Weed Control in Corn and Soybean with Group 15 (VLCFA Inhibitor) Herbicides Applied Preemergence
Bibliographic record
Abstract
Limited information exists on the efficacy of pethoxamid for annual grass and broadleaf control in corn and soybean in Ontario. A total of 10 field experiments (5 with corn and 5 with soybean) were conducted during 2015 to 2017 in Ontario, Canada, to compare the weed control efficacy of dimethenamid-P at 544 g·ai·ha−1, pethoxamid at 840 g·ai·ha−1, pyroxasulfone at 100 g·ai·ha−1, and S-metolachlor at 1050 g·ai·ha−1 applied preemergence (PRE). Reduced weed interference with pyroxasulfone and dimethenamid-P resulted in corn yield that was similar to the weed-free control; however, weed interference with pethoxamid and S-metolachlor reduced corn yield 28 and 33%, respectively. Reduced weed interference with pyroxasulfone resulted in soybean yield that was similar to the weed-free control; however, weed interference with pethoxamid, dimethenamid-P, and S-metolachlor reduced soybean yield 27, 27, and 30%, respectively. At 4 and 8 weeks after application (WAA), all VLCFA inhibitor herbicides (Group 15) provided excellent redroot pigweed control (90 to 99%) in corn. There were no differences in common ragweed control, density, and dry weight among the VLCFA inhibitor herbicide evaluated; pyroxasulfone provided highest numeric common ragweed control and lowest numeric density and dry weight. At 4 and 8 WAA, pyroxasulfone provided the best common lambsquarters and wild mustard control and lowest numeric density and dry weight in corn and soybean. At 8 WAA, the VLCFA inhibitor herbicides controlled green foxtail 91 to 96% in corn; dimethenamid-P provided better control of green foxtail than pethoxamid in soybean. There were no differences in barnyard grass control among the VLCFA inhibitor herbicides evaluated.
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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".