Control of Glyphosate Resistant Canada Fleabane (Conyza canadensis (L.) Cronquist) with 2,4-D Choline/Glyphosate DMA in Corn (Zea mays L.)
Bibliographic record
Abstract
Glyphosate resistant Canada fleabane (GRCF) exists in Ontario due to the repeated use of glyphosate on Roundup Ready crops. GR weeds must now be managed using an integrated approach including herbicides like, 2,4-D choline/glyphosate DMA, a premixed herbicide solution. The objective of this research was to determine the ideal application timing of 2,4-D choline/glyphosate DMA herbicide for the control of GRCF. Single applications of 2,4-D choline/glyphosate DMA (1720 g ae ha-1) provided 71-93% control, while sequential applications provided 98-100% control of GRCF 8 WAA. S-metolachlor (1600 g ai ha-1) + flumetsulam (50 g ai ha-1) + clopyralid (135 g ae ha-1) applied preemergence provided the best consistent control of GRCF (95-99%) of the preplant corn residual herbicides evaluated. 2,4-D choline/glyphosate DMA applied post-emergence provided 97-100% control of GRCF following any preplant corn residual herbicide. The size of the GRCF (10, 20 and 30 cm tall) at the time of the 2,4-D choline/glyphosate DMA application did not affect the efficacy of the herbicide. The 2,4-D choline/glyphosate DMA formulation and a tank mix of 2,4-D amine and glyphosate DMA provide equivalent control of GRCF.
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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".