Sensitivity of Dry Bean to Herbicides Applied Preplant for Glyphosate-Resistant Horseweed Control in a Strip-Tillage Cropping System
Bibliographic record
Abstract
Abstract During 2016 and 2017, four field experiments were conducted at Huron Research Station near Exeter, ON, to evaluate the sensitivity of dry bean grown under a strip-tillage cropping system, to potential herbicides for the control of glyphosate-resistant (GR) horseweed. At 8 wk after emergence (WAE), saflufenacil, metribuzin, saflufenacil+metribuzin, 2,4-D ester, flumetsulam, cloransulam-methyl, and chlorimuron-ethyl caused 13% to 32%, 8% to 52%, 32% to 53%, 5% to 7%, 13% to 21%, 16% to 29%, and 23% to 43% visible injury in dry beans, respectively. Saflufenacil decreased aboveground biomass 65% in kidney bean and 80% in white bean. Metribuzin decreased biomass 82% in kidney bean and 50% in white bean. Saflufenacil+metribuzin decreased biomass 88% in kidney bean, 68% in small red bean, and 80% in white bean. Chlorimuron-ethyl decreased biomass 40% in white bean. There was no decrease in dry bean biomass with the other herbicides evaluated. Metribuzin and saflufenacil+metribuzin reduced kidney bean seed yield 72% and 76%, respectively. Saflufenacil+metribuzin, flumetsulam, cloransulam-methyl, and chlorimuron-ethyl reduced small red bean seed yield 39%, 27%, 30%, and 54%, respectively. Saflufenacil, metribuzin, saflufenacil+metribuzin, flumetsulam, cloransulam-methyl, and chlorimuron-ethyl reduced seed yield of white bean 52%, 32%, 62%, 33%, 42%, and 62%, respectively. There was no decrease in dry bean yield with the other herbicides evaluated. Among herbicides evaluated, 2,4-D ester caused the least crop injury with no effect in dry bean seed 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 | 0.001 |
| 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".