Yield losses and pyraclostrobin sensitivity in blackleg (Leptosphaeria maculans) of canola
Bibliographic record
Abstract
Blackleg, caused by Leptosphaeria maculans (Desm.) Ces. & de Not., is an important disease of canola (Brassica napus L.) worldwide. In Canada, blackleg is managed mainly by the cultivation of resistant or moderately resistant canola hybrids and the application of fungicides. Field experiments were conducted in central Alberta in 2017 and 2018 to determine the relationship between blackleg severity and yield in two moderately resistant hybrids ‘73-15RR’ and ‘1950RR’. Seed yield per plant was found to decrease as a consequence of L. maculans infection, with regression analysis showing that the relationship between yield and disease severity was best explained by quadratic equations. Sensitivity to the fungicide pyraclostrobin, a strobilurin that is commonly applied as a foliar and seed treatment for blackleg and other diseases, was compared in L. maculans collections made in Alberta in 2011 and 2016. The half-maximal effective concentration (EC50) of pyraclostrobin was determined using agar and microtiter plate assays, and two discriminatory doses of the fungicide were selected to identify highly insensitive isolates in the collections. The mean EC50 was approximately 4× greater for the isolates collected in 2016 versus those collected in 2011. While almost all isolates were still sensitive to pyraclostrobin, this increase in the EC50 suggests that proper fungicide stewardship is warranted for the sustainable long-term management of L. maculans.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".