Resistance to the demethylation‐inhibiting fungicide propiconazole in Canadian populations of <i>Microdochium nivale</i>
Bibliographic record
Abstract
Abstract Turfgrass managers have anecdotally reported decreased efficacy of DMI fungicides for the control of Microdochium patch and pink snow mold at golf courses in British Columbia, Canada. Isolates of Microdochium nivale from these locations, along with isolates collected in Ontario, Canada, were tested for their sensitivity to the fungicide propiconazole [1‐([2‐(2,4‐dichlorophenyl)‐4‐propyl‐1,3‐dioxolan‐2‐yl]methyl)‐1,2,4‐triazole]. Ontario isolates (47) had values for effective concentration causing 50% growth inhibition (EC 50 ) ranging from <0.001 to 0.89 µg ml –1 . In comparison, British Columbia isolates (50) had an EC 50 range of 0.02 to 8.7 µg ml –1 . Sensitivity testing with a discriminatory concentration (0.1 µg ml –1 ) of a larger set of isolates revealed that 24% of Ontario isolates (43 of 181) and 77% of British Columbia isolates (55 of 71) exhibited resistance to propiconazole (>50% growth on 0.1 µg ml –1 compared to non‐amended media). Because of the cool, wet climate of coastal British Columbia, turfgrass managers use more applications of fungicides annually, including propiconazole, to control diseases caused by M. nivale , and this has resulted in a greater proportion of isolates being resistant to propiconazole. In contrast, Ontario has a less favorable climate for these diseases, with accordingly fewer fungicide applications directed toward this pathogen and hence less risk of fungicide resistance developing.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".