Optimization of Tolpyralate for Weed Control in Corn (Zea mays L.)
Bibliographic record
Abstract
Thirty field experiments were conducted from 2019-2021 at six locations in Ontario, Canada to optimize the efficacy of tolpyralate for weed control in corn. Two studies investigated the interaction between 4-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibitors (tolpyralate, mesotrione, and topramezone) and reactive oxygen species (ROS)-generators (atrazine, bromoxynil, bentazon, and glufosinate) on annual weed species and one study focused on the interaction on glyphosate-resistant Canada fleabane. Co-application of HPPD-inhibitors and ROS-generators were generally synergistic or additive for weed control, but the response depended on the herbicide, rate of herbicide, weed species, and response parameter. In contrast to mesotrione, tolpyralate was antagonistic with glufosinate for common ragweed, Setaria spp., and barnyardgrass control at 8 weeks after application. The effective dose of atrazine to complement tolpyralate was established for seven annual weed species. Three separate studies identified that MSO Concentrate®, Merge®, or Carrier® adjuvants should be added to tolpyralate, tolpyralate + atrazine, or tolpyralate + bromoxynil.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".