Weed control in corn with tolpyralate and atrazine plus grass herbicides
Bibliographic record
Abstract
Abstract Six field experiments were established in southwestern Ontario in 2021 and 2022 to evaluate whether the addition of a grass herbicide (acetochlor, dimethenamid-p, flufenacet, pendimethalin, pyroxasulfone, or S -metolachlor) to tolpyralate + atrazine improves late-season weed control in corn. Tolpyralate + atrazine caused 12% and 5% corn injury at 1 and 4 wk after herbicide application (WAA); corn injury was not increased with the addition of a grass herbicide. Weed interference reduced corn yield 60%. The addition of a grass herbicide to tolpyralate + atrazine did not enhance velvetleaf control. The addition of acetochlor or dimethenamid-p to tolpyralate + atrazine enhanced pigweed species control 4% 4 WAA; the addition of other grass herbicides tested did not increase pigweed species control. The addition of acetochlor enhanced common ragweed control 5% at 4 WAA, and the addition of acetochlor or dimethenamid-p enhanced common ragweed control 8% at 8 WAA; the addition of other grass herbicides did not improve common ragweed control. The addition of acetochlor to tolpyralate + atrazine enhanced common lambsquarters control up to 4%; there was no enhancement in common lambsquarters control with the addition of the other grass herbicides. Tolpyralate + atrazine controlled barnyardgrass 90% and 78% at 4 and 8 WAA, respectively; the addition of a grass herbicide enhanced barnyardgrass control 9% to 10% and 21% at 4 and 8 WAA, respectively. Tolpyralate + atrazine controlled green or giant foxtail 80% and 69% at 4 and 8 WAA, respectively; the addition of a grass herbicide enhanced foxtail species control 15% to 19% and 24% to 29% at 4 and 8 WAA, respectively. This research shows that adding a grass herbicide to tolpyralate + atrazine mixture can improve weed control efficacy, especially increased annual grass control in corn production.
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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 | 0.000 | 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".