Evaluation of preharvest sprouting traits in a collection of spring wheat germplasm using genotype and genotype × environment interaction model
Bibliographic record
Abstract
With 10 tables Abstract Preharvest sprouting (PHS) can greatly affect the consistent production of high‐quality spring wheat ( Triticum aestivum L.) in many regions worldwide including Western Canada. A worldwide collection of red‐ and white‐seeded spring wheat genotypes with different levels of sprouting response were characterized for three PHS traits: falling number, germination index and sprouting index in three different environments in Manitoba, Canada. The data sets were analysed by the genotype and genotype × environment interaction model. Variance components were estimated, genotypic and their interaction effects with environments were predicted by using one of mixed linear model approaches: minimum norm quadratic unbiased estimation approach. Genotypic variance expressed as proportion to the phenotypic variance was higher compared to genotype × environment (G × E) interaction effects for all three PHS traits, suggesting that these genotypes can be used to develop high level of PHS‐resistant cultivars regardless of environment. Strong correlations between PHS traits across environments suggest that all three traits are repeatable and reliable methods to determine PHS response in spring wheat depending on sample types (spike, grain or flour). Predicted genotypic effects, G × E interaction effects and the linear discriminant analysis revealed that white‐seeded genotypes ‘AUS1408’, ‘SC8019‐R1’ and Kanata, and red‐seeded genotypes ‘AC Domain’, ‘AC Majestic’ and ‘Red RL4137’ would be useful PHS‐resistant donors in spring wheat cultivar development programmes.
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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 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".