Sulfentrazone plus a Low Rate of Halosulfuron for Weed Control in White Bean (<i>Phaseolus vulgaris</i> L.)
Bibliographic record
Abstract
Halosulfuron was recently registered as the second soil-applied herbicide for broadleaf weed control in Ontario dry beans, but does not provide an alternative mode of action. Sulfentrazone is used to control broadleaf weeds in soybean and other pulse crops, and its registration for Ontario dry beans would provide a different mode of action for broadleaf weed control. Five field studies were conducted over two years (2014, 2015) to determine if the spectrum of broadleaf weed control is improved by adding a half-rate of halosulfuron to sulfentrazone PRE, and to determine the tolerance of white bean to sulfentrazone (140 or 210 g ai ha-1), s-metolachlor (1050 g ai ha-1), and halosulfuron (17.5 g ai ha-1) applied alone and in combination. Crop injury was assessed at 2 and 4 weeks after crop emergence. Weed control was assessed at 4 and 8 weeks after herbicide application (WAA), and weed density and biomass were determined at 8 WAA. Seed moisture and yield were determined at harvest. Halosulfuron added to sulfentrazone improved the control of Ambrosia artemisiifolia and Sinapis arvensis. Sulfentrazone + s-metolachlor + halosulfuron caused up to 23% crop injury. Therefore, this study concludes that sulfentrazone + s-metolachlor + halosulfuron provides broad spectrum weed control, but is too injurious to white bean for registration in Ontario.
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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".