Quantitative trait loci analysis of economically important traits in<i>Sorghum bicolor</i>×<i>S. sudanense</i>hybrid
Bibliographic record
Abstract
Lu, X-p., Yun, J-f., Gao, C-p. and Acharya, S. 2011. Quantitative trait loci analysis of economically important traits in Sorghum bicolor×S. sudanense hybrid. Can. J. Plant Sci. 91: 81–90. Many agronomic traits of Sorghum bicolor×S. sudanense hybrid are quantitatively inherited, and the gene mapping of these traits has important research and practical consequences. In this study, genetic mapping and quantitative trait loci (QTL) analyses were conducted using 248 F2:3plants of a cross between sorghum 314A (female parent) and Sudan grass 2002GZ-1 (male parent). A total of 178 markers (170 amplified fragment length polymorphism and 8 random amplified polymorphic DNA) were employed to construct a linkage map with 10 linkage groups covering 836 cM of the genome. The two parents expressed polymorphism for 10 agronomic characters (plant height, stem diameter, leaf number, leaf length, leaf width, spike length, tiller number, ratio of stem and leaf weight, fresh plant weight and dry plant weight). When analyzed for possible QTLs a total of 98 QTLs were identified in two test sites, out of which 26 QTLs overlapped in both sites. The average number of QTLs per character was found to be 2.6 and the distributions of these QTLs were found to be uneven across linkage groups. This, and the fact that molecular marker densities were not proportional with QTL frequencies, indicates that the detectable QTLs correlated with the agronomic traits and the genetic map can be useful for improvement in relevant characters in Sorghum bicolor×S. sudanense hybrids.
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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".