Control of glyphosate-resistant giant ragweed (<i>Ambrosia trifida</i> L.) with premix of iodosulfuron/thiencarbazone applied alone or in tank mixtures in no-till corn (<i>Zea mays</i> L.)
Bibliographic record
Abstract
The objectives of this study were to evaluate the efficacy of a new premix of iodosulfuron (6%)/thiencarbazone (45%) applied alone or tank-mixed with 2,4-D, dicamba, glyphosate, or metribuzin in the fall and (or) early spring followed by preemergence (PRE) and postemergence (POST) herbicide applications for control of glyphosate-resistant giant ragweed and their effect on corn yield. Field experiments were conducted in no-till corn fields infested with glyphosate-resistant giant ragweed (20–30 plants m−2) near Clay Center and McCool Junction, NE, in 2013 and 2014, respectively. A premix of iodosulfuron/thiencarbazone applied alone or in split applications in the fall and early spring controlled glyphosate-resistant giant ragweed <60% and resulted in a density of 14 giant ragweed plants m−2, which was comparable to the untreated control at 28 d after early spring treatment (DAEST). Metribuzin or 2,4-D applied alone resulted in <75% giant ragweed control at 28 DAEST; however, 2,4-D or dicamba tank-mixed with iodosulfuron/thiencarbazone provided ≥92% control. Treatments including 2,4-D or dicamba led to 85%–98% reduction in giant ragweed biomass at 28 DAEST. A follow-up application of a premix of isoxaflutole/thiencarbazone tank-mixed with atrazine applied PRE was not effective, although a POST application of tembotrione + atrazine resulted in >91% control at 28 d after postemergence treatment. The premix applied alone did not provide effective control of giant ragweed in corn primarily because lack of residual activity.
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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".