Glyphosate-Resistant Canada Fleabane Control with Three-Way Herbicide Tankmixes in Soybean
Bibliographic record
Abstract
Eight field trials (2 in 2016, 3 in 2017, 3 in 2018) were conducted in farmers’ fields with heavy infestations of GR Conyza canadensis (Canada fleabane, horseweed or marestail) to evaluate glyphosate (900 g ae ha-1) plus saflufenacil (25 g ai ha-1), 2,4-D ester (500 g ai ha-1) or paraquat (1100 g ai ha-1) applied preplant (PP) as 2-way tankmixes, or in 3-way tankmixes with sulfentrazone (140 g ai ha-1), flumioxazin (107 g ai ha-1) or metribuzin (400 g ai ha-1) for the glyphosate-resistant (GR) C. canadensis control in GR soybean. Glyphosate plus saflufenacil applied PP controlled GR C. canadensis as much as 90%. The addition of sulfentrazone, flumioxazin or metribuzin to the tankmix provided as much as 93%, 96% and 97% control of GR C. canadensis, respectively. Glyphosate plus 2,4-D ester applied PP provided as much as 59% control of GR C. canadensis. The addition of sulfentrazone, flumioxazin or metribuzin to the tankmix provided as much as 60%, 59% and 91% control of GR C. canadensis, respectively. Glyphosate plus paraquat applied PP provided as much as 85% control of GR C. canadensis. The addition of sulfentrazone, flumioxazin or metribuzin to the tankmix provided as much as 88%, 89% and 98% control of GR C. canadensis, respectively. Density and biomass reductions of GR C. canadensis with herbicides evaluated followed the same pattern as weed control evaluations. GR C. canadensis interference reduced soybean yield 66%. Reduced GR C. canadensis interference with the preplant herbicides evaluated provided soybean yield similar to the weed-free control. Results from this study show that glyphosate plus saflufenacil, glyphosate plus 2,4-D ester or glyphosate plus paraquat tankmixed with metribuzin can provide effective control of GR C. canadensis in GR soybean.
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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) | 0.001 | 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".