Protein concentration inheritance and selection in durum wheat
Bibliographic record
Abstract
Grain protein concentration is important in the determination of the value of durum wheat (Triticum turgidum L. var. durum) for pasta manufacture. This study was undertaken to investigate the heritability and inheritance of protein concentration in seven genetically diverse durum populations, and to determine if the precision of this information could be improved by adjustment for micro-environmental trends. Grain protein and grain yield were measured at multiple locations and years. The Papadakis method was used to adjust for environmental trends in these replicated trials, and the moving mean was used for confimation in a sample of 19 un-replicated breeding trials. Environmental trends were substantial, and trend adjustment improved both correlations among locations and precision. Consequently, trend adjustment may be useful for genetic studies to improve trial precision, but would be of questionable merit in early-generation breeding trials due to the cost of additional protein measurements and marginal improvement in selection response. Grain yield was negatively correlated with grain protein concentration in all trials. Protein concentration was moderately heritable and complexly inherited in these populations, with the number of estimated effective factors ranging from 5 to 17 for the majority of trials. The complexity of inheritance and interactions of protein with yield and environment makes early-generation selection for protein difficult.Key words: Grain protein concentration, heritability, inheritance, semi-dwarf
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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) | 0.000 | 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) | 0.000 | 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 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".