Relationship of Kernel Size to Flour Water Absorption for Canada Western Red Spring Wheat
Bibliographic record
Abstract
ABSTRACT Canada Western Red Spring (CWRS) wheat exhibits consistent positive relationships between kernel weight and farinograph and baking water absorption. These relationships are sufficiently robust to be statistically significant (P < 0.05) for historical Canadian Grain Commission harvest survey data generated one year apart for 17 years, and for historical data on individual cultivars in advanced Canadian wheat breeding trials, also generated annually. Verification of the relationships were obtained by analyzing different kernel size fractions obtained by sieving CWRS harvest survey samples and pure CWRS cultivars from various origins. In all cases, highly significant positive relationships were observed between kernel size and water absorption. The relationships were evident for individual streams from pilot‐scale millings of sized fractions from CWRS harvested in two different years. Strong correlations of kernel weight to farinograph and baking absorption also were shown for sized fractions from commercial samples of American Dark Northern Spring and Australian Prime Hard wheat. The strong statistical association between kernel size and water absorption could not be explained on the basis of wheat hardness (flour starch damage), protein content, or dough strength. In view of the importance of flour water absorption to bakers, further investigation is warranted to identify the cause for the association between large kernel size and high water absorption.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".