Weed control, environmental impact and profitability with glyphosate tank mixes in glyphosate-tolerant corn
Bibliographic record
Abstract
Eight field trials were conducted over a 3-yr period at Exeter (one site in 2005 and 2006) and Ridgetown (three sites in 2005, two sites in 2006 and one site in 2007), Ontario, to evaluate different postemergence glyphosate tank mixes for weed management in glyphosate-tolerant corn. Treatments included a weedy check, a single application of glyphosate, a sequential application of glyphosate and tank mixes of glyphosate plus either atrazine, dicamba/atrazine, mesotrione, s-metolachlor plus atrazine, s-metolachlor/atrazine, dicamba/diflufenzopyr, and dimethenamid plus atrazine. A single application of glyphosate, the sequential application of glyphosate and tank mixes of glyphosate plus either atrazine, dicamba/atrazine, mesotrione, s-metolachlor plus atrazine, s-metolachlor/atrazine, dicamba/diflufenzopyr, and dimethenamid plus atrazine provided 92-100% control of redroot pigweed, 87-100% control of common ragweed, 74-100% control of common lambsquarters, and 43-100% control of green foxtail. All herbicide treatments had a higher yield than the weedy check. There were no differences in corn yield among the herbicide treatments evaluated. The glyphosate alone or in tank mix combination with mesotrione or dicamba/diflufenzopyr programs had the lowest environmental impact. Glyphosate plus atrazine and dicamba/diflufenzopyr were the most profitable weed management programs in glyphosate-tolerant corn.Key words: Atrazine, dicamba, diflufenzopyr, dimethenamid, glyphosate, mesotrione, pendimethalin, rimsulfuron, s-metolachlor, Zea mays L.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".