Genetic diversity and phylogenetic relationships in <i>Vigna</i> Savi germplasm revealed by DNA amplification fingerprinting
Bibliographic record
Abstract
The pantropical genus Vigna (Leguminosae) comprises 7 cultivated species that are adapted to a wide range of extreme agroclimatic conditions. Few data are available on the relationships among these cultivated species or on their importance as sources of resistance against biotic and abiotic stresses. Therefore, we optimized DNA amplification fingerprinting (DAF) to estimate the genetic diversity within, and genetic relationships among, a representative core collection of cowpea, as compared with 16 accessions representing cultivars from 6 Vigna species. A set of 26 primers was selected from 262 tested random primers and used for the characterization of 85 Vigna accessions (6 V. angularis, 4 each of V. mungo and V. radiata, 2 V. umbellata, 1 V. aconitifolia, and 68 V. unguiculata), with Phaseolus vulgaris subsp. vulgaris as outgroup. A total of 212 polymorphic bands were used for maximum parsimony analysis. Our results clearly distinguished Brazilian from African V. unguiculata genotypes. At the species level, V. angularis was the most related and V. radiata the most divergent species relative to V. unguiculata. DAF markers were also informative at the intraspecific level, detecting a large diversity between cowpea cultivars. The implications of the presented results for cowpea breeding programs are discussed.
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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".