Testing for resistance to smut diseases of barley, oats and wheat in western Canada
Bibliographic record
Abstract
The smut pathogens of barley (Hordeum vulgare), oat (Avena sativa), and wheat (Triticum aestivum) were likely introduced to Canada on seed of their hosts by pioneering farmers. In the late 19th and early 20th centuries, smut diseases were common and often severe in western Canada. These diseases have become effectively controlled through the use of certified seed, seed treatment fungicides, and resistant cultivars. Genetic resistance is inherent in the cultivar and is often preferred over other control techniques because it avoids the costs, environmental concerns, and health risks that can be associated with seed-treatment chemicals. Resistance to smut diseases of barley, oat, and wheat are criteria used in the evaluation of new lines for registration for commercial production in western Canada. This article provides a brief introduction to the taxonomy of smut pathogens of small grain cereals and the methods used for determining pathogenic variability within the pathogen population. We also describe the techniques used for inoculating and assessing candidate cultivar lines for smut resistance in western Canada. These techniques are not difficult, and large numbers of lines can be tested each year; however, they are labour, time, and physical-resource intensive. The integration of plant pathology and breeding and the role of the Canadian regulatory system as they relate to varietal resistance to the smut fungi are considered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".