<i>Leptosphaeria maculans</i> Isolates Reveal Their Allele Frequency in Western Canada
Bibliographic record
Abstract
Blackleg, caused by Leptosphaeria maculans, is a major disease of canola in Canada, Australia, and Europe. For effective deployment of resistant varieties and disease management, it is crucial to understand the population structure of L. maculans. In this study, we analyzed L. maculans isolates from commercial fields in western Canada from 2014 to 2016 for the presence and frequency of avirulence (Avr) genes. A total of 1,584 isolates were examined for the presence of Avr genes AvrLm1, AvrLm2, AvrLm3, AvrLm4, AvrLm6, AvrLm7, AvrLm9, AvrLepR1, AvrLepR2, and AvrLmS via a set of differential host genotypes carrying known resistance genes and a PCR assay. Several Avr genes showed a higher frequency in the pathogen population, such as AvrLm6 and AvrLm7, which were present in >90% of isolates, whereas AvrLm3, AvrLm9, and AvrLepR2 showed frequencies of <10%. A total of 189 races (different combinations of Avr genes) were detected, with Avr-2-4-6-7-S, Avr-1-4-6-7, and Avr-2-4-6-7 as the three predominant races. When the effect of crop rotation was assessed, only a 3-year rotation showed a significantly higher frequency of AvrLm2 relative to shorter rotations. This study provides the information for producers to select effective canola varieties for blackleg management and for breeders to deploy new R genes in disease resistance breeding in western Canada.
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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 | 0.000 | 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".