Molecular markers for seed colour in<i>Brassica juncea</i>
Bibliographic record
Abstract
A detailed RFLP map was used to map QTLs associated with seed colour in Brassica juncea using a doubled-haploid population derived from a cross between a black/brown-seeded cultivar and a yellow-seeded breeding line. Segregation analysis suggested that seed colour was under control of 2 unlinked loci with duplicate gene action. However, QTL analysis revealed 3 QTLs, SC-B4, SC-A10 and SC-A6, affecting seed colour. The QTLs were consistent across environments, and individually explained 43%, 31%, and 16%, respectively, and collectively 62% of the phenotypic variation in the population. Digenic interaction analysis showed that closest flanking locus of QTL SC-B4, wg7b6cNM, had strong epistasis with the locus wg5a1a, which is tightly linked to QTL SC-A6. The interaction of these 2 loci explained 27% of the phenotypic variation in the population, while the whole model explained 84%. In a multiple regression model, the effects of QTL SC-A10, as well as its interaction with other loci, were non-significant, whereas the effects of loci wg7b6cNM and wg5a1a and their interaction were significant. Ninety-eight percent of the DH lines carried the expected alleles of loci wg7b6cNM and wg5a1a for seed colour, confirming that only these 2 loci were linked to seed colour in B. juncea. Four additional digenic interactions significantly affected seed colour, and all 5 digenic interactions were consistent across environments.
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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 | 0.000 |
| 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) | 0.001 | 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".