Comparison of Glyphosate Formulations for Weed Control and Tolerance in Maize (<i>Zea mays</i> L.) and Soybean [<i>Glycine max</i> (L.) Merr.]
Bibliographic record
Abstract
Twenty-two field experiments (six maize (Zea mays L.) and five soybean [Glycine max (L.) Merr.] using low glyphosate doses to assess weed control and six maize and five soybean using high glyphosate doses to assess tolerance) were conducted from 2010 to 2012 at two locations in southern Ontario, Canada to compare the commercially available glyphosate formulations of Roundup Weather MAX?, Clearout?41 Plus, and Wise Up? (WeatherMAX, Clearout, and WiseUp, respectively). Inmaize and soybean, control of velvetleaf, pigweed species, common lambs quarters, and green foxtail 4 weeks after treatment (WAT) using 900 g·ae·ha-1 ranged from at least 85% to 99%, regardless of formulation. By 8 WAT with 900 g·ae·ha-1, control of these weeds generally declined, but still ranged from 82% to 97% across all formulations. At harvest, maize yields were similar to the weed-free control for 900 g·ae·ha-1 of glyphosate as WeatherMAX and Clearout; however, reduced weed control with WiseUp resulted in an 8.8% yield loss. For soybean, yields were similar to the weed-free control, regardless of formulation or dose. In the tolerance experiments, 2.1% and 2.8% injury was observed 4 WAT for maize treated with 3600 g·ae·ha-1 of glyphosate as WeatherMAX and WiseUp, respectively. However, maize yields were unaffected by glyphosate formulation or dose. In soybean, visible injury of 8.5%, 4.5%, and 3.7% was observed 1 WAT with 5400 g·ae·ha-1 of glyphosate as WeatherMAX, WiseUp, and Clearout, respectively; by 8 WAT, visible injury was similar to the untreated control, regardless of formulation or dose. The early injury from 5400 g·ae·ha-1 of glyphosate resulted in 8.5%, 4.6%, and 5.5% yield loss for the WeatherMAX, WiseUp, and Clearout formulations, respectively.
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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".