Race characterization of <i>Pyrenophora tritici-repentis</i> and sensitivity to propiconazole and pyraclostrobin fungicides
Bibliographic record
Abstract
Tan spot, caused by Pyrenophora tritici-repentis (Ptr), is one of the most destructive leaf spot diseases of wheat in Canada. Several races of the fungus are known to occur. Wheat growers have adopted fungicides to manage tan spot; however, intensive spraying may lead to development of reduced sensitivity to fungicides in the pathogen. In this study, 62 Ptr isolates were collected from across Saskatchewan and Alberta and characterized to race on a wheat differential set, with confirmation of race designations by polymerase chain reaction. This same set of isolates, and 27 isolates from an earlier study, were also evaluated for sensitivity to propiconazole and pyraclostrobin fungicides by determining the effective concentration of each needed to inhibit radial growth and conidiospore germination by 50%. Races 1 and 2 were predominant in western Canada, with 57% of isolates classified as race 1 and 40% as race 2. In Alberta, race 1 was most common (76%), while in Saskatchewan, it was race 2 (57%). Two of 26 isolates were classified as race 3, which was detected only in Saskatchewan in 2013. There was no evidence of reduced sensitivity of either fungicide in the Ptr isolates examined. This study provides a baseline for Ptr sensitivity to propiconazole and pyraclostrobin to which future collections of the pathogen may be compared.
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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 | 0.000 | 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) | 0.001 | 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".