The effect of seed size, seed treatment, seeding date and depth on Rhizoctonia seedling blight of canola
Bibliographic record
Abstract
Hwang, S. F., Ahmed, H. U., Turnbull, G. D., Gossen, B. D. and Strelkov, S. E. 2014. The effect of seed size, seed treatment, seeding date and depth on Rhizoctonia seedling blight of canola. Can. J. Plant Sci. 94: 311–321. Rhizoctonia solani can have a substantial impact on seedling establishment and productivity of canola (Brassica napus). The effects of seeding date, seeding depth, seed size, and seed treatment on seedling blight of canola were evaluated under greenhouse and field conditions. Early seeding resulted in higher seedling emergence in one trial year and higher seed yield in all trial years relative to a late-seeded treatment. Mid-sized seed (range 0.7–2.0 mm diam.) had greater seedling emergence in R. solani-inoculated growth medium in a greenhouse trial and higher seed yield in one of two field trials compared with smaller seed (<0.7 mm). In the greenhouse study, sowing of large seed resulted in greater plant height and shoot weight compared with sowing of smaller seed. The effect of seeding depth was significant only on shoot dry weight, which increased at a seeding depth of 2.6 cm. Seed treatment with Helix Xtra (thiamethoxam+difenconazole+metalaxyl+fludioxonil), and Prosper FX (clothianidin+carboxin+trifloxystrobin+metalaxyl) resulted in a significant increase in seedling emergence and yield compared with the inoculated control. These results indicate that fungicidal seed treatment can minimize the impact of R. solani on canola.
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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 | 0.001 |
| 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 | 0.001 |
| 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".