Canada fleabane (Conyza canadensis) control with preplant applied residual herbicides followed by 2,4-D choline/glyphosate DMA applied postemergence in corn
Bibliographic record
Abstract
Ford, L., Soltani, N., Robinson, D. E., Nurse, R. E., McFadden, A. and Sikkema, P. H. 2014. Canada fleabane (Conyza canadensis) control with preplant applied residual herbicides followed by 2,4-D choline/glyphosate DMA applied postemergence in corn. Can. J. Plant Sci. 94: 1231-1237. Glyphosate resistant (GR) Canada fleabane (Conyza canadensis) is an extremely problematic weed in no-tillage farming operations. A total of five field trials were conducted over a 2-yr (2012 and 2013) period in Ontario to determine the level of GR Canada fleabane control with a two-pass weed control program of a pre plant (PP) residual herbicide followed by 2,4-D choline/glyphosate dimethylamine (DMA) applied POST. Among residual herbicide treatments evaluated, s-metolachlor (1600 g a.i. ha-1)+flumetsulam (50 g a.i. ha-1)+clopyralid (135 g a.e. ha-1) provided the most consistent (95-99%) control across all sites 8 wk after application (WAA). S-metolachlor/atrazine (1800 g a.i. ha-1) did not provide effective GR Canada fleabane control (21-86%) 8 WAA. The preplant residual herbicides followed by 2,4-D choline/glyphosate DMA (1720 g a.e. ha-1) POST provided 97-100% control. Glyphosate (900 g a.e. ha-1) applied PP followed by 2,4-D choline/glyphosate DMA POST provided 80-93% control 8 WAA. The application of 2,4-D choline/glyphosate DMA POST following any PP residual herbicide resulted in 97% or greater control of GR Canada fleabane. Results from this research demonstrate that residual herbicides applied PP followed by 2,4-D choline/glyphosate DMA POST provides excellent control of GR Canada fleabane, and also incorporates different modes of action thereby limiting the selection of resistant weeds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".