Control of volunteer adzuki bean (<i>Vigna angularis</i>) with pre- and postemergence herbicides in corn (<i>Zea mays</i>)
Bibliographic record
Abstract
Volunteer adzuki bean within a corn crop can decrease corn yield and increase the adzuki bean seed bank. The objective of this research was to evaluate the efficacy of various preemergence (PRE) and postemergence (POST) herbicides for the control of volunteer adzuki bean [Vigna angularis (Willd.) Ohwi & Ohashi] in corn. Trials were conducted at two locations in 2005, 2006, 2007 and 2009. Experiments were arranged in a randomized complete block design with either seven PRE or ten POST herbicides. Volunteer adzuki bean interference resulted in corn yield losses of 16 to 43%. The PRE herbicides provided variable control. The highest and most consistent control PRE was provided by dicamba/atrazine, which provided 69 to 97% control of volunteer adzuki bean at 4 and 8 wk after emergence (WAE), respectively. Dicamba/atrazine PRE reduced volunteer adzuki bean density and biomass 67 to 97% 10 WAE. Generally, the POST herbicides evaluated provided better and more consistent control of volunteer adzuki bean than the PRE herbicides. Dicamba/atrazine applied POST provided greater than 95% control of volunteer adzuki bean and consistently reduced volunteer adzuki bean density and biomass to the equivalent of the weed-free control. Dicamba/atrazine applied POST resulted in corn yields equivalent to the weed-free control. The best option for controlling volunteer adzuki bean in corn is to apply dicamba/atrazine POST, while the remaining herbicides evaluated provided variable control.
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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) | 0.000 | 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 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".