Strategic genetic insights and integrated approaches for successful management of blackleg in canola/rapeseed farming
Bibliographic record
Abstract
Abstract This review describes a triumphant narrative in the battle against the devastating plant pathogen complex, Leptosphaeria maculans and L . biglobosa , and the success of the world's second‐largest oilseed crop, canola/oilseed rape. Emphasizing global collaborations, the article explores successfully mitigating this destructive disease in canola/rapeseed production across Australia, Canada and Europe. It highlights how strategies may vary between continents to adapt to specific contexts. While initial resistance ( R ) genes proved effective, the evolution of the pathogen under crop‐induced disease pressure led to the breakdown of these genes. Now, growers in these regions have been equipped with new tools, allowing them to make informed decisions that help to keep the disease at generally low levels. A pivotal factor in this success has been a deepened understanding of the intricate science underlying the host–pathogen interaction. Concerted efforts of individual laboratories and collaborative initiatives have played an essential role in this success, including novel methods for disease control based on extensive research that has translated into developing highly resistant varieties, enhanced pathogen monitoring, improved cultivar recommendation and integrated management strategies. The review showcases many milestone advancements, including the cloning of numerous avirulence genes within the pathogen, characterization of specific R genes, the development of various molecular tools for monitoring both pathogen and host, the introduction of groundbreaking disease management strategies such as R gene labelling, rotation and stacking, and establishment of a universal pathogen isolate collection that facilitates the exchange of information among multiple laboratories and adds a new dimension to this triumph.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".