Sensitivity of western Canadian <i>Pyrenophora teres</i> f. <i>teres</i> and <i>P. teres</i> f. <i>maculata</i> isolates to propiconazole and pyraclostrobin
Bibliographic record
Abstract
Pyrenophora teres f. teres (Ptt) and Pyrenophora teres f. maculata (Ptm), the causal agents of the net and spot forms of net blotch of barley, respectively, can be managed in western Canada with fungicides containing propiconazole and pyraclostrobin. Given the potential for development of fungicide resistance, the sensitivity of a collection of Ptt and Ptm isolates to propiconazole and pyraclostrobin was evaluated using microtitre plate bioassays. The concentration of propiconazole needed to inhibit fungal growth by 50% (EC50) was 1.5 mg L−1 for Ptt and 2.3 mg L−1 for Ptm, while the EC50 of pyraclostrobin was 0.015 mg L−1 for Ptt and 0.024 mg L−1 for Ptm. Subsequently, 39 Ptt and 27 Ptm isolates were screened with discriminatory doses of 5 mg propiconazole L−1 and 0.15 mg pyraclostrobin L−1. Inhibition of growth as a result of propiconazole was 12–95% for Ptt and 48–92% for Ptm; growth inhibition as a result of pyraclostrobin was 40–100% and 24–100%, respectively. Two Ptt isolates were insensitive to propiconazole, while one Ptm isolate was insensitive to pyraclostrobin. The latter also showed decreased sensitivity to propiconazole. The identification of net blotch isolates insensitive to these fungicides emphasizes the need for farmers to employ integrated crop management strategies to avoid fungicide resistance build-up.
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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) | 0.001 | 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".