Effects of Genotype and Environment on the Starch Properties and End‐Product Quality of Oats
Bibliographic record
Abstract
ABSTRACT Five Canadian oat genotypes were grown at six environments in Manitoba to assess the effects of genotype, environment, and genotype‐by‐environment interaction on oat starch properties and end‐product quality. Genotypic variation was significant for total starch, amylose content, starch swelling volume (SSV), Rapid Visco Analyser (RVA) pasting viscosities, differential scanning calorimetry (DSC) thermal properties, and starch gel texture as well as the quality of flakes and cooked oatmeal made by laboratory‐scale methodologies. Environment was the dominant factor contributing to the total variation of starch content, RVA pasting viscosities, SSV, and DSC thermal properties. Most measurements of starch gel and oatmeal texture were not affected by growing environment. Cross‐over analysis revealed that changes in the ranking of genotypes across environments occurred for starch RVA hot paste, breakdown and shear thinning viscosities, work of gel compression, flake hydration capacity, and the proportion of large flakes, indicating that breeding for these traits would require multiple testing sites. Trends were observed between oatmeal texture and several flake and starch gel properties, warranting further study. Results of this study indicated that there is a potential to breed Canadian oat cultivars with improved functional end‐product quality for use in the milling and food manufacturing industries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".