Sustainability on the farm: breeding for resistance and management of major canola diseases in Canada contributing towards an IPM approach
Bibliographic record
Abstract
Genetic diversity is vital for the survival of any population.If humans were all the same, a single strain of a nasty flu virus, like COVID-19, could wipe us all out!In plants, genetic diversity plays a similar role.The variation in the type and number of resistance gene(s) between individuals can cause the difference between surviving a disease or not.Studies on genetic diversity will lead to the identification of novel disease-resistance genes.Canola (Brassica napus L.) is an economically and nutritionally important oilseed worldwide.Several serious diseases, including blackleg, clubroot, sclerotinia stem rot, and verticillium stripe, threaten canola production in Canada and worldwide.Traditional methods are not enough for effective control of these diseases.Therefore, the ideal approach is to optimize and utilize the resistance genes found in different B. napus cultivars.With the advent of next-generation sequencing and the development of genomics and molecular genetics techniques, it is now possible to rapidly identify and apply resistance genes.This paper reviews current information about disease-resistance genes identified in B. napus cultivars, mapping and cloning, their importance, role and function, and their association with plant disease resistance and application in resistance breeding.The feasibility of using current resistance sources in Canadian cultivars for developing new disease-resistant cultivars is also discussed.Sustainability of a farm and an agricultural system could be maintained by breeding for disease resistance, including the resistant varieties and incorporating other integrated pest management strategies along with it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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".