Genetic and biochemical variability among <i>Moringa oleifera</i> Lam. accessions collected from different agro-ecological zones
Bibliographic record
Abstract
Genomic DNA polymorphism and variation in biologically active components of Moringa oleifera were investigated by two different techniques: RAPD-PCR and HPLC analysis. The concentrations of phenolic compounds (cinnamic, caffeic, ferulic, and coumaric acids) and the content of flavonoids (rutin) were quantified by HPLC analysis. Among 20 RAPD primers, 13 were selected to generate polymorphic amplicons producing an average of 5028 bands, of which 83.7% were found to be polymorphic among 57 accessions of M. oleifera (MO 1 to MO 57) and one outgroup (ACB 58) from Banasthali region, India. In total, 57 accessions were clustered into five major groups within the dendrogram. The results of this analysis were further confirmed by principal coordinate analysis (PCoA). There was also high diversity in the concentration of active compounds in the collected samples as revealed by HPLC analysis. The data revealed that the content of polyphenolic compounds varied between 0.06 (sample KVKB) and 210.5 mg/kg (sample BG). The results suggest that there is a strong correlation between phytochemical variables and DNA polymorphism. The study concludes that the results of the genetic, morphological, and phytochemical diversity could be used to select the best accessions of M. oleifera for agricultural cultivation and breeding.
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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 | 0.001 | 0.001 |
| 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".