Herbicide Timing and Rate Effects on Weed Management in Three Herbicide-Resistant Canola Systems
Bibliographic record
Abstract
Herbicide-resistant canola dominates the canola market in Canada. A multiyear field experiment was conducted at three locations to investigate the effect of time of weed removal (two-, four-, or six-leaf canola) and herbicide rate (50 or 100% recommended) in three herbicide-resistant canola systems. Weeds were controlled in glufosinate-resistant canola (GLU) with glufosinate, in glyphosate-resistant canola (GLY) with glyphosate, and in imidazolinone-resistant canola (IMI) with a 50:50 mixture of imazamox and imazethapyr. Canola yields were similar among the three canola cultivar–herbicide systems. Yields were not influenced by 50 vs. 100% herbicide rates. Timing of weed removal had the greatest effect on canola yield, with weed removal at the four-leaf stage giving the highest yields in most cases. Percent dockage was often greater for GLU and IMI than for GLY. In comparison with the other treatments, dockage levels doubled for GLU after application at 50% herbicide rates. The consistency of monocot weed control was usually greater for GLY than for GLU or IMI systems. However, weed biomass data revealed no differences in dicot weed control consistency between IMI and GLY systems. Greater dockage and weed biomass variability after weed removal at the six-leaf stage or after low herbicide rates suggests higher weed seed production, which could constrain the adoption of integrated weed management practices in subsequent years.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".