The Accumulation of Phenolic Compounds in Genetically Selected Amaranthus hybridus is Influenced by Endophytic Natural Growth Regulator
Bibliographic record
Abstract
Amaranth ( Amaranthus spp . L.) ( Amaranthaceae ), an endemic plant in Central and South America, grows worldwide, being cultivated in many temperate and tropical countries. Although several species of amaranth are frequently considered weeds, the plant is recognized as a food, constructive medicine, a source of protein and minerals livestock feed. The plant is widely cultivated, promoted, and increasingly consumed as a leafy vegetable and traditional medicine in Africa. Despite progressive genetic improvement and modern plant growing technologies, unfavorable climatic and ecological factors reduce the yield, and quality of the active plant botanicals. The role of bio-transformed endophytic microbial plant growth regulator formulation (BESF) on yield and accumulation of phenolic compounds in amaranth leaves is poorly understood. The current study assessed the effects of pre-sowing seed treatments with 0.0 %, 0.2 % and 0.4 % concentrations of BESF solution on germination, leaf yield, flavor and phenolic content in genetically selected Amaranthus hybridus var . cruentus . Data collected were subjected to analysis of variance (ANOVA). Significant treatment means were separated using Tukey test at p < 0.05. BESF significantly increased fresh marketable leaf yield by over 360 kg/ha (29 %) compared to the control. The total flavonoid content in the leaves was raised by 34 % and 47 % with 0.2 % and 0.4 % BESF solution treatments respectively, compared to control. Maximum concentration levels of rutin, apigenin, apigetrin, and quercetin was obtained with 0.4% BESF solution treatment. An analysis of the collected data suggest that BESF was effective in overall improvement in leaf yield, chemical content, and flavor of A. hybridus var . cruentus, allowing us to recommend BESF application to raise A. hybridus var . cruentus leaves for nutrition and pharmacological applications.
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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 | 0.000 | 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 | 0.001 | 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".