Study on the Application Effect of Soil Improvement Techniques in Off-Season Cultivation of Leonurus japonicus
Bibliographic record
Abstract
Leonurus japonicus is a common Chinese herbal medicine with high medicinal value. However, when it is planted off-season in winter and spring, it often encounters some problems, such as too cold weather, hard soil, easy loss of nutrients, and fewer microorganisms. These problems will affect its growth and efficacy. In order to make L. japonicus grow well in these seasons, we have consulted the methods of improving soil at home and abroad in recent years. There are mainly several ways: such as using machinery to loosen the soil, adding organic fertilizers, using beneficial bacteria, applying biochar, and adjusting the pH of the soil. By comparing cases, field experiments, and literature in different regions, we have summarized which methods are most effective under various soil problems. We have also compiled a more practical technical combination table for the reference of growers. The study found that as long as the appropriate method is selected according to the actual situation of the soil, the soil structure and root environment can be significantly improved, thereby increasing the emergence rate, yield, and accumulation of medicinal ingredients of L. japonicus . We also suggest that the government and agricultural departments increase the promotion of technology and improve the service system to promote the wider application of these technologies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".