MOLECULAR CHARACTERIZATION AND GENETIC DIVERSITY STUDIE OF SOYBEAN (GLYCINE MAX L.) CULTIVARS USING RAPD MARKERS
Bibliographic record
Abstract
Soybean (Glycine max L.) is an important crop plant which contains a high amount of oil and protein. The main goal of the work was to use 13 RAPD (Random Amplified Polymorphic DNA) markers to study genetic polymorphism in a set of 28 soybean genotypes and to construct a dendrogram from the obtained results, based on which we will recommend genotypes for further breeding. In total, using 13 RAPD markers, we determined 108 fragments in a set of 28 soybean genotypes with an average number of 8.31 fragments per genotype. The number of fragments varied from 6 (OPB-08, OPE-07) to 12 (SIGMA-D-01). Of the total number of 108 fragments, 70 were polymorphic with an average number of 5.38 polymorphic fragments per genotype. The number of polymorphic fragments varied from 3 (OPE-07, OPF-14) to 9 (SIGMA-D-01). The average percentage of fragment polymorphism was 63.68% and ranged from 42.86% (OPF-14) to 83.33% (OPB-08). Diversity index (DI) values ranged from 0.710 (OPA-03) to 0.846 (OPD-08) with an average value of 0.763. A dendrogram prepared based on hierarchical cluster analysis using UPGMA algorithm separated 28 soybean genotypes into four clusters. The applied RAPD markers proved to be a suitable tool for detection of the genetic diversity of soybean genotypes. Based on the constructed dendrogram, it is possible to select suitable varieties for marker-assisted breeding.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".