Genetic diversity and population structure of<i>Vigna exilis</i>and<i>Vigna grandiflora</i>(Phaseoleae, Fabaceae) from Thailand based on microsatellite variation
Bibliographic record
Abstract
The objective of this research was to determine the genetic diversity and population structure of natural populations of two rare wild species of Asian Vigna (Phaseoleae, Fabaceae), Vigna exilis Tateishi & Maxted and Vigna grandiflora (Prain) Tateishi & Maxted, from Thailand. Employing 21 simple sequence repeat markers, 107 and 85 individuals from seven and five natural populations of V. exilis and V. grandiflora, respectively, were analyzed. In total, the markers detected 196 alleles for V. exilis and 219 alleles for V. grandiflora. Vigna exilis populations showed lower average values in number of alleles, allelic richness, observed heterozygosity, gene diversity, and outcrossing rate than V. grandiflora populations, namely 58.00% versus 114.60%, 51.96% versus 74.80%, 0.02% versus 0.18%, 0.40% versus 0.66%, and 3.24% versus 17.41%, respectively. Pairwise FSTamong populations demonstrated that V. exilis was much more differentiated than V. grandiflora. Analysis of molecular variance revealed that 41.83% and 15.06% of total variation resided among the populations of V. exilis and V. grandiflora, respectively. Seven and two genetic clusters were detected for V. grandiflora and V. exilis by STRUCTURE analysis. Our findings suggest that different strategies are required for in situ conservation of the two species. All V. exilis populations, or as many as possible, should be conserved to protect genetic resources of this species, while a few V. grandiflora populations can capture the majority of its genetic variation.
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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".