Herbicide programs for control of glyphosate-resistant canola (<i>Brassica napus</i>) in glyphosate-resistant soybean
Bibliographic record
Abstract
Abstract Glyphosate-resistant (GR) canola is a widely grown crop across western Canada and has quickly become a prolific volunteer weed. Glyphosate-resistant soybean is rapidly gaining acreage in western Canada. Thus, there is a need to evaluate herbicide options to manage volunteer GR canola in GR soybean crops. We conducted an experiment to evaluate the efficacy of various PRE and POST herbicides applied sequentially to volunteer GR canola and to evaluate soybean injury caused by these herbicides. Trials were conducted across Saskatchewan and Manitoba in 2014 and 2015. All treatments provided a range of suppression (>70%) to control (>80%) of volunteer canola. All treatments with the exception of the glyphosate-treated control reduced aboveground canola biomass by an average of 96%. As well, canola seed contamination was reduced from 36% to less than 5% when a PRE and POST herbicide were both used. Moreover, all combinations of herbicides used had excellent crop safety (<10%). All PRE and POST herbicide combinations provided better control of volunteer canola compared with the glyphosate-only control, but tribenuron followed by bentazon and tribenuron followed by imazamox plus bentazon provided solutions that were low cost, currently available (registered in western Canada), and had the potential to minimize development of herbicide resistance in other weeds.
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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 | 0.000 | 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) | 0.001 | 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".