Optimization of Weed Control in E3 Soybean [Glycine max (L.) Merr.]
Bibliographic record
Abstract
Twenty field experiments were conducted from 2021-2022 at nine locations in Ontario, Canada to optimize weed control in E3 soybean. Two studies compared one- and two-pass weed control programs for control of glyphosate-resistant (GR) Canada fleabane and multiple herbicide-resistant (MHR) waterhemp. Two-pass weed control programs provided greater control of GR Canada fleabane and MHR waterhemp. Another study investigated herbicide interactions for volunteer corn control in E3 soybean. The co-application of 2,4-D choline or dicamba with quizalofop-p-ethyl or clethodim resulted in antagonistic herbicide interactions and reduced control. Glufosinate co-applied with quizalofop-p-ethyl or clethodim resulted in a synergistic interaction. The effect of glufosinate rate, the addition of AMS, and weed size at application was established for five annual weed species. The effect of glufosinate rate, the addition of AMS, and weed size at application is weed species specific. Generally, control is improved with increased rate and reduced weed size at application.
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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 | 0.001 | 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".