Comparison of a Glyphosate-Resistant Canola (<i>Brassica napus</i>L.) System with Traditional Herbicide Regimes
Bibliographic record
Abstract
Herbicide-resistant cultivars account for over 90% of the canola grown in western Canada and cultivars resistant to glyphosate dominate the market. Field experiments were conducted at three locations in Alberta to compare the glyphosate system with more traditional herbicide regimes. Glyphosate applied before seeding in spring resulted in better weed control, lower dockage, and higher canola yield and net return than 2,4-D applied in the fall. Glyphosate applied once (two- to four-leaf canola) or twice (two- to four-leaf followed by five- to six-leaf canola) in-crop provided similar weed control, dockage, and canola yield as ethalfluralin applied PRE in the fall followed by an in-crop mixture of sethoxydim, ethametsulfuron, and clopyralid; and superior weed control and canola yield and lower dockage than ethalfluralin alone or an in-crop mixture of sethoxydim and ethametsulfuron. The in-crop glyphosate applications resulted in higher net revenues than the other treatments. There was little or no advantage to applying glyphosate twice compared with once in-crop. The amount of active ingredient entering the environment varied with the herbicide regime but was lower with the glyphosate system than with most of the traditional regimes, especially when glyphosate was applied only once in-crop.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".