Chromosomal Localization of QTLs Controlling Genotype X Environment Interaction in Wheat Substitution Lines Using Nonparametric Methods
Bibliographic record
Abstract
To locate the genes controlling grain yield stability, substitution line of Cheyenne (as donor) into the genetic background of Chinese Spring (as recipient) and their parents were used in a randomized complete block design with three replications under two different conditions (rain-fed and irrigated) for two years. Interrelationship among nonparametric measures showed that non-parametric statistics Si(1), Si(2), Si(3), Si(6), NPi(1), NPi(2), NPi(3), NPi(4), ?r and ?gy were significantly (P<0.01) correlated and exhibited negative and significant (P<0.01) correlation with grain yield, while the statistics RS, TOP and Kr revealed positive and significant correlation with grain yield. The results of spearman’s rank correlation were confirmed by Ward’s hierarchical cluster analysis. Principal components analysis indicated that the two first components explained 92.68% (77.71 and 14.97% by components 1 and 2, respectively) of the total variance. Screening nonparametric estimates using biplot technique based on two first components classified the stability measures in 2 groups. Nonparametric statistical procedures and ranking method indicated that most of the quantitative trait loci (QTLs) involved in controlling phenotypic stability in wheat are located on the chromosomes 2A, 3A and 4A in A genome and 3D and 5D in D genome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".