Construction of a genetic linkage map of black gram,<i>Vigna mungo</i>(L.) Hepper, based on molecular markers and comparative studies
Bibliographic record
Abstract
A genetic linkage map of black gram, Vigna mungo (L.) Hepper, was constructed with 428 molecular markers using an F9 recombinant inbred population of 104 individuals. The population was derived from an inter-subspecific cross between a black gram cultivar, TU94-2, and a wild genotype, V. mungo var. silvestris. The linkage analysis at a LOD score of 5.0 distributed all 428 markers (254 AFLP, 47 SSR, 86 RAPD, and 41 ISSR) into 11 linkage groups. The map spanned a total distance of 865.1 cM with an average marker density of 2 cM. The largest linkage group spanned 115 cM and the smallest linkage group was of 44.9 cM. The number of markers per linkage group ranged from 11 to 86 and the average distance between markers varied from 1.1 to 5.6 cM. Comparison of the map with other published azuki bean and black gram maps showed high colinearity of markers, with some inversions. The current map is the most saturated map for black gram to date and will provide a useful tool for identification of QTLs and for marker-assisted selection of agronomically important characters in black gram.
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 | 0.001 |
| 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".