Control of Canada fleabane (<i>Conyza canadensis</i>) with glyphosate DMA/2,4-D choline applications in corn (<i>Zea mays</i>)
Bibliographic record
Abstract
Glyphosate resistant (GR) Canada fleabane (horseweed) has quickly spread across southwestern Ontario and is a difficult weed to control in GR crops. Glyphosate dimethylamine (DMA)/2,4-D choline (Enlist Duo?TM Dow AgroSciences LLC), a new herbicide premix developed by Dow Agro Sciences, provides control of GR and other problematic weeds. The objective of this study was to compare single and sequential applications of glyphosate DMA/2,4-D choline for the control of GR Canada fleabane in GR corn. Three single applications of glyphosate (DMA)/2,4-D choline (1720 g·ae·ha-1) were evaluated: 1) preplant (PP) applied to Canada fleabane up to 10 cm diameter/height, 2) postplant 1 (POST 1) applied when Canada fleabane was up to 20 cm tall and 3) postplant 2 (POST 2) applied up to 30 cm tall Canada fleabane. Four sequential applications were also examined:1) PP followed by (fb) POST 1, 2) PP fb POST 2, 3) POST 1 fb POST 2 and 4) PP fb POST 1 fb POST 2. The single applications provided 69%-86% control of the GR Canada fleabane while the sequential applications increased control to 92%-100%. Three applications did not provide an increase in control over a sequential two-pass application at 8 weeks after the application (WAA). Results from this research indicate that a sequential 2-pass application of glyphosate DMA/2,4-D choline provided acceptable control of GR Canada fleabane in corn.
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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 | 0.001 |
| 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".