Safening effect of bentazon on cloransulam-methyl and halosulfuron-methyl in dry bean
Bibliographic record
Abstract
Bentazon, applied as a tankmix, has been shown to have the potential for reducing the injury from some POST herbicides. Field experiments were conducted in 2008 and 2009 at Exeter, ON and in 2009 at Ridgetown, ON to determine if the addition of bentazon reduces the injury from cloransulam-methyl or halosulfuron-methyl applied POST in black, cranberry, kidney and white beans. Bentazon added to cloransulam-methyl reduced the level of injury 0 to 6% at 17.5 g·ai·ha–1 and 0 to 9% at 35 g·ai·ha–1 in dry bean. Bentazon added to halosulfuron-methyl reduced the level of injury as much as 4% at 35 g·ai·ha–1 and 6% at the 70 g·ai·ha–1. Bentazon added to cloransulam-methyl increased plant height as much as 3 cm. The addition of bentazon to halosulfuron-methyl had no effect on the height of various market classes of dry bean. Bentazon added to cloran-sulam-methyl generally has no effect on seed moisture content in black and white bean but decreased seed moisture content of cranberry and kidney bean as much as 4%. The addition of bentazon to halosulfuron-methyl caused no effect on seed moisture content of dry bean. Cloransulam-methyl caused a 7% to 18% reduction in dry bean yield compared to halosulfuron-methyl and 12% to 21% reduction in yield compared to bentazon. Bentazon added to cloransulam-methyl increased dry bean yield by 0.16 and 0.31 t·ha–1 at Exeter (2009) and Ridgetown (2009) respectively. The addition of bentazon to halosulfuron-methyl had no effect on dry bean yield.
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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) | 0.000 | 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 | 0.000 |
| 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".