Control of multiple-resistant waterhemp [<i>Amaranthus tuberculatus</i> (Moq.) Sauer] with preemergence and postemergence herbicides in corn in Ontario
Bibliographic record
Abstract
Waterhemp is a competitive, summer annual, broadleaf weed that poses a considerable threat to Ontario grain farmers. Populations with multiple resistance to Group 2 (ALS-inhibitors), Group 5 (photosystem II inhibitors), and Group 9 (EPSPS inhibitors) herbicides have been confirmed in Ontario. If left uncontrolled, waterhemp competition can result in corn yield losses of up to 74%. The objective of this research was to evaluate preemergence (PRE) and postemergence (POST) herbicides for control of multiple-herbicide resistant (MR) waterhemp. Two field studies at two locations (Cottam and Walpole Island) were conducted in 2016 and 2017. Fifteen PRE and 12 POST herbicides were evaluated for waterhemp control, density, and aboveground biomass and corn yield. At 8 wk after application (WAA), S-metolachlor/mesotrione/atrazine (1393/139/524 g a.i. ha −1 ) and S-metolachlor/mesotrione/bicyclopyrone/atrazine (1259/140/35/588 g a.i. ha −1 ) applied PRE were the most efficacious, controlling MR waterhemp 87% and 91%, respectively. At 8 WAA, the most efficacious POST herbicides were mesotrione + atrazine and dicamba/atrazine, controlling MR waterhemp 92% and 87%, respectively. Reduced waterhemp interference with the PRE herbicides evaluated resulted in corn yield that was similar to the weed-free control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".