Glyphosate-Resistant Common Ragweed Control in Corn with Postemergence Herbicides
Bibliographic record
Abstract
Four field trials were conducted on a farm infested with glyphosate-resistant (GR) common ragweed during 2016 and 2017 to evaluate various postemergence (POST) herbicides for the control of GR common ragweed in GR corn. Dicamba at 600 g·a.i.·ha-1, dicamba/diflufenzopyr at 200 g·a.i.·ha-1, dicamba/atrazine at 1500 g·a.i.·ha-1, topramezone + atrazine at 12.5 + 500 g·a.i.·ha-1, bromoxynil + atrazine at 280 + 1500 g·a.i.·ha-1, glufosinate at 500 g·a.i.·ha-1 and 2,4-D ester at 560 g·a.i.·ha-1 provided 58% to 85% control at 4 WAA and 49% to 88% control at 8 WAA. Other herbicides evaluated controlled GR common ragweed 9% to 41%. Common ragweed density was reduced 97%, 95%, 95% and 87% and shoot dry weight was reduced 93%, 95%, 94% and 90% with bromoxynil + atrazine, dicamba, glufosinate and topramezone + atrazine applied POST in GR corn, respectively. Results show that dicamba, bromoxynil + atrazine, topramezone + atrazine and glufosinate applied POST are the most efficacious herbicides among the herbicides evaluated for the control of GR common ragweed in GR corn.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".