The Potential of Agro-homeopathy Applied to Medicinal Plants—A Review
Bibliographic record
Abstract
The homeopathic preparations can influence the growth, secondary metabolites production, essential oil yield and phytochemical profile when applied in the grown of medicinal plants. To compile this review articles from existing literature about basic research related to the use of homeopathic preparation on the cultivation of medicinal plants and its influence on the phytochemical profile, growth, yield and composition of essential oil were collected. The bibliographic research was carried out in scientific databases sites—Scopus, Web of Science and PubMed. Seventeen publications were found in which homeopathy was applied in the cultivation of medicinal plants. Its use changed the phytochemical profile, increased the essential oil yield, the production of secondary metabolites (coumarins, alkaloids, phenylpropanoids), the the nutrients absorption and the growth of the medicinal plant species were studied. This review shows that the application of homeopathic preparations in the cultivation of medicinal plants increases the production of secondary metabolites and essential oils that are important for human and animal health therapeutic treatments.The homeopathic preparation application is an alternative for the growth of medicinal plants with ecological balance, and without soil and water contamination. It is also affordable to farmers and researchers. However, further studies are required on its influence on the phytochemical profile of the cultivated medicinal species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".