Kernel Quality Association and Path Analysis in Bread Wheat
Bibliographic record
Abstract
The correlation and path coefficient analysis of some kernel quality traits have been studied for 92 cultivars, breeding lines and landrace varieties of bread wheat (Triticum aestivum L.). Ninety-two genotypes were evaluated in alpha lattice design with two replications. Result of analysis of variance indicated that there were significant differences among genotypes in the most of traits. The correlation analysis showed that there were significant positive correlations among thousand kernel weight (TKW), grain length (GL) and grain width (GW). We also showed TKW, GL and GW had positive correlation with grain protein percentage, gluten weight, and falling number. Grain protein was significantly correlated with several kernel characteristics including: TKW, GL, GW, hardness index, gluten weight, SDS sedimentation, and falling number. On the first and second steps of stepwise regression analysis, protein percentage and falling number were the most effective traits in explaining different trait variations. Path coefficient analysis also showed the direct and significant effects of grain protein percentage and medium direct effect of falling number on SDS sedimentation. This result can be used in wheat breeding programs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".