Simulated mesotrione drift followed by glyphosate, imazethapyr, bentazon or glyphosate plus chlorimuron in soybean
Bibliographic record
Abstract
There have been anecdotal accounts of increased crop sensitivity due to herbicide drift followed by an in-crop herbicide. An experiment was conducted from 2005 to 2007 at Elora, Ridgetown, and Woodstock, Ontario, to determine the effects of simulated mesotrione drift followed by in-crop applications of glyphosate, imazethapyr, bentazon and glyphosate plus chlorimuron on glyphosate-resistant soybean [Glycine max (L.) Merr.] visual injury, plant height, plant density, shoot dry weight, and seed yield. As the rate of simulated mesotrione drift increased, there was an increase in soybean injury and a decrease in shoot dry weight, height, and yield. Simulated mesotrione drift followed by bentazon resulted in synergistic responses in injury shortly after application in some environments. This increase in injury was transient, with no synergistic responses in density, shoot dry weight, and yield. In contrast, antagonistic responses were observed when glyphosate, imazethapyr, or glyphosate plus chlorimuron were applied after simulated mesotrione drift in some environments. Further research is required to develop a better understanding of the interactions of drift followed by the application of an in-crop herbicide. Key words: Bentazon, chlorimuron, glyphosate, imazethapyr, mesotrione, synergism
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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 | 0.000 |
| 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".