Responses of Dry Bean to 2,4-D Ester Applied Preplant and Preemergence
Bibliographic record
Abstract
Six field studies were completed in Ontario (during 2016 to 2018) to assess the tolerance of adzuki, kidney, small red and navy bean to 2,4-D ester at 528 or 1056 g·ai·ha-1 applied 14, 7 and 1 day before seeding (PP) and 3 days after seeding (PRE). 2,4-D applied PP or PRE caused as much as 4%, 6%, 7% and 8% injury in adzuki, kidney, small red and navy (white) bean, respectively. There was an increase in bean injury as the preplant interval decreased. At 1 WAE, 2,4-D applied at 1056 g·ai·ha-1 14, 7 and 1 day PP and 3 days after seeding caused up to 6%, 10%, 18% and 5% visible bean injury, respectively. The level of injury decreased over time with minimal bean injury (0 to 3%) at 8 WAE. Bean stand counts were similar to the non-treated control with 2,4-D applied at various timings except at 1 day PP when 2,4-D at the 2X rate decreased bean stand 13%. There was up to 23% and 43% decrease in bean dry weight with 2,4-D applied PP at 528 and 1056 g·ai·ha-1 7 and 1 day PP, respectively. Bean height (6 WAE) was not affected by 2,4-D applied at various timings except at 1 day PP when 2,4-D (1056 g·ai·ha-1) decreased bean height 10%. Additionally, there was no effect of 2,4-D treatments on bean maturity or yield. Based on these results, the safest times to apply 2,4-D are 14 days before seeding or PRE. Injury was higher when 2,4-D was applied 7 and 1 day PP. Injury was lower in adzuki bean compared to kidney, small red or navy bean.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".