Comparison of Population Genetic Structures between Asian and American Mungbean Accessions Using SSR Markers
Bibliographic record
Abstract
The purpose of this study was to evaluate the genetic diversity and population structure of 65 mungbean accessions collected from East and Southeast Asia, the United States and Guatemala using 15 simple sequence repeat (SSR) markers. In total, 47 alleles were detected, the number of the alleles per locus range from two to six, with an average of 3.13. The mean major allele frequency (MAF), expected heterozygosity (HE), and polymorphic information content (PIC) of the 15 SSR loci were 0.76, 0.05, and 0.28, respectively. Of the 47 alleles, 17 (36.2%) were common, with a frequency of 0.05– 0.5; 16 (34.0%) were rare (frequency < 0.05) and 14 (29.8%) were abundant (frequency > 0.5). On the basis of the UPGMA dendrogram, most of the accessions were clustered into two main groups. The first group (Group I) included seven accessions and the second comprised 58 accessions, which were further divided into four subgroups. Four subpopulations were detected by model-based structure analysis. Fifty-five accessions (84.6%) showed a clear relation to each cluster based on their inferred ancestry value (>75%), while the remaining 10 accessions (15.4%) were categorized as admixtures. Mungbean accessions from US distributed to almost all clusters and 2 accessions shared genetic constituents showing it derived from mixed ancestry with Asean accessions. These results could be useful in identifying mungbean germplasms and facilitating their improvement programs.
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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) | 0.000 | 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 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".