Yellow nutsedge (<i>Cyperus esculentus</i> L.) control in corn with various rates of halosulfuron
Bibliographic record
Abstract
There are a limited number of herbicide options that provide commercially acceptable control of yellow nutsedge in corn. A study consisting of nine field experiments was conducted during 2013–2016 in growers’ fields in Ontario to evaluate the efficacy of glyphosate plus various rates of halosulfuron applied after emergence (postemergence, POST) for the control of yellow nutsedge in glyphosate-resistant corn. Glyphosate (900 g a.e. ha−1) plus halosulfuron applied POST at the registered rate of 34–68 g a.i. ha−1 caused minimal injury in glyphosate-resistant corn. The predicted halosulfuron rates needed to control yellow nutsedge 50%, 80%, and 90% were 3, 24, and >140 g a.i. ha−1 at 4 weeks after herbicide application (WAA) and 2, 13, and 73 g a.i. ha−1 at 8 WAA, respectively. The predicted halosulfuron rates required to reduce yellow nutsedge density 50%, 80%, and 90% were 13, 42, and 109 g a.i. ha−1, respectively. In addition, the predicted halosulfuron rates required to reduce yellow nutsedge dry weight 50%, 80%, and 90% were 6, 23, and 54 g a.i. ha−1, respectively. Contrasts comparing halosulfuron (35 g a.i. ha−1) with other herbicides showed that glyphosate plus halosulfuron provided as much as 35% greater control of yellow nutsedge than glyphosate plus bentazon (1080 g a.i. ha−1) and as much as 22% greater control of yellow nutsedge than glyphosate plus tembotrione/thiencarbazone (45 g a.i. ha−1).
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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".