Protein Structural Features in Winter Wheat: Benchmarking Diversity in Ontario Hard and Soft Winter Wheat
Bibliographic record
Abstract
A set of 32 winter wheat lines and varieties was selected to benchmark Ontario winter wheat as a first step toward improving quality. Protein secondary structure, total and accessible thiols, rheological properties, gluten aggregation kinetics, and network forming capabilities of different polymers were determined for each wheat line. Results revealed that there were statistically significant differences among the lines selected (P < 0.05). The differences between hard and soft wheat classes were not as large as would be expected, however, despite the range of quality parameters measured. Benchmarks revealed that several soft wheat lines outperformed hard wheat lines in standard breadmaking quality measures. Protein conformation changed significantly as the moisture content of the samples increased to mimic different model product systems: flour, dough, and batter. The conformation of the flour samples exhibited different patterns between hard and soft wheat classes, although these differences became narrower in the dough and batter states. Principal component analysis (PCA) factors included most quality parameters measured, with the notable exceptions of solvent retention capacity tests and total thiols. Protein conformation and accessible thiols were significant PCA factors that tended to override the rheological measures of quality they represented, suggesting that protein secondary structure and disulfide bonding patterns are fundamental aspects of rheological quality measures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".