Detection of Leptosphaeria maculans and Leptosphaeria biglobosa Causing Blackleg Disease in Canola from Canadian Canola Seed Lots and Dockage
Bibliographic record
Abstract
Blackleg, caused by Leptosphaeria maculans, is a major threat to canola production in Canada. With the exception of China, L. maculans is present in areas around the world where cruciferous crops are grown. The pathogen can cause trade barriers in international canola seed export due to its potential risk as a seed contaminant. The most recent example is China restricting canola seeds imported from Canada and Australia in 2009. Therefore, it is important to assess the level of Blackleg infection in Canadian canola seed lots and dockage (seeds and admixture). In this study, canola seed lots and dockage samples collected from Western Canada were tested for the presence of the aggressive L. maculans and the less aggressive L. biglobosa. Results showed that both L. maculans and L. biglobosa were present in seed lots and dockage samples, with L. biglobosa being predominant in infected seeds. Admixture separated from dockage had higher levels of L. maculans and L. biglobosa infection than samples from seed lots. Admixture appears to harbour higher levels of L. maculans infection compared to seeds and is more likely to be a major source of inoculum for the spread of the disease than infected seeds.
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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".