The Application and Effect Evaluation of Eco-Friendly Soil Improvement Techniques in the Cultivation of <i>Chrysanthemum morifolium</i> (Ramat.) Hemsl.
Bibliographic record
Abstract
We have sorted out the research in recent years and found that many environmentally friendly methods, such as using organic materials to mix soil, adding biological fertilizers, adding microorganisms, planting with other crops, and scientific fertilization methods, can make chrysanthemum grow better, have higher yields, and have more beautiful flowers. Moreover, these methods can also improve the soil environment, such as making the nutrients in the soil more sufficient and the types of microorganisms more diverse. Some studies used coconut bran, earthworm manure, and leaf mold (mixed in a ratio of 2:1:1) as the substrate, and found that chrysanthemum grew stronger and the flowers were more beautiful. In addition, using biological fertilizers or microbial strains can improve the fertility of the soil and make the beneficial microorganisms in the soil more active. Planting chrysanthemum with corn not only increases the yield of chrysanthemum and the effective ingredients, but also helps increase the number of good microorganisms and improve the ecological environment of the soil. There is an organic liquid fertilizer called Jeevamrit, which, combined with scientific nutrient management, can also make the nutrients in the soil richer and the number of microorganisms more abundant. Some studies have also found that after removing viruses, the roots will grow better and the soil quality will improve. These eco-friendly soil improvement techniques can not only improve the yield and quality of chrysanthemums, but also improve the soil environment. For those who want to grow flowers sustainably, these practices are quite valuable for reference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".