Evaluation of biofungicides for control of clubroot on canola
Bibliographic record
Abstract
Clubroot of canola, caused by the protist pathogen Plasmodiophora brassicae (Pb), is an \nemerging threat to canola production in western Canada. Effective/practical control options \nare currently lacking. This study was initiated to assess registered microbial fungicides for \ncontrol of clubroot on canola. Selected biofungicides were initially applied as a soil drench \nand the fungicides Allegro and Ranman were also included for comparisons. Selected \nproducts were further evaluated at varying concentrations, soil drench volumes, and for seed \ntreatment. At moderate disease pressure, the biofungicides Serenade and Prestop, as well as \nsynthetic fungicides Allegro and Ranman were highly effective as a soil-drench treatment, \nreducing clubroot severity by 85–100% in controlled conditions. Biofungicide concentration \nappeared to be important while soil-drench volumes may be reduced. All products, however, \nwere significantly less effective or ineffective under extremely high disease pressure. All \nproducts were less efficacious in trials using infested field soils, a circumstance that may be \nrelated to treatment timing. Results from seed-treatment trials were too variable to draw a \nconclusion but there was a strong indication that this approach be successful though more \nresearch is required on microbial formulations. Serenade, Prestop, Allegro, and Ranman \nshould be further evaluated under field conditions for clubroot control.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".