The race structure of <i>Leptosphaeria maculans</i> in western Canada between 2012 and 2014 and its influence on blackleg of canola
Bibliographic record
Abstract
Field surveys indicate blackleg of canola has increased in western Canada since 2010. Earlier studies showed changes in the pathogen (Leptosphaeria maculans (Sowerby) P.Karst.; Lm) population between 2007 and 2010–2011. In this study, Lm isolates were collected from trap plots (591) and/or commercial fields (372) on the prairies between 2012 and 2014, and tested for the profile of avirulence (Av) genes on a set of host differentials for 10 Lm Av alleles. Up to 35 ‘Westar’ trap plots were set up each year, and commercial fields were surveyed in the same area in 2012 and 2013. A similar trend was observed for Av profile between the two Lm populations; Av1, Av3, Av9 and AvLep2 were at low or very low levels, while Av2, Av4, Av6 and Av7 were present in >60% of the isolates. A total of 82 races were identified in these Lm populations, suggesting a greater diversity than those reported elsewhere. Margalef and Simpson indices also confirmed the greater genetic diversity in Lm. In fact, for each known R gene, there is at least one virulent race already existing in the Lm population. The races carrying Av2, Av4, Av6 or Av7 accounted for >70% of the Lm population, indicating that an R gene corresponding to any of these Av alleles may be effective against blackleg on the prairies. However, Av results alone appeared insufficient for explaining different levels of blackleg observed in canola fields; additional factors, possibly including crop rotation and local weather, may also play a role.
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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 | 0.001 |
| 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".