Glyphosate-resistant horseweed control in glyphosate/glufosinate/2,4-D-resistant soybean with one- and two-pass herbicide programs
Bibliographic record
Abstract
Abstract Glyphosate-resistant (GR) biotypes of horseweed were first confirmed in southern Ontario in 2010 and have spread across southern Ontario. A total of four field experiments were conducted between 2021 and 2022 to determine GR horseweed control with one- and two-pass herbicide programs in glyphosate/glufosinate/2,4-D-resistant (GG2R) soybean. 2,4-D choline/glyphosate DMA, halauxifen-methyl, and saflufenacil applied preplant (PP) controlled GR horseweed by 59%, 72%, and 78% 8 wk after postemergence (POST) application (WAA-POST); there was no improvement of GR horseweed control when 2,4-D choline/glyphosate DMA was added to saflufenacil; in contrast, there was improved GR horseweed control when saflufenacil was added to 2,4-D choline/glyphosate DMA. Glufosinate and 2,4-D choline/glyphosate DMA applied POST controlled glyphosate-resistant horseweed by 71% and 86%, respectively, 8 WAA-POST. Two-pass herbicide programs of a PP followed by POST application provided greater GR horseweed control than a PP or POST herbicide applied alone. Glufosinate or 2,4-D choline/glyphosate DMA applied POST following 2,4-D choline/glyphosate DMA or halauxifen-methyl applied PP improved GR horseweed control by 29% to 38% and 24%, respectively at 8 WAA-POST. The application of 2,4-D choline/glyphosate DMA applied POST following saflufenacil applied PP improved control by 20% 8 WAA-POST; there was no improvement of GR horseweed control when glufosinate was applied POST following saflufenacil applied PP or when either POST herbicide was applied following saflufenacil + 2,4-D choline/glyphosate DMA applied PP. When used in a two-pass program, 2,4-D choline/glyphosate DMA POST provided 2% to 3% greater control of GR horseweed than glufosinate.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".