Herbicide Programs for Control of Glyphosate-Resistant Volunteer Corn in Glufosinate-Resistant Soybean
Bibliographic record
Abstract
Glyphosate-resistant (GR) volunteer corn is a significant problem weed in soybean grown in rotation with corn in the midwestern United States and eastern Canada. The objective of this study was to evaluate the efficacy of glufosinate applied in single or sequential applications compared with acetyl-coenzyme A carboxylase (ACCase) inhibitors applied alone or tank mixed with glufosinate for controlling GR volunteer corn in glufosinate-resistant soybean. At 15 d after early-POST (DAEP), ACCase inhibitors applied alone controlled volunteer corn 76 to 93% compared to 71 to 82% control when tank mixed with glufosinate. The expected volunteer corn control achieved by tank mixing ACCase inhibitors and glufosinate was greater than the glufosinate alone, indicating that glufosinate antagonized ACCase inhibitors at 15 DAEP, but not at later rating dates. ACCase inhibitors applied alone or tank mixed with glufosinate followed by late-POST glufosinate application controlled volunteer corn and green foxtail ≥ 97% at 30 DAEP. Single early-POST application of glufosinate controlled common waterhemp and volunteer corn 53 to 78%, and green foxtail 72 to 93% at 15 DAEP. Single as well as sequential glufosinate applications controlled green foxtail and volunteer corn greater than or equal to 90%, and common waterhemp greater than 85% at 75 d after late-POST (DALP). Contrast analysis suggested that glufosinate applied sequentially provided greater control of volunteer corn at 15 and 75 DALP compared to a single application. Similar results were reflected in volunteer corn density and biomass at 75 DALP. Volunteer corn interference did not affect soybean yield, partly because of extreme weather conditions (hail and high winds) in both years of this study.
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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".