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 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 | 0.000 | 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".