Control of Multiple-Herbicide-Resistant Canada Fleabane With Fall, Spring, and Sequential Herbicide Applications in Winter Wheat
Bibliographic record
Abstract
Limited information exists on the efficacy of pyrasulfotole/bromoxynil, fluroxypyr/halauxifen-methyl + MCPA EHE, and clopyralid applied in the fall, spring, or sequentially [fall followed by (fb) spring] for the control of multiple-herbicide-resistant (MHR) Canada fleabane in winter wheat under Ontario environmental conditions. Three field experiments were initiated in the autumn of 2020 and 2021 for a total of 6 site-years to evaluate fall- and spring-applied herbicides and their sequential applications for the control of MHR Canada fleabane in winter wheat in Ontario. Pyrasulfotole/bromoxynil applied in the fall, spring, or sequentially controlled MHR Canada fleabane 83, 99, and 100%, respectively at 8 weeks after the spring application (WAB); the spring and sequential applications provided better control than the fall application. Fluroxypyr/halauxifen + MCPA and clopyralid applied in the fall, spring, or sequentially controlled MHR Canada fleabane 97 to 100% and 99 to 100%, respectively at 8 WAB. Based on orthogonal contrasts the spring and sequential herbicide applications provided greater control than the fall application (8 WAB). MHR Canada fleabane interference reduced winter wheat yield up to 27% in this study. Based on orthogonal contrasts reduced MHR Canada fleabane interference with the fall application resulted in 17% higher winter wheat yield than when herbicide application was delayed to the spring. Although MHR Canada fleabane was controlled very effectively with clopyralid winter wheat yield was lower, presumably due to crop injury; this observation will have to be explored further in future research. Results from this study indicate that pyrasulfotole/bromoxynil and fluroxypyr/halauxifen + MCPA applied in the fall can be used to effectively control MHR Canada fleabane and minimize winter wheat yield loss due to weed interference.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".