Analysis of <i>Leptosphaeria maculans</i> Race Structure in a Worldwide Collection of Isolates
Bibliographic record
Abstract
ABSTRACT Leptosphaeria maculans, the causal agent of stem canker of oilseed rape, develops gene-for-gene interactions with its hosts. To date, eight L. maculans avirulence (Avr) genes, AvrLm1 to AvrLm8, have been genetically characterized. An additional Avr gene, AvrLm9, that interacts with the resistance gene Rlm9, was genetically characterized here following in vitro crosses of the pathogen. A worldwide collection of 63 isolates, including the International Blackleg of Crucifers Network collection, was genotyped at these nine Avr loci. In a first step, isolates were classified into pathogenicity groups (PGs) using two published differential sets. This analysis revealed geographical disparities as regards the proportion of each PG. Genotyping of isolates at all Avr loci confirmed the disparities between continents, in terms of Avr allele frequencies, particularly for AvrLm2, AvrLm3, AvrLm7, AvrLm8, and AvrLm9, or in terms of race structure, diversity, and complexity. Twenty-six distinct races were identified in the collection. A larger number of races (n = 18) was found in Australia than in Europe (n = 8). Mean number of virulence alleles per isolate was also higher in Australia (5.11 virulence alleles) than in Europe (4.33) and Canada (3.46). Due to the diversity of populations of L. maculans evidenced here at the race level, a new, open terminology is proposed for L. maculans race designation, indicating all Avr loci for which the isolate is avirulent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".