The Addition of Dicamba to POST Applications of Quizalofop-p-ethyl or Clethodim Antagonizes Volunteer Glyphosate-Resistant Corn Control in Dicamba-Resistant Soybean
Bibliographic record
Abstract
Two studies consisting of six field experiments each were conducted at three locations in southwestern Ontario, Canada, in 2014 and 2015 to evaluate the possible antagonism when dicamba was added to quizalofop-p-ethyl or clethodim for the control of volunteer glyphosate-resistant (GR) corn. At 4 wk after application (WAA), quizalofop-p-ethyl at 24, 30, or 36 g ai ha−1provided 88, 94, and 95% control of volunteer GR corn, respectively. The addition of dicamba at 300 or 600 g ae ha−1to quizalofop-p-ethyl (24 g ha−1) reduced the activity of quizalofop-p-ethyl on volunteer GR corn by 12 and 20%. At 4 WAA, clethodim at 30, 37.5, and 45 g ai ha−1provided 85, 91, and 95% control of volunteer GR corn, respectively. The addition of dicamba at 300 or 600 g ha−1to clethodim (30 g ha−1) resulted in antagonism, causing a reduction in volunteer GR corn by 12 and 11%, respectively. In general, there was greater antagonism when the high rate of dicamba was tank-mixed with the lower rate of the graminicide. There was no antagonistic effect on soybean yield by tank-mixing dicamba with either graminicide at all rates evaluated. Based on these results, volunteer GR corn can be controlled effectively by increasing the rate of the graminicide when tankmixed with dicamba.
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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 | 0.001 |
| 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".