Glyphosate-Resistant Giant Ragweed (<i>Ambrosia trifida</i> L.): 2,4-D Dose Response and Control with Postemergence Herbicides in Soybean
Bibliographic record
Abstract
Glyphosate resistant giant ragweed is an increasing problem in glyphosate resistant cropping systems in southwestern Ontario. The postemergence herbicides registered for use in soybean in Ontario do not provide consistent control of glyphosate resistant giant ragweed. There is limited research on the lowest effective rate of 2,4-D for the control of glyphosate resistant giant ragweed. Consequently, the objectives of this study were a) to determine the efficacy of herbicides applied postemergence for the control of glyphosate resistant giant ragweed in glyphosate resistant soybean, and b) to determine the lowest effective rate of 2,4-D for the control of glyphosate-resistant giant ragweed. Ten postemergence herbicide combinations and seven rates of 2,4-D were evaluated in field studies conducted in 2011 and 2012 at six locations confirmed with glyphosate-resistant giant ragweed. The post emergence herbicides evaluated did not provide acceptable/consistent control. Of the herbicides evaluated, glyphosate plus cloransulam-methyl provided 26% to 70% control 8 WAA of glyphosate resistant giant ragweed, which was the best of the herbicides combinations evaluated. The doses of 2,4-D required to reduce giant ragweed shoot dry weight by 50, 80 and 95% were 142, 310 and 1048 g a.e. ha-1, 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 | 0.000 | 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".