Tolerance of mung bean to postemergence herbicides
Bibliographic record
Abstract
There are a limited number of postemergence (POST) herbicides available for weed management in mung bean production in Ontario. Five field studies were conducted in 2010, 2011 and 2012 near Exeter, Ontario and in 2011 and 2012 near Ridgetown, Ontario to determine the tolerance of mung bean to fomesafen, bentazon, bentazon + fomesafen and halosulfuron applied POST at the 1X and 2X proposed manufacturer’s recommended rate. Bentazon caused 5%-29%, 4%-31%, and 2%-18% injury, fomesafen caused 3%-17%, 1%-7%, and 0%-6% injury, bentazon + fomesafen caused 6%-40%, 4%-37%, and 1%-20% injury, and halosulfuron caused 13%-65%, 8%-75%, and 5%-47% injury in mung bean at 1, 2, and 4 weeks after treatment (WAT), respectively. At Exeter, fomesafen had no adverse effect on height of mung bean but bentazon, bentazon + fomesafen and halosulfuron decreased mung bean height as much as 5% compared to the untreated control. At Ridgetown, there was no decrease in mung bean height due to the herbicides applied. Fomesafen had no adverse effect on shoot dry weight of mung bean but bentazon, bentazon + fomesafen and halosulfuron decreased shoot dry weight of mung beans as much as 43%, 47%, and 57%, respectively. Fomesafen, bentazon, bentazon + fomesafen and halosulfuron had no adverse effect on the seed moisture content and seed yield of mung bean with the exception of halosulfuron applied POST at 70 g ai ha-1 which increased seed moisture content 0.4% at Exeter and 1.4% at Ridgetown and decreased yield 16% at Exeter compared to the untreated control. Based on these results, there is not an adequate margin of crop safety for bentazon, bentazon + fomesafen and halosulfuron applied POST in mung bean. However, there is potential for fomesafen applied POST at the proposed manufacturer’s rate of 240 g ai ha-1 in mung 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".