Screening of Disease-Resistant Germplasm and Its Application in Off-Season Cultivation of <i>Leonurus japonicus var. hunanensis</i>
Bibliographic record
Abstract
This study focused on screening and using the disease resistant germplasm resources of Leonurus japonicus var. hunanensis , and analyzed its main diseases in detail. Through field observation, common problems such as powdery mildew, downy mildew and root rot were identified, and their incidence and damage degree under off-season cultivation conditions were evaluated. In the process of off-season planting, due to the changes of temperature, humidity and management conditions, diseases are prone to outbreak, especially in warm and humid environment. The research team conducted field surveys in many planting areas and screened out a batch of disease resistant materials with good comprehensive properties. Among the selected materials with high resistance to powdery mildew, some still maintain a low incidence in high humidity environment, reducing the dependence on chemicals, reducing production costs and improving economic benefits. According to the performance of disease resistant materials, the cultivation mode and management methods were further adjusted. The planting density, water supply and fertilizer use were improved, and the planting environment was properly regulated. The use of disease resistant germplasm combined with optimized cultivation techniques can significantly reduce the occurrence of disease and improve the yield and the level of active ingredients of Leonurus japonicus var. hunanensis in off-season production. This not only improves the economic benefits, but also reduces the use of pesticides, helps to achieve the goal of green planting, and provides a reliable basis for the sustainable development of the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".