Annual weed management in isoxaflutole-resistant soybean using a two-pass weed control strategy
Bibliographic record
Abstract
Abstract Transgenic crops are being developed with herbicide resistance traits to expand innovative weed management solutions for crop producers. Soybean with traits that confer resistance to the hydroxyphenylpyruvate dioxygenase herbicide isoxaflutole is under development and will provide a novel herbicide mode of action for weed management in soybean. Ten field experiments were conducted over 2 years (2017 and 2018) on five soil textures with isoxaflutole-resistant soybean to evaluate annual weed control using one- and two-pass herbicide programs. The one-pass weed control programs included isoxaflutole plus metribuzin, applied PRE, at a low rate (52.5 + 210 g ai ha −1 ), medium rate (79 + 316 g ai ha −1 ), and high rate (105 + 420 g ai ha −1 ); and glyphosate applied early postemergence (EPOST) or late postemergence (LPOST). The two-pass weed control programs included isoxaflutole plus metribuzin, applied PRE, followed by glyphosate applied LPOST, and glyphosate applied EPOST followed by LPOST. At 4 weeks after the LPOST application, control of common lambsquarters, pigweed species, common ragweed, and velvetleaf was variable at 25% to 69%, 49% to 86%, and 71% to 95% at the low, medium, and high rates of isoxaflutole plus metribuzin, respectively. Isoxaflutole plus metribuzin at the low, medium, and high rates controlled grass species evaluated (i.e., barnyardgrass, foxtail, crabgrass, and witchgrass) 85% to 97%, 75% to 99%, and 86% to 100%, respectively. All two-pass weed management programs provided 98% to 100% control of all species. Weed control improved as the rate of isoxaflutole plus metribuzin increased. Two-pass programs provided excellent, full-season annual grass and broadleaf weed control in isoxaflutole-resistant soybean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".