Control of glyphosate-resistant Canada fleabane in Ontario with multiple effective modes-of-action in glyphosate/dicamba-resistant soybean
Bibliographic record
Abstract
Canada fleabane is a winter or summer annual weed that is found throughout North America. Fall-emerged Canada fleabane can fix carbon early in the growing season, giving it a competitive advantage over nearby crop and weed species. Glyphosate-resistant (GR) Canada fleabane was originally found in one county in Ontario, Canada, in 2010 and had spread to at least 29 additional counties within the province by 2016. Previous research with several preplant herbicides resulted in variable control of GR Canada fleabane in soybean. The objective of this study was to evaluate the efficacy of glyphosate/dicamba (1800 g a.e. ha−1) alone or with the addition of a second effective mode-of-action for the control of GR Canada fleabane in glyphosate/dicamba-resistant soybean. At 4 weeks after application, glyphosate/dicamba + saflufenacil, saflufenacil/dimethenamid-P, saflufenacil/imazethapyr, or paraquat controlled GR Canada fleabane 97%, 96%, 97%, and 98%, respectively. All herbicide treatments decreased Canada fleabane density and biomass by 93%–99%. When choosing herbicide programs, it is important to consider the use of multiple modes-of-action to decrease selection pressure for the evolution of herbicide-resistant Canada fleabane. Treatments containing saflufenacil, saflufenacil/dimethenamid-P, or saflufenacil/imazethapyr with the addition of glyphosate/dicamba are recommended for the control of GR Canada fleabane.
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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 | 0.001 | 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".