Genetic diversity of the Bambara groundnut (<i>Vigna subterranea</i>(L.) Verdc.) as assessed by SSR markers
Bibliographic record
Abstract
Bambara groundnut ( Vigna subterranea (L.) Verdc.) is an important African legume crop. In this study, a collection consisting of 240 accessions was analyzed using 22 simple sequence repeat (SSR) markers. In total, 166 alleles were detected, with a mean of 7.59 alleles per locus. Allelic and gene diversities were higher in the west African and Cameroon/Nigeria regions with 6.68 and 6.18 alleles per locus, and 0.601 and 0.571, respectively. The genetic distance showed high similarity between west African and Cameroon/Nigeria accessions. Principal coordinate analyses and neighbor-joining analysis consistently revealed that the majority of west African accessions were grouped with Cameroon/Nigeria accessions, but they were differentiated from east African, central African, and southeast Asian accessions. Population structure analysis showed that two subpopulations existed, and most of the east African accessions were restricted to one subpopulation with some Cameroon/Nigeria accessions, whereas most of the west African accessions were associated with most of the Cameroon/Nigeria accessions in the other subpopulation. Comparison with SSR analysis of other Vigna cultigens, i.e., cultivated azuki bean ( Vigna angularis ) and mungbean ( Vigna radiata ), reveals that the mean gene diversity of Bambara groundnut was lower than azuki bean but higher than mungbean.
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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".