Genotype × Location Interaction Patterns and Testing Strategies for Oat in the Canadian Prairies
Bibliographic record
Abstract
The Canadian Prairies is one of the most important oat (Avena sativa L.) production regions in the world and oat breeding objectives for the region include high grain yield, groat content, and dietary fiber concentration. Using data from the 2004 through 2009 Western Cooperative Oat Registration trials, we investigated yearly genotype × location interaction patterns for grain yield, groat percentage, and dietary fiber concentration and the relationships among these traits. Despite large hectareage and wide geographical range, the Canadian Prairies was found to be relatively homogeneous and can be regarded as a single mega‐environment, with southern Manitoba tending to be slightly different from other portions of the region. Simulation indicated that three test locations could suffice to represent the region and provide reliable information for relative grain yield of differing genotypes. Groat percentage and dietary fiber content were more heritable than grain yield thus requiring fewer (no more than three) test locations for reliable selection. β‐glucan and total dietary fiber were closely correlated, thus the latter could potentially be improved via selecting for the former, which is simpler to determine, although more research is required in this area. However, dietary fiber content was negatively correlated with grain yield and/or groat content, constituting a challenge for breeding milling oats.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".