Tolerance of adzuki bean to preplant‐incorporated, pre‐emergence, and post‐emergence herbicides in Ontario, Canada
Bibliographic record
Abstract
Weed management options for adzuki‐bean growers in Ontario, Canada are limited due to few herbicide registrations. Four field trials were conducted at three locations in south‐western Ontario in 2007 and 2008 to determine the tolerance of adzuki bean to several preplant‐incorporated (PPI), pre‐emergence (PRE), and post‐emergence (POST) herbicides. All the herbicides were applied at the doses registered for use in soybean. The application of pendimethalin, cloransulam‐methyl, and halosulfuron‐methyl (PPI), flumetsulam, cloransulam‐methyl, and halosulfuron‐methyl (PRE), and acifluorfen and fomesafen (POST) caused ≤15% crop injury; however, the injury was transient and did not reduce the adzuki bean yield. The POST application of cloransulam‐methyl and imazethapyr caused ≤23% crop injury and reduced the biomass by ≤50%, but did not reduce the plant height or crop yield. Metribuzin, flumetsulam, atrazine, and pyroxasulfone (PPI), metribuzin, linuron, pyroxasulfone, and atrazine (PRE), and bentazon, imazethapyr plus bentazon, halosulfuron‐methyl, and thifensulfuron‐methyl (POST) caused ≤61% crop injury. These treatments reduced the biomass, plant height, and crop yield. Based on these results, pendimethalin, cloransulam‐methyl, and halosulfuron‐methyl applied PPI, flumetsulam, cloransulam‐methyl, and halosulfuron‐methyl applied PRE, and acifluorfen and fomesafen applied POST might be potential weed management options for weed management in adzuki bean. Cloransulam‐methyl and imazethapyr applied POST will need further evaluation due to phytotoxicity concerns. Metribuzin, flumetsulam, atrazine, and pyroxasulfone applied PPI, metribuzin, linuron, atrazine, and pyroxasulfone applied PRE, and bentazon, imazethapyr plus bentazon, halosulfuron‐methyl, and thifensulfuron‐methyl applied POST did not have an adequate margin of safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".