Identification, Mapping, and Economic Evaluation of QTLs Encoding Root Maggot Resistance in <i>Brassica</i>
Bibliographic record
Abstract
Commercial varieties of canola (Brassica napus L. and B rapa L.) are susceptible to infestations by the root maggots Delia radicum (L.) and Delia floralis (Fallén) (Diptera: Anthomyiidae) in western Canada. Although cultural strategies can ameliorate crop damage from root maggot infestations, these methods are not sufficiently effective to prevent substantial economic losses. In this paper we report the development of germplasm for resistance to root maggot infestations and the introgression of genes from a resistant relative (Sinapis alba L.) to susceptible B napus The effectiveness of the conferred resistance to root maggot damage was validated by comparing different genotypes for pest damage and yield loss with and without insecticide applications. Yield of B napus plants, susceptible to root maggot infestations, increased when insecticide was applied (by up to 24%), but no significant yield differences were observed among resistant intergeneric hybrids that were treated or not treated with insecticide. One hundred and thirty‐five restriction fragment length polymorphisms (RFLPs) were used to construct a B. napus genetic linkage map and to identify quantitative trait loci (QTLs) associated with resistance to root maggot damage. Two QTLs, RM‐G8 and RM‐G4, were found to be associated with resistance to root maggot damage. Together, these two QTLs explain 54.6% of the total variation observed.
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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 | 0.000 | 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 | 0.000 |
| 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".