Influence of protein concentration on farinograph absorption, mixing requirements and mixing tolerance
Bibliographic record
Abstract
Accurate and precise measurements of grain quality traits are required throughout the wheat (Triticum aestivum L. Em. Thell.) production and marketing chain to ensure that quality standards are maintained. In the present study, spring and winter wheat genotypes representing a wide range of grain quality types and protein concentration (PC) were grown in dry land and irrigated nitrogen fertilizer trials in western Canada to determine the influence of flour PC and quality on farinograph water absorption, mixing requirements and mixing tolerance. As expected, there was a strong linear relationship between PC and water absorption. In contrast, PC had a large nonlinear influence on mixing requirements and tolerance measurements, which made it difficult to make meaningful comparisons among genotypes when flour PC was less than 12%. Consequently, because cultivar grain yield potential, environment, and fertilizer management decisions have a large and variable influence on PC, it is important that cultivar selection procedures and marketing programs based on farinograph measurements include grain quality comparisons made over a wide range of PC and preferably at similar PC. This allows for a more accurate assessment of cultivar genetic potential that is independent of the normally strong negative relationship between PC and grain yield potential. When combined with the ability to segregate grain on the basis of PC at the time of delivery, it also allows producers a greater opportunity to select cultivars and adopt management practices that maximize returns while maintaining effective quality control in grain handling systems. Key words: Wheat, grain quality, farinograph, protein concentration, N management, cultivar selection and marketing
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".