A Six-Year Investigation of the Dynamics of Avirulence Allele Profiles, Blackleg Incidence, and Mating Type Alleles of<i>Leptosphaeria maculans</i>Populations Associated with Canola Crops in Manitoba, Canada
Bibliographic record
Abstract
Blackleg, caused by the fungal pathogen Leptosphaeria maculans, is one of the most economically important diseases of canola (Brassica napus, oilseed rape) worldwide. This study assessed incidence of blackleg, the avirulence allele, and mating type distributions of L. maculans isolates collected in commercial canola fields in Manitoba, Canada, from 2010 to 2015. A total of 956 L. maculans isolates were collected from 2010 to 2015 to determine the presence of 12 avirulence alleles using differential canola cultivars and/or PCR assays specific for each avirulence allele. AvrLm2, AvrLm4, AvrLm5, AvrLm6, AvrLm7, AvrLm11, and AvrLmS were detected at frequencies ranging from 97 to 33%, where the AvrLm1, AvrLm3, AvrLm9, AvrLepR1, and AvrLepR2 alleles were the least abundant. When the race structure was examined, a total of 170 races were identified among the 956 isolates, with three major races, AvrLm-2-4-5-6-7-11, AvrLm-2-4-5-6-7-11-S, and Avr-1-4-5-6-7-11-(S) accounting for 15, 10, and 6% of the total fungal population, respectively. The distribution of the mating type alleles (MAT1-1 and MAT1-2) indicated that sexual reproduction was not inhibited in any of the nine Manitoba regions in any of the years L. maculans isolates were collected.
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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".