Tolerance of Dry Beans to Pyraflufen-Ethyl/2,4-D Ester
Bibliographic record
Abstract
Five field experiments were conducted in Ontario to determine the tolerance of dry beans to pyraflufen-ethyl (6.7 and 13.4 g ai ha-1), 2,4-D ester (520.3 and 1040.6 g ai ha-1) and pyraflufen-ethyl/2,4-D ester (527 and 1054 g ai ha-1) applied preplant. Pyraflufen-ethyl at 6.7 and 13.4 g ai ha-1 caused < 2% injury in azuki, kidney, small red, and white bean. 2,4-D ester at 520.3 and 1040.6 g ai ha-1 caused up to 4 and 6% injury in azuki bean; up to 5 and 12% injury in kidney bean; up to 7 and 12% injury in small red bean; and up to 5 and 8% injury in white bean, respectively. Pyraflufen-ethyl/2,4-D ester at 527 and 1054 g ai ha-1 caused up to 4 and 6% injury in azuki bean; 5 and 11% injury in kidney bean; 7 and 13% injury in small red bean; and 5 and 10% injury in white bean, respectively. Pyraflufen-ethyl (6.7 and 13.4 g ai ha-1), 2,4-D ester (520.3 and 1040.6 g ai ha-1) or their combination applied preplant caused no adverse effect on dry bean stand, aboveground dry biomass, height, seed moisture content, or yield except for 2,4-D (2X rate) and pyraflufen-ethyl/2,4-D ester (2X rate) which reduced dry bean aboveground biomass as much as 32% and plant height up to 28%. This study concludes that pyraflufen-ethyl (6.7 g ai ha-1), 2,4-D ester (520.3 g ai ha-1), and pyraflufen-ethyl/2,4-D ester (527 g ai ha-1) applied preplant is safe to use for weed management in azuki, kidney, small red, and white bean. However, care must be taken to avoid spray overlaps with 2,4-D ester and pyraflufen-ethyl/2,4-D ester to avoid unacceptable dry bean injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".