Inheritance pattern of earliness and yield traits in half diallel crosses of spring wheat
Bibliographic record
Abstract
A half-diallel mating system was used to evaluate six wheat cultivars and their F1 and F2 populations for inheritance of earliness and morphological and yield traits. These genotypes were crossed in a half-diallel fashion during 2010–2011 to get 15 cross combinations. The 6 × 6 wheat F1 and F2 half-diallel populations and their parental cultivars were assessed in a randomized complete block design during 2011–2012 and 2012–2013, respectively. Genotypes revealed significant (P ≤ 0.01) differences for all the traits in both generations. According to scaling tests, an additive–dominance model was partially adequate for all the traits in the F1 and F2 generations. Diallel analysis revealed significant values for additive (D) and dominance (H1 and H2) genetic components of variance for majority traits in both generations, however, the overdominance type of gene action was predominant for inheritance. Additive gene action was observed for days to heading and plant height in the F1 generation and tiller per plant and grain yield per plant in the F2 generation. In the loci (H2 < H1), the majority of the traits showed an unequal proportion of positive and negative genes with asymmetrical distribution among parental genotypes (H2/4H1 < 0.25). Significance of both additive and nonadditive genetic variations suggested integrated breeding strategies with delayed selection for improvement in wheat populations.
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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".