Control of glyphosate-resistant waterhemp with preemergence herbicides in glyphosate- and dicamba-resistant soybean
Bibliographic record
Abstract
Glyphosate-resistant (GR) waterhemp was first discovered in Ontario, Canada, in 2014. In Ontario, GR waterhemp interference in previous studies reduced soybean yield up to 73%. Tank-mixes of herbicides with multiple modes-of-action are important for delaying the evolution of herbicide resistance. The objective of this study was to evaluate the efficacy of pyroxasulfone (150 g a.i. ha−1), S-metolachlor/metribuzin (1943 g a.i. ha−1), pyroxasulfone/sulfentrazone (300 g a.i. ha−1), and pyroxasulfone/flumioxazin (240 g a.i. ha−1) applied preemergence (PRE) with and without the addition of glyphosate/dicamba (1800 g a.e. ha−1) for the control of GR waterhemp in soybean. At 8 wk after treatment application (WAA), glyphosate/dicamba applied PRE controlled GR waterhemp 45%. Pyroxasulfone, S-metolachlor/metribuzin, pyroxasulfone/sulfentrazone, and pyroxasulfone/flumioxazin applied PRE controlled GR waterhemp 79%, 87%, 91%, and 95%, respectively. At 2, 4, 8, and 12 WAA, the addition of glyphosate/dicamba to the aforementioned PRE herbicides did not improve GR waterhemp control. There was no increase in GR waterhemp control with the addition glyphosate/dicamba; however, multiple herbicide modes-of-action should be utilized to reduce the selection intensity for herbicide-resistant 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 | 0.000 | 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".