Weed control in conventional soybean with pendimethalin followed by imazethapyr plus imazamox/quizalofop
Bibliographic record
Abstract
The objectives of this study were to evaluate the efficacy of pendimethalin applied pre-emergence (PRE) followed by post-emergence (POST) application of imazethapyr + imazamox/quizalofop-p-ethyl for weed control and their effect on conventional soybean injury, yield attributes, and yield. Field experiments were conducted in 2013 and 2014 in conventional soybean. Herbicide treatments provided ≥90%, 70%, and 85% control of crowfoot grass, large crabgrass, and goosegrass, respectively, and ≤80% control of false amaranth and horse purslane at 30 d after sowing (DAS). At 60 DAS, pendimethalin applied alone or followed by hand-hoeing/quizalofop-p-ethyl/imazethapyr + imazamox provided 100% control of goosegrass and 65%–100% control of crowfoot grass/large crabgrass. Pendimethalin followed by imazethapyr + imazamox/quizalofop-p-ethyl as well as quizalofop-p-ethyl applied alone resulted in complete control of crowfoot grass, large crabgrass, and goosegrass, but control of broadleaf weeds was variable. Pendimethalin followed by imazethapyr + imazamox at 70 g ha−1 at 28 DAS, imazethapyr + imazamox at 60 or 70 g ha−1 at 21 DAS followed by quizalofop-p-ethyl at 37.5 g ha−1 at 42 DAS resulted in soybean branch numbers per plant, number of pods per plant, and soybean seed yield comparable to weed-free control. Control of Benghal dayflower and purple nutsedge was not acceptable.
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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".