Effect of amitrole and 2,4‐D applied preplant and pre‐emergence in soybean (<i>Glycine max</i>)
Bibliographic record
Abstract
There is limited information on the effect of amitrole and 2,4‐D ester applied preplant and pre‐emergence in soybean (Glycine max L.) in Ontario, Canada. Six field trials were conducted over a 2 year period (2004 to 2005) at three Ontario locations to evaluate the response of soybean to amitrole or 2,4‐D ester applied at 14 days preplant (DPP), 7 DPP, 1 day after planting (DAP), and 7 DAP. The application of amitrole resulted in as much as 5.8, 3.9, 1.7, and 1% visible crop injury in soybean at 7, 14, 28, and 56 days after emergence (DAE), respectively. There was no visible injury in soybean with any amitrole treatment at 56 DAE, except for amitrole applied at 7 DAP, which caused 1% visible injury in soybean at 2310 g ha−1. The application of the 2,4‐D ester caused ≤8.3, 9.7, 4.6, and 1.3% visible injury in soybean at 7, 14, 28, and 56 DAE, respectively. The visible injury decreased over time. There was no visible injury in soybean with any of the 2,4‐D ester treatments at 56 DAE, except for the 2,4‐D ester treatment applied at 7 DAP, which caused 1% visible injury at 1155 g ha−1 and 1.3% visible injury at 2310 g ha−1. Soybean generally responded similarly to amitrole and 2,4‐D ester when applied at 14 and 7 DPP; however, soybean was more tolerant to amitrole compared to 2,4‐D ester when applied at 1 or 7 DAP. The application of amitrole and 2,4‐D ester resulted in no biomass or yield reduction in soybean compared to the weed‐free, untreated control at all doses and application timings evaluated. Soybean is tolerant to the preplant and pre‐emergence application of amitrole or 2,4‐D ester at the doses evaluated.
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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) | 0.000 | 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".