Inheritance of test weight and kernel weight in eight durum wheat crosses
Bibliographic record
Abstract
High test weight and uniform kernel size are important grading factors for durum wheat [Triticum turgidum L. ssp. durum (Desf.) Husn.] because both are associated with semolina yield. The objective of this research was to determine the inheritance and heritability of test and kernel weights to facilitate development of selection strategies. Eight durum populations were grown in replicated, multi-location, multi-year field trials. Test weight and kernel weights were determined on all plots after harvest. Both traits were affected by genotype and to a lesser extent by year or location. Genotype environmental interactions were generally minor. Trial means for test weight ranged from 72.7 to 81.0 kg hL-1 and from 31.5 to 50.9 mg for kernel weight. All populations showed bi-directional transgressive segregation for both traits, and the estimated number of effective factors controlling them ranged from 4 to 23, indicating quantitative inheritance. With the exception of one population, heritability of test weight ranged from 0.80 to 0.92 and of kernel weight from 0.83 to 0.93. Both traits generally showed positive phenotypic and genotypic correlations with plant height. Also, mean test weight and kernel weight were higher for the gibberellic acid-sensitive (tall) than for the insensitive (semidwarf) group within populations segregating for gibberellic acid response.Key words: Test weight, kernel weight, inheritance, heritability
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".