Weed Management in Conventional- and No-Till Soybean Using Flumioxazin/Pyroxasulfone
Bibliographic record
Abstract
Eleven field experiments were conducted over a 3-yr period (2010, 2011, and 2012) in conventional- and no-till soybean with a flumioxazin and pyroxasulfone premix. PRE and preplant applications were evaluated for soybean injury, weed control, and yield compared to standard herbicides. Early-season soybean injury from flumioxazin/pyroxasulfone ranged from 1 to 19%; however, by harvest, soybean yields were similar across labeled rates (160 and 200 g ai ha−1), standard treatments, and the nontreated control. Flumioxazin/pyroxasulfone provided excellent control (99 to 100%) of velvetleaf, pigweed species (redroot pigweed and smooth pigweed), and common lambsquarters across almost all rates tested (80 to 480 g ai ha−1). Common ragweed, green foxtail, and giant foxtail control increased with flumioxazin/pyroxasulfone rate. The biologically effective rates varied between tillage systems. The flumioxazin/pyroxasulfone rate required to provide 80% control (R80) of pigweed was 3 and 273 g ai ha−1under conventional- and no-till, respectively. For common ragweed, the R80was 158 g ai ha−1under conventional tillage; yet, under no-till, the rate was nonestimable. The results indicate that flumioxazin/pyroxasulfone can provide effective weed control as a setup for subsequent herbicide applications.
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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 | 0.001 | 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 | 0.000 |
| 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".