Sensitivity of Azuki Bean and Control of Multiple Herbicide-Resistant Canada Fleabane With Saflufenacil Herbicide Mixtures
Bibliographic record
Abstract
During 2021 and 2022, four experiments were conducted to ascertain the sensitivity of azuki bean to saflufenacil herbicide mixtures, and five experiments were conducted to determine the control of multiple herbicide-resistant (MHR) Canada fleabane with various saflufenacil herbicide mixtures applied preplant (PP) in soybean at various locations in southwestern Ontario, Canada. At 1, 2, 4, and 8 weeks after emergence (WAE), glyphosate + saflufenacil caused 2-5% azuki bean injury. The addition of metribuzin, bromoxynil, halauxifen-methyl, or 2,4-D ester caused 2-7%, 2-4%, 4-9%, and 2-4% azuki bean injury, respectively. Glyphosate + saflufenacil + bromoxynil plus either metribuzin, halauxifen-methyl, or 2,4-D ester caused 3-7%, 5-11%, and 3-6% azuki bean injury, respectively. Saflufenacil mixtures evaluated had no adverse effect on azuki bean stand, biomass m-1, biomass plant-1, height, seed moisture content, or yield. At 4 and 8 weeks after application (WAA), glyphosate + saflufenacil control MHR Canada fleabane 93 and 87%, respectively; there was no improvement in MHR Canada fleabane control with the glyphosate + saflufenacil mixtures evaluated. At 8 WAA, saflufenacil herbicide mixtures evaluated reduced MHR Canada fleabane density 43-95% and biomass 47-96%; differences were not statistically significant. MHR Canada fleabane interference reduced soybean yield 50%; however, reduced MHR Canada fleabane interference with all glyphosate + saflufenacil mixtures evaluated resulted in soybean yield that was similar to the weed-free control. This study concludes that saflufenacil herbicide mixtures evaluated have the potential to be used for the control MHR Canada fleabane in azuki bean.
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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 | 0.000 | 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".