Efficacy of 2,4-D Choline/Glyphosate Dimethylamine on Glyphosate Resistant Canada Fleabane (<i>Conyza canadensis</i>) at Different Sizes
Bibliographic record
Abstract
Glyphosate resistant (GR) Canada fleabane has spread quickly across southwestern Ontario and new strategies for the control of this competitive weed must be developed especially in no-tillage crops. A premix of 2,4-D choline and glyphosate dimethylamine (DMA) has been developed for application on tolerant corn, soybean and cotton crops that provides an option for the control of this problematic GR weed. The objective of this research was to determine the required dose needed to effectively control GR Canada fleabane at different size categories in field and greenhouse experiments. In the field experiments, nine rates of 2,4-D choline/glyphosate DMA (53.8 to 13,760 g·ae·ha-1) were applied to GR Canada fleabane that were 10 cm in diameter/tall, 20 cm tall or 30 cm tall. Similarly, in the greenhouse, seven rates of 2,4-D choline/glyphosate DMA (0 to 3440 g·ae·ha-1) were applied to 10, 20 and 30 cm tall GR Canada fleabane plants. The three different size classes of GR Canada fleabane responded similarly to 2,4-D choline/glyphosate DMA in the field experiment. In the greenhouse there were some differences in control for the three size classes of GR Canada fleabane with 2,4-D choline/glyphosate DMA; the 20 and 30 cm tall plants required similar rates to provide equivalent control, but the 10 cm plants required a lower rate. In all situations, greater than 1720 g·ae·ha-1 of 2,4-D choline/glyphosate DMA was required to provide 95% control of 10, 20 and 30 cm tall Canada fleabane in greenhouse (35 DAA) and field experiments (8 WAA), respectively.
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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 | 0.001 | 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".