Tolerance of azuki bean to herbicides applied preplant for weed control in a strip‐tillage cropping system
Bibliographic record
Abstract
Abstract Five field experiments were conducted at Huron Research Station near Exeter, Ontario, Canada, during 2018 to 2020 to assess the tolerance of strip‐till‐grown azuki bean to various preplant (PP) herbicides. The herbicides selected have activity on glyphosate‐resistant (GR) Canada fleabane, an emerging weed biotype in strip‐till azuki bean production. Saflufenacil, metribuzin, 2,4‐D ester, saflufenacil + metribuzin, saflufenacil + 2,4‐D ester, metribuzin + 2,4‐D ester, and saflufenacil + metribuzin + 2,4‐D ester, applied PP 1 week before seeding, at the proposed label rate (1X) and twice that rate (2X) caused as much as 6%, 5%, 6%, 7%, 8%, 10%, and 13% visible azuki bean injury. The herbicide‐induced azuki bean injury was transient and had no effect on plant density, aboveground dry biomass, height, maturity, and yield except for the dry biomass which was reduced by 28% with metribuzin + 2,4‐D ester and 36% with saflufenacil + metribuzin + 2,4‐D ester at the 2X rate and azuki bean height which was reduced 9% at the 2X rate with saflufenacil + metribuzin + 2,4‐D ester. Based on these results, saflufenacil, metribuzin, 2,4‐D ester, saflufenacil + metribuzin, saflufenacil + 2,4‐D ester, and metribuzin + 2,4‐D ester applied PP have potential for GR Canada fleabane control in strip‐till azuki beans. However, there is not enough crop safety for using a three‐way tankmix of saflufenacil + metribuzin + 2,4‐D ester, applied PP, in azuki 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 | 0.000 | 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".