Multiple herbicide-resistant horseweed (<i>Conyza canadensis</i>) dose response to tolpyralate and tolpyralate plus atrazine and comparison to industry standard herbicides in corn
Bibliographic record
Abstract
Abstract Horseweed biotypes resistant to glyphosate and ALS-inhibiting herbicides are becoming more prevalent in Canada and the United States and present a significant management challenge in field crops. Tolpyralate is a recently commercialized herbicide for use in corn that inhibits 4-hydroxyphenylpyruvate dioxygenase (HPPD), and there is little information regarding its efficacy on horseweed. Six field experiments were conducted in 2017 and 2018 at four locations in Ontario, Canada, to determine the biologically effective dose of tolpyralate and tolpyralate + atrazine and to compare label rates of tolpyralate and tolpyralate + atrazine to currently accepted herbicide standards for POST control of glyphosate and cloransulam-methyl resistant (MR) horseweed. At 8 wk after application (WAA), tolpyralate at 4.8 and 22.6 g ha–1provided 50% and 80% control, respectively. When applied with atrazine at a 1:33.3 tank-mix ratio, 22.3 + 741.7 g ha–1provided 95% control of MR horseweed. The addition of atrazine to tolpyralate at label rates improved control of MR horseweed to 98%, which was similar to the control provided by dicamba:atrazine and bromoxynil + atrazine. The results of this study indicate that tolpyralate + atrazine provides excellent control of MR horseweed POST in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".