The interaction of pyroxasulfone and flumioxazin applied preemergence for the control of multiple-herbicide-resistant waterhemp (<i>Amaranthus tuberculatus</i>) in soybean
Bibliographic record
Abstract
Abstract Six field experiments were conducted to investigate any interaction between pyroxasulfone and flumioxazin on soybean tolerance and control of multiple-herbicide-resistant (MHR) waterhemp in soybean during 2016 and 2017 in Ontario, Canada. There was a synergistic increase in soybean injury with the co-application of pyroxasulfone and flumioxazin at all rates evaluated at 2 wk after emergence (WAE), the two highest rates evaluated (134/106 and 268/211 g ai ha –1 ) at 4 WAE, and the highest rate (268/211 g ai ha –1 ) evaluated at 8 WAE. Soybean injury with all pyroxasulfone and flumioxazin treatments was transient and had no adverse effect on soybean grain yield. Pyroxasulfone applied preemergence at 45, 89, 134, and 268 g ai ha –1 controlled MHR waterhemp up to 72%, 89%, 92%, and 95%, respectively. Flumioxazin applied preemergence at 35, 70, 106, and 211 g ai ha –1 controlled MHR waterhemp up to 78%, 90%, 93%, and 96%, respectively. Pyroxasulfone/flumioxazin applied preemergence at 45/35, 89/70, 134/106, and 268/211 g ai ha –1 controlled MHR waterhemp up to 92%, 96%, 98%, and 100%, respectively. There were no significant antagonistic or synergistic interactions for the control of MHR waterhemp with pyroxasulfone/flumioxazin at rates evaluated except at 268/211 g ai ha –1 , which provided a synergistic increase in MHR waterhemp control at 4 WAE. The MHR waterhemp biomass and density reductions followed a trend similar trend to visible control. Pyroxasulfone/flumioxazin at 268/211 g ai ha –1 caused a synergistic response in biomass reduction (9% difference). Based on these results, there is an additive increase in MHR waterhemp control and potential for a synergistic increase in soybean injury with the co-application of pyroxasulfone plus flumioxazin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".