Weed control with postemergence glyphosate tank mixes in glyphosate-resistant soybean
Bibliographic record
Abstract
Walsh, K. D., Soltani, N., Brown, L. R. and Sikkema, P. H. 2014. Weed control with postemergence glyphosate tank mixes in glyphosate-resistant soybean. Can. J. Plant Sci. 94: 1239–1244. Six field trials were conducted over a 3-yr period (2011, 2012 and 2013) in Ontario, Canada, to evaluate various postemergence (POST) glyphosate tank mixes for weed management in glyphosate-resistant (GR) soybean. Herbicide treatments included glyphosate applied alone or mixed with acifluorfen, fomesafen, bentazon and thifensulfuron-methyl. Glyphosate tank mixtures with acifluorfen, fomesafen, bentazon and thifensulfuron-methyl caused GR soybean injury of up to 21, 11, 4 and 14% at 7 d after treatment (DAT), which was reduced to 5, 0, 0 and 2% by 28 DAT, respectively. Velvetleaf, green pigweed, common ragweed and common lambsquarters control ranged from 55 to 95, 93 to 100, 70 to 92 and 81 to 98% at 28 DAT respectively. Relative to glyphosate alone, tank mixtures with thifensulfuron-methyl provided equivalent to increased weed control, while acifluorfen, fomesafen and bentazon provided equivalent to reduced weed control. All herbicide tank mixtures resulted in higher yields (3.8–4.0 t ha −1 ) than the untreated check (2.7 t ha −1 ), and were generally equivalent to glyphosate alone (4.1 t ha −1 ). Results from this study indicate that the glyphosate tank mixtures evaluated did not provide a benefit over glyphosate alone.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".