Preplant Herbicide Programs for the Control of Multiple-Herbicide-Resistant Waterhemp in No-Till Corn
Bibliographic record
Abstract
Multiple-herbicide-resistant (MHR) waterhemp has been confirmed in 18 Ontario counties. A total of four experiments were conducted in commercial fields with MHR waterhemp in 2020 and 2021 to evaluate preplant (PP) herbicide tank mixtures for control of MHR waterhemp in no-till corn. There was minimal visible corn injury from the herbicide treatments evaluated. At 2 WAA, all herbicide tank mixtures provided greater than 90% control of MHR waterhemp except for pyroxasulfone/carfentrazone + atrazine which controlled MHR waterhemp 85%. At 12 WAA, S-metolachlor/mesotrione/atrazine, S-metolachlor/mesotrione/bicyclopyrone/atrazine, and diflufenican + atrazine + flufenacet controlled MHR waterhemp 86, 91, and 98%, respectively; all other herbicide tank mixtures provided 65 to 83% control. At 8 WAA, S-metolachlor/mesotrione/bicyclopyrone/atrazine and diflufenican + atrazine + flufenacet reduced MHR waterhemp density 97 and 100%, respectively similar to the weed-free control; all other herbicide tank mixtures reduced MHR waterhemp density 64 to 96%. At 8 WAA, S-saflufenacil/dimethenamid-P + mesotrione, S-metolachlor/mesotrione/atrazine, tembotrione + dicamba + flufenacet, metolachlor/mesotrione/bicyclopyrone/atrazine, and diflufenican + atrazine + flufenacet reduced MHR waterhemp biomass 96, 97, 98, 98, and 100%, respectively; all other herbicide tank mixtures reduced MHR waterhemp biomass 72 to 93%. MHR waterhemp interference reduced corn yield 80% in this study. All herbicide tank mixtures resulted in corn yield that was similar to the weed-free control. Among the herbicide tank mixtures evaluated S-metolachlor/mesotrione/bicyclopyrone/atrazine and diflufenican + atrazine + flufenacet provided the greatest control of MHR waterhemp in no-till 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) | 0.000 | 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 | 0.001 | 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".