Genetic diversity in bambara groundnut (<i>Vigna subterranea</i> (L.) Verdc) landraces revealed by AFLP markers
Bibliographic record
Abstract
Bambara groundnut (Vigna subterranea (L.) Verdc), an African indigenous legume, is popular in most parts of Africa. The present study was undertaken to establish genetic relationships among 16 cultivated bambara groundnut landraces using fluorescence-based amplified fragment length polymorphism (AFLP) markers. Seven selective primer combinations generated 504 amplification products, ranging from 50 to 400 bp. Several landrace-specific products were identified that could be effectively used to produce landrace-specific markers for identification purposes. On average, each primer combination generated 72 amplified products that were detectable by an ABI Prism 310 DNA sequencer. The polymorphisms obtained ranged from 68.0 to 98.0%, with an average of 84.0%. The primer pairs M-ACA + P-GCC and M-ACA + P-GGA produced more polymorphic fragments than any other primer pairs and were better at differentiating landraces. The dendrogram generated by the UPGMA (unweighted pair-group method with arithmetic averaging) grouped 16 landraces into 3 clusters, mainly according to their place of collection or geographic origin. DipC1995 and Malawi5 were the most genetically related landraces. AFLP analysis provided sufficient polymorphism to determine the amount of genetic diversity and to establish genetic relationships in bambara groundnut landraces. The results will help in the formulation of marker-assisted breeding in bambara groundnut.
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 | 0.000 |
| 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".