Detection of Blackleg Resistance Gene Rlm1 in Double-Low Rapeseed Accessions from Sichuan Province, by Kompetitive Allele-Specific PCR
Bibliographic record
Abstract
Blackleg is a serious disease in Brassica plants, causing moderate to severe yield losses in rapeseed worldwide. Although China has not suffered from this disease yet (more aggressive Leptosphaeria maculans is not present yet), it is crucial to take provisions in breeding for disease resistance to have excellent blackleg-resistant cultivars already in the fields or in the breeding pipeline. The most efficient strategy for controlling this disease is breeding plants with identified resistance genes. We selected 135 rapeseed accessions in Sichuan, including 30 parental materials and 105 hybrids, and we determined their glucosinolate and erucic acid content and confirmed 17 double-low materials. A recently developed single-nucleotide polymorphism (SNP) marker, SNP_208, was used to genotype allelic Rlm1/rlm1 on chromosome A07, and 87 AvrLm1-resistant materials. Combined with the above-mentioned seed quality data, we identified 11 AvrLm1-resistant double-low rapeseed accessions, including nine parental materials and two hybrids. This study lays the foundation of specific R gene-oriented breeding, in the case that the aggressive Leptosphaeria maculans invades and establishes in China in the future and a robust and less labor consuming method to identify resistance in canola germplasm.
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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 | 0.000 | 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".