Effects of cultivar and environment on farinograph and Canadian short process mixing properties of Canada Western Red Spring wheat
Bibliographic record
Abstract
Fourteen Canada Western Red Spring (CWRS) wheat (Triticum aestivum L.) cultivars grown at six Saskatchewan sites in 1995 were used to assess the impact of cultivar and environment on dough mixing properties. ANOVA showed that cultivar, environment and interactions thereof generally had highly significant effects on farinograph and Canadian short process (CSP) water absorption and mixing parameters. Most of the variance in farinograph and CSP absorption could be attributed to environment, while cultivar was the predominant contributor to farinograph dough development time and stability. Both cultivar and environment had a strong influence on CSP mixing time and mixing energy. Neither protein nor particle size index as covariate had a strong influence on the partitioning of variance. Strong significant relationships were evident among absorption parameters and among most mixing strength parameters. No strongly significant relationships were found between absorption and mixing strength parameters. Significant differences were evident among cultivar means and among station means for all parameters. The strong impact of environment on most mixing properties suggests that attention should be focused on the potential adverse impact on the uniformity of CWRS shipments due to the trend towards reduced blending among regions associated with the continued development of large high throughput primary elevators. Key words: Cultivar, environment, wheat, dough mixing properties
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".