Multiple-resistant waterhemp control in herbicide-resistant 3 (HT3) soybean
Bibliographic record
Abstract
Herbicide-resistant (HR) crops, specifically glyphosate-, glufosinate-, and dicamba-resistant (HT3) soybean, will offer producers a new weed management option for the control of some HR weeds in soybean. Four field experiments were conducted near Cottam and on Walpole Island, ON, Canada, during 2017 and 2018 to assess the control of multiple-resistant (MR) waterhemp (herbicide groups 2, 5, and 9) in HT3 soybean treated with various herbicide programs. Pyroxasulfone/flumioxazin, flumioxazin plus metribuzin, or S-metolachlor/metribuzin applied preemergence (PRE) and followed by (fb) glyphosate postemergence (POST) controlled MR waterhemp at 94%, 66%, and 78%, respectively, in early September. Pyroxasulfone/flumioxazin, flumioxazin plus metribuzin, or S-metolachlor/metribuzin applied PRE controlled MR waterhemp 86%–97% when fb glufosinate POST; 100% when fb glyphosate plus dicamba POST; 99%–100% when fb glufosinate plus dicamba POST; and 100% when fb glyphosate plus dicamba POST and fb glufosinate POST2 (before the R2 soybean growth stage) in early September. Reduced MR waterhemp interference with all herbicide programs resulted in an increase in HT3 soybean yield (up to 59%) relative to the weedy control. Results indicate that pyroxasulfone/flumioxazin, flumioxazin plus metribuzin, or S-metolachlor/metribuzin applied PRE fb glufosinate POST, glyphosate plus dicamba POST, glufosinate plus dicamba POST, or glyphosate plus dicamba POST fb glufosinate POST2 provides similar and excellent season-long control of MR waterhemp in HT3 soybean.
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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".