Humic Substances and Efficient Microorganisms: Elicitation of Medicinal Plants—A Review
Bibliographic record
Abstract
In function of the green revolution the indiscriminate use of agrochemicals and pesticides in agriculture has been also shown in the production of medicinal plants, resulting in the increase of productivity but with high residual contamination and low rates in the production of secondary metabolites responsible for the biological and pharmacological activity in vegetable drugs. In another hand, new techniques of elicitation has been applied to stimulate the medicinal plants production through the organic and agroecological management, contributing for the increase of performance, quality and production. In this context, it is aimed with this review to present such as the humic substances: fulvic acid, humic acid and efficient microorganisms which influence and help the ontogeny and the secondary metabolites production of medicinal plants. The reviewed articles show that the use of fulvic acid, humic acid and efficient microorganisms in the production of medicinal plants contributes on the increase of biosynthesis, secondary metabolites production such as coumarins, alkaloids, phenylpropanoids and essential oils, as well as the increase of nutrients absorption, growth and development of 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".