Weed Management in Azuki Bean with Postemergence Herbicides
Bibliographic record
Abstract
A limited number of postemergence (POST) herbicides are available for weed management in azuki bean production in Ontario. A total of three field trials were conducted during 2017 to 2019 at the Huron Research Station, University of Guelph, Exeter, Ontario, Canada to evaluate the efficacy of various postemergence (POST) herbicides for weed control in azuki bean. Fomesafen, acifluorfen and acifluorfen + Assist® caused up to 6% crop injury and weed interference reduced azuki bean yield up to 42%. Bentazon, bentazon Forte, bentazon/acifluorfen, bentazon + fomesafen + Assist®, bentazon + fomesafen + Turbocharge® and bentazon Forte + fomesafen caused up to 16% injury and weed interference reduced azuki bean yield up to 53%. Fomesafen, acifluorfen, acifluorfen + Assist®, bentazon, bentazon Forte, bentazon/acifluorfen, bentazon + fomesafen + Assist®, bentazon + fomesafen + Turbocharge® and bentazon Forte + fomesafen provided 36% to 88% control of redroot pigweed (Amaranthus retroflexus L.), common ragweed (Ambrosia artemesiifolia L.), and common lambsquarters (Chenopodium album L.). Fomesafen, acifluorfen + Assist® and acifluorfen provided 59% to 83% control of flower-of-an-hour (Hibiscus trionum L.), but bentazon, bentazon Forte, bentazon/acifluorfen, bentazon + fomesafen + Assist®, bentazon + fomesafen + Turbocharge® and bentazon Forte + fomesafen provided 78% to 99% control of H. trionum. All POST herbicides evaluated provide 99% to 100% control of wild mustard (Sinapis arvensis L.) in azuki bean. Based on these results, none of the POST herbicide evaluated that included bentazon can be used safely for the control of annual broadleaved weeds in azuki bean under Ontario environmental conditions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".