among consumers because of its anti-inflammatory, antioxidant and liver-protecting effects, and it has both medicinal and ornamental value.With the expansion of planting scale, pests and diseases have become the main obstacles affecting yield and quality.Although chemical pesticides are still the main means of prevention and control, there are problems such as increased resistance and environmental pollution.In order to promote green planting, this study screened out a number of resistant chrysanthemum varieties, and combined field natural infection experimental analysis, artificial inoculation experiments and molecular marker analysis to clarify the role of resistance genes, like CmWD40 and CmWRKY8-1 in the defense mechanism.The results showed that, these varieties can effectively reduce the incidence of pests and diseases while maintaining yield and quality, and adapt to the green prevention and control system.This study provides strong germplasm support and theoretical basis for the sustainable cultivation of Hangbaiju, and helps its promotion and application in ecological agriculture and organic certification paths.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".