Development of a SCAR Marker to Track Canola Resistance Against Blackleg Caused by <i>Leptosphaeria maculans</i> Pathogenicity Group 3
Bibliographic record
Abstract
Blackleg of rapeseed and canola (Brassica napus) is caused by various pathogenicity groups (PG) of Leptosphaeria maculans. The disease occurring in the Canadian prairies for the last two decades was caused by PG2 and was controlled by host resistance. PG3 and PG4 isolates have been found recently in Canada, but there is no resistance available against these pathogenicity groups in commercial Canadian varieties. This study sought to identify canola cultivars that could be used as sources of resistance to PG3 and to develop molecular markers for marker-assisted selection. Resistance to PG3 specifically was found in B. napus ‘Dunkeld’ and ‘Quinta’, while B. juncea ‘Cutlass’ and ‘Domo’ proved to be resistant to PG2, PG3, and PG4. A set of F2 progeny of ‘Westar’ (susceptible) × ‘Dunkeld’ was used to identify genetic markers linked to PG3 resistance. These markers were physically located on a BAC clone from B. rapa subsp. pekinensis containing a homolog to a serine threonine 20 (ste20)-like kinase in Arabidopsis thaliana. Thus, we have developed a sequence characterized amplified region (SCAR) marker available for marker-assisted selection in breeding canola for resistance against blackleg caused by L. maculans PG3. This work has received a provisional patent (serial # 60/977,933 – Oct. 5, 2007).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".