Broadleaf Weed Control with Halosulfuron Tankmixes in White Bean
Bibliographic record
Abstract
Six field trials were conducted over a four-year (2017-2020) period near Exeter and Ridgetown, Ontario to determine the efficacy of halosulfuron tankmixes applied postemergence to control broadleaf weeds in white bean. Halosulfuron caused up to 4% injury in white bean at 2 and 4 weeks after treatment (WAT). Bentazon, acifluorfen, fomesafen, bentazon/acifluorfen, and bentazon + fomesafen caused 2% - 16% injury at 2 WAT and up to 3% injury at 4 WAT in white bean. The addition of halosulfuron to the aforementioned herbicides did not accentuate white bean injury. Reduced weed interference with the herbicides evaluated increased white been yield 50% - 90% compared to the weedy control; there was no difference in seed yield among herbicide treatments evaluated. At 4 WAT, halosulfuron at 25, 37.5 and 50 g ai ha-1 controlled velvetleaf 86%, 93% and 97%; redroot pigweed 83%, 85% and 89%; common ragweed 90%, 93% and 94%; common lambsquarters 27%, 28% and 36%; flower-of-an-hour 66%, 76% and 69%; and wild mustard 100%, 100% and 100%, respectively. Bentazon, acifluorfen, fomesafen, bentazon/acifluorfen, and bentazon + fomesafen controlled velvetleaf 73%, 14%, 52%, 42% and 68%; redroot pigweed 40%, 91%, 85%, 75% and 80%; common ragweed 36%, 81%, 92%, 68% and 84%; common lambsquarters 87%, 39%, 48%, 60% and 76%; flower-of-an-hour 90%, 66%, 63%, 73% and 83%; and wild mustard 97%, 97%, 100%, 99% and 100%, respectively. Halosulfuron tankmixed with bentazon, acifluorfen, fomesafen, bentazon/acifluorfen or bentazon + fomesafen controlled velvetleaf 90%, 51%, 68%, 75% and 90%; redroot pigweed 80%, 99%, 95%, 92% and 91%; common ragweed up to 94%, 97%, 93%, 94% and 95%; common lambsquarters 74%, 62%, 43%, 62% and 66%; flower-of-an-hour 92%, 78%, 74%, 82% and 87%; and wild mustard 100%, 100%, 100%, 100% and 100%, respectively. Weed density and dry biomass followed the same trend. This study concludes that the optimal halosulfuron tankmix is broadleaf weed species specific for weed management in dry 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) | 0.000 | 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 | 0.000 |
| 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".