Tolerance of Azuki and White Bean to Tiafenacil Tank Mixes
Bibliographic record
Abstract
Few studies have investigated the tolerance of azuki and white bean to preplant (PP) applications of tiafenacil herbicide mixtures in Ontario. Four field experiments were conducted in southwestern Ontario, Canada, to assess the tolerance of azuki and white bean to PP applications of tiafenacil and tiafenacil herbicide mixtures at 1X and 2X rates. In azuki bean, tiafenacil at the 1X and 2X rate cause 0.5 and 0.4% injury at 4 weeks after bean emergence (WAE), respectively, mixtures of tiafenacil with halauxifen-methyl plus bromoxynil at the 1X and 2X rates caused 1.3 and 4.6% injury, respectively; significantly greater compared to tiafenacil applied alone. Other tiafenacil herbicide mixtures evaluated caused similar azuki bean injury to tiafenacil applied alone. In white bean, the combinations of tiafenacil with metribuzin, 2,4-D ester, and bromoxynil + 2,4-D ester at the 2X rate caused 1.4-1.6% more visible injury than tiafenacil applied alone at the same rate; however, other herbicide mixtures caused similar injury levels to tiafenacil applied alone. None of the tiafenacil herbicide treatments evaluated reduced bean stand at 3 WAE. Tiafenacil + 2,4-D ester reduced bean biomass plant-1 and m-1 by 18% relative to the non-treated control at 3 WAE. At 6 WAE, none of the tiafenacil treatments evaluated affected plant height, and at harvest, none of the tiafenacil herbicide treatments influenced seed moisture content or bean yield relative to the non-treated control. These results conclude that, although certain tiafenacil mixtures may cause minor and transient injury in azuki and white bean, they do not substantially affect growth, maturity, or yield, making them suitable options for pp weed control in both azuki and white bean production.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".