Evaluation of Antioxidant Activity and Some Secondary Metabolites of Purple Coneflower (Echinacea purpurea L.) in Response to Biological and Chemical Fertilizers
Bibliographic record
Abstract
Purple coneflower (Echinacea pupurea L.) is an ornamental-medicinal plant belonging to Asteraceae family. It has long been used as an herbal medicine. In order to study the effects of biological and chemical fertilizers on quantitative and qualitative yields of purple coneflower, an experiment was conducted at Department of Horticultural Science and Landscape, University of Tehran. The trial was arranged based on a randomized complete block design, with eight treatments and three replications. Treatments were included control (no fertilizers), nitrogen-fixing, bacteria: Azospirillum lipoferum (AL), Azotobacter chrococum (AC), phosphorus solubilizing bacterium Pseudomonas fluorecens, (PF), Glomus intrradices inoculum (GI), the mixture of the three bacteria and the mixture of the three bacteria plus the mycorrhizal inoculum. According to the results of HPLC, Catechin content was high at herbage in both years. In addition, the best results have been taken from the AL and AC with the control. Caffeic acid, chlorogenic acid and Epicatechin contents were higher during the first year of the herbage, and control, AC and AL treatments gave high value. Ferulic acid was high in the herbage during the first year and generally high values were obtained from PF and GI treatments. Quercetin content was high in plant root during the first year with AC application.
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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 | 0.000 | 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) | 0.001 | 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".