Effects of Moringa-based phytostim biostimulant on the growth, nutritional composition, and antioxidant bioactivity of two amaranth species
Bibliographic record
Abstract
The current study aims to evaluate the effect of pre-harvest application of different concentrations of phytostim biostimulant on growth, yield, nutrition, phenolic acids and some phenolic compound in two species of Amaranthus . A two factor greenhouse trial was established in a split-plot to accommodate eight phytostim biostimulant concentrations (0, 0.5, 1, 1.5, 2.5, 3, 4.5, and 6%) in Amaranthus cruentus and Amaranthus caudatus for up to 60 days post-transplant. Application of 1.5% phytostim biostimulant resulted in the highest growth and biomass accumulation, whilst concentrations of >3% resulted in reduced growth and biomass yield in both studied species. These results were in concomitant to an enhanced accumulation of total phenols, total flavonoids, β-carotene, and antioxidants activity (DPPH and ABTS) in Amaranthus cruentus . Application of 1% in Amaranthus cruentus had a positive effect of the accumulation of zinc, iron and calcium and some amino acids (alanine, asparagine, lysine and arginine). In Amaranthus caudatus , 0.5% concentration resulted in the highest total phenols, total flavonoids, antioxidant activity (DPPH and ABTS), chlorophyll b and total chlorophyll. Amaranthus cruentus 0.5% resulted in the highest 2-O-caffeoylglucaric acid, whilst application of 1.0% in Amaranthus caudatus showed the highest caffeic acid, 3-O-glucuronide thus suggesting. Thus, suggesting that phytostim biostimulant can be used to manipulate growth and nutritional components in the studied Amaranthus species. Lower concentrations (0.5–1.5%) of phytostim biostimulant can be recommended for cultivation of A. Cr. A. cruentus and Amaranthus caudatus .
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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) | 0.000 | 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) | 0.001 | 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".