IMPROVING INTEGRATED DISEASE MANAGEMENT TOOLS FOR BACTERIAL LEAF STREAK OF WHEAT IN CANADA
Bibliographic record
Abstract
Bacterial leaf streak (BLS), caused by the bacterium Xanthomonas translucens (Xt), is a biologically and economically significant disease affecting wheat worldwide. The disease manifests as water-soaked streaks on leaves, which eventually turn necrotic, leading to reduced photosynthetic capacity and yield loss. Integrated disease management (IDM) is a holistic approach that aims to combine various strategies such as to manage BLS. However, at present, options remain limited, with no resistant cultivars or effective chemical or biological control strategies currently available. Development and use of resistant wheat cultivars is one strategy. Researchers have identified several genetic sources of resistance to BLS, and incorporating these into commercial cultivars may help manage the disease more effectively over time. Continuous efforts in breeding programs are essential to stay ahead of evolving pathogen populations. This bacteria is seed borne, meaning that the pathogen can survive on the seeds. Early detection through field scouting and seed diagnostic tools can prevent the spread of the disease. This project aims to contribute to the development of integrated management practices by focusing on two key objectives. The first is the development of a seed testing protocol for early detection of Xt using Loop-mediated isothermal amplification (LAMP). With this protocol the specific pathovar (undulosa) of Xanthomonas translucens that is most problematic on wheat can be detected at a DNA concentration of 2 pg/reaction on field samples. The other strategy to manage BLS is through resistant cultivars, which I focused on by evaluating reactions to BLS among 96 wheat cultivars and 130 elite breeding lines under field and controlled conditions. Promising cultivars and lines with resistance or reduced susceptibility against BLS were identified. This work should contribute to enhancement of effective strategies to ensure sustainable wheat production in the face of the challenge of BLS.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".