Oat mega‐environments in Canada
Bibliographic record
Abstract
Abstract Genotype × environment interaction (GE) is a perennial problem in plant breeding and must be dealt with. Dealing with GE starts with differentiation of repeatable GE from unrepeatable GE in a target region. Repeatable GE can be used by dividing the target region into mega‐environments (MEs) and breeding ME‐specific cultivars, and unrepeatable GE must be accommodated by testing adequately within a ME. This study applied LG (location‐grouping) biplot analysis to several datasets from multiyear oat ( Avena sativa L.) variety trials conducted at locations across Canada. Analysis showed that the oat growing regions in Canada can be divided into three MEs: the crown rust ( Puccinia coronata Corda f. sp. avenae Eriks.) prone regions in southern and eastern Ontario (ME1), other regions in eastern Canada (ME2), and the Canadian Prairies (ME3). In addition, two sub‐MEs existed within ME2. Latitude was shown to be the main factor for the ME differentiation. The results suggest that oat variety trials should be conducted and cultivar recommendation be made according to MEs, as opposed to by administrative regions that are currently in place.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".