Genetic diversity of the rice bean (<i>Vigna umbellata</i>) genepool as assessed by SSR markers
Bibliographic record
Abstract
The genetic diversity of 472 rice bean accessions (388 cultivated and 84 wild) from 16 Asian countries was evaluated by 13 simple sequence repeat (SSR) markers. In total, 168 alleles were detected, and the numbers of alleles in cultivated and wild accessions were 129 and 132, respectively. The gene diversity in cultivated populations (0.565) was about 83% of that for wild (0.678) populations. Cultivated populations from Vietnam, Myanmar, Nepal, and India had the highest gene diversity (>0.5). East Asian accessions formed a distinct genepool. Indonesian cultivated accessions showed high genetic divergence from other cultivated populations and had the most similar genetic structure to wild accessions. In Nepalese cultivated accessions, many accessions from western regions were quite distinct from others and formed a specific group. These Nepalese accessions could be considered a unique gene source for rice bean breeding. In contrast, eastern Nepalese accessions showed an SSR profile similar to that of Southeast Asian rice beans. The present study represents the first comprehensive SSR analysis in cultivated and wild rice bean germplasm and clarifies geographical distribution of genetic profile that might be used to broaden the genetic base of currently grown rice bean cultivars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".