Tolerance of four dry bean market classes to flufenacet, acetochlor, and <i>S</i>-metolachlor applied preplant incorporated
Bibliographic record
Abstract
Abstract Common bean and azuki bean are poor competitors with weeds and demonstrate sensitivity to herbicides used for weed control in soybean. S-metolachlor, flufenacet, and acetochlor are categorized as Group 15 herbicides and provide control of multiple annual grass and select small-seeded broadleaf weeds. By way of field trials near Exeter and Ridgetown, Ontario, in 2019, 2020, and 2021, four dry bean market classes (azuki, kidney, small red, and white bean) were evaluated for their tolerance to 1× established label rates and 2× rates of S-metolachlor (1,600 and 3,200 g ai ha−1), flufenacet (750 and 1,500 g ai ha−1) and acetochlor (1,700 and 3,400 g ai ha−1) applied preplant incorporated (PPI). Injury was evaluated by assessing visible injury symptoms, density, shoot biomass, height, seed moisture content, and seed yield. Azuki bean was more sensitive to the Group 15 herbicides than other dry bean market classes; the Group 15 herbicides caused a 12% reduction in azuki bean growth at 2 wk after emergence; growth reduction was ≤2% in the other bean classes. Flufenacet (2× rate) was the most injurious treatment, causing a 27% reduction in azuki bean yield. This study concludes that kidney, small red, and white bean have a sufficient margin of crop safety to flufenacet, acetochlor, and S-metolachlor applied PPI. Azuki bean was sensitive to flufenacet; additional research is needed to investigate azuki bean tolerance to acetochlor and S-metolachlor applied PPI.
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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".