The Addition of Dicamba to POST Applications of Quizalofop-p-ethyl or Clethodim Antagonizes Volunteer Glyphosate-Resistant Corn Control in Dicamba-Resistant Soybean
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 −1 provided 88, 94, and 95% control of volunteer GR corn, respectively. The addition of dicamba at 300 or 600 g ae ha −1 to 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 −1 provided 85, 91, and 95% control of volunteer GR corn, respectively. The addition of dicamba at 300 or 600 g ha −1 to 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it