Construction of a genetic linkage map and QTL analysis in bambara groundnut
Bibliographic record
Abstract
Bambara groundnut (Vigna subterranea (L.) Verdc.) is an indigenous underutilized legume that has the potential to improve food security in semi-arid Africa. So far, there are a lack of reports of controlled breeding populations that could be used for variety development and genetic studies. We report here the construction of the first genetic linkage map of bambara groundnut using a F3 population derived from a "narrow" cross between two domesticated landraces (Tiga Nicuru and DipC) with marked divergence in phenotypic traits. The map consists of 238 DArT array and SSR based markers in 21 linkage groups with a total genetic distance of 608.3 cM. In addition, phenotypic traits were evaluated for a quantitative trait loci (QTL) analysis over two generations. A total of 36 significant QTLs were detected for 19 traits. The phenotypic effect explained by a single QTL ranged from 11.6% to 49.9%. Two stable QTLs were mapped for internode length and growth habit. The identified QTLs could be useful for marker-assisted selection in bambara groundnut breeding programmes.
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".