Herbicide tank mixtures to control co-existing glyphosate-resistant Canada fleabane and giant ragweed in soybean
Bibliographic record
Abstract
Populations of glyphosate-resistant (GR) Canada fleabane and GR giant ragweed can be found in several locations in southwestern Ontario. While these species can be managed individually, a scenario has developed where both species are present in GR soybean. Ten separate field experiments (five with Canada fleabane and five with giant ragweed) were conducted over a 2-yr period (2013–2014) in soybean to evaluate preplant (PP) herbicide tank mixtures that could control both weed species if they were present in the same field. Herbicides were rated for soybean injury, weed control, population density, and aboveground biomass. Two- and three-way tank mixtures containing amitrole (i.e., glyphosate + amitrole, glyphosate + amitrole + saflufenacil, and glyphosate + amitrole + 2,4-D) were among the most effective treatments. For example, control of GR Canada fleabane and GR giant ragweed was at least 92% at 4 wk after treatment (WAT) and weed density and biomass were generally similar to the weed-free control. However, without amitrole, the best PP herbicide option was a three-way tank mixture of glyphosate + saflufenacil + 2,4-D which provided improved control and greater reductions in density and biomass compared with the two-way glyphosate tank mixtures containing saflufenacil or 2,4-D.
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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 | 0.001 | 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".