Multiple-herbicide-resistant waterhemp control in glyphosate/glufosinate/2,4-D-resistant soybean with one- and two-pass weed control programs
Bibliographic record
Abstract
Abstract Waterhemp control in Ontario has increased in complexity due to the evolution of biotypes that are resistant to five herbicide modes of action (Groups 2, 5, 9, 14, and 27 as categorized by the Weed Science Society of America). Four field trials were carried out over a 2-yr period in 2021 and 2022 to assess the control of multiple-herbicide-resistant (MHR) waterhemp biotypes in glyphosate/glufosinate/2,4-D-resistant (GG2R) soybean using one- and two-pass herbicide programs. S-metolachlor/metribuzin, pyroxasulfone/sulfentrazone, pyroxasulfone/flumioxazin, and pyroxasulfone + metribuzin applied preemergence (PRE) controlled MHR waterhemp similarly by 46%, 63%, 60%, and 69%, respectively, at 8 wk after postemergence (POST) application (WAA-B). A one-pass application of 2,4-D choline/glyphosate DMA POST provided greater control of MHR waterhemp than glufosinate. Two-pass herbicide programs of a PRE herbicide followed by (fb) a POST-applied herbicide resulted in greater MHR waterhemp control compared to a single PRE or POST herbicide application. PRE herbicides fb glufosinate or 2,4-D choline/glyphosate DMA POST controlled MHR waterhemp by 74% to 91% and by 84% to 96%, respectively, at 8 WAA-B. Two-pass herbicide applications of an effective PRE residual herbicide fb 2,4-D choline/glyphosate DMA POST in GG2R soybean can effectively manage waterhemp that is resistant to herbicides in Groups 2, 5, 9, 14, and 27.
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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) | 0.001 | 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 | 0.001 | 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".