Control of glyphosate-resistant horseweed (<i>Conyza canadensis</i>) with tiafenacil mixes in corn
Bibliographic record
Abstract
Abstract Four field experiments were completed in commercial corn fields during 2019 and 2020 to determine glyphosate-resistant (GR) horseweed control in corn with tiafenacil alone or in combination with bromoxynil, dicamba, or tolpyralate applied preplant (PP). Corn planted 1 to 10 d after herbicide application was not injured with any of the herbicides tested. GR horseweed interference reduced corn grain yield 32% when left uncontrolled. Herbicides reduced GR horseweed interference and resulted in corn grain yield that was similar to the weed-free control. Glyphosate (900 g ae ha −1 ) + tiafenacil at 12.5, 25, and 37.5 g ha −1 controlled GR horseweed 63%, 68%, and 72% at 4 wk after treatment (WAT) and decreased GR horseweed density 64%, 43%, and 83% and dry biomass 69%, 55%, and 83%, respectively. Glyphosate + tiafenacil at 12.5, 25, and 37.5 g ha −1 plus bromoxynil (280 g ai ha −1 ) controlled GR horseweed 81%, 88%, and 87% at 4 WAT and reduced GR horseweed density 82%, 94%, and 93% and dry biomass 93%, 93%, and 98%, respectively. Glyphosate + tiafenacil at 12.5, 25, and 37.5 g ha −1 plus dicamba (300 g ai ha −1 ) controlled GR horseweed 86%, 88%, and 88% at 4 WAT and decreased GR horseweed density 76%, 89%, and 86% and dry biomass 94%, 98%, and 98%, respectively. Glyphosate + tiafenacil at 12.5, 25, and 37.5 g ha −1 plus tolpyralate (30 g ai ha −1 ) controlled GR horseweed 90%, 90%, and 91% at 4 WAT and decreased GR horseweed density 93%, 91%, and 95% and dry biomass 98%, 97%, and 97%, respectively. The industry standards in Ontario, glyphosate + dicamba/atrazine and glyphosate + saflufenacil/dimethenamid-p controlled GR horseweed 95% and 100% at 4, 8, and 12 WAT and caused 99% and 100% density or biomass reduction, respectively.
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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".