Effects of soil-borne <i>Rhizoctonia solani</i> on canola seedlings after application of glyphosate herbicide
Bibliographic record
Abstract
Rashid, A., Hwang, S. F., Ahmed, H. U., Turnbull, G. D., Strelkov, S. E. and Gossen, B. D. 2013. Effects of soil-borne Rhizoctonia solani on canola seedlings after application of glyphosate herbicide. Can. J. Plant Sci. 93: 97–107. Application of glyphosate (N-phosphonomethyl glycine) prior to seeding is a common weed management practice in many agricultural systems. However, there are concerns that this practice may increase the impact of soil-borne diseases on the crop, even in cultivars that are resistant to glyphosate. In the current study, the effects of pre-plant applications of glyphosate on seedling blight of canola caused by Rhizoctonia solani and subsequent crop growth were examined under field and greenhouse conditions. Under greenhouse conditions in soil inoculated with R. solani, glyphosate application 15 d before seeding reduced seedling emergence, increased damping-off, and decreased plant height and shoot dry weight of canola relative to a glyphosate-free control. However, the adverse effects were substantially reduced when the crop was seeded 33 d after glyphosate application. This indicates that glyphosate application prior to planting may increase the impact of R. solani on canola seedlings, but that this effect diminishes quite rapidly. Soil populations of R. solani declined over the 33-d period regardless of glyphosate treatment. Glyphosate application 10 d before seeding increased seedling emergence and seed yield (1 of 2 yr) of canola in field trials inoculated with R. solani.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".