Activity of Isoxaflutole plus Metribuzin Tankmixes in Isoxaflutole-Resistant Soybean
Bibliographic record
Abstract
Isoxaflutole-resistant soybean is currently in development for commercialization in North America. Proposals to use isoxaflutole + metribuzin as the main herbicide tank-mixture raise concerns as there is limited grass control with these herbicides. Strategies are needed to improve grass control with isoxaflutole + metribuzin. Nine experiments were conducted over a two-year period (2017, 2018) to determine the efficacy of isoxaflutole + metribuzin (52.5 + 210 g a·i· ha-1) applied alone and co-applied with pendimethalin, dimethenamid-P, pethoxamid, pyroxasulfone or S-metolachlor applied preemergence (PRE). Comparisons were made with isoxaflutole + metribuzin at a low rate (52.5 + 210 g a·i· ha-1), medium rate (79 + 315 g a·i· ha-1) and a high rate (105 + 420 g a·i· ha-1). Eight weed species were evaluated including common lambsquarters, green and redroot pigweed, common ragweed, velvetleaf, green and giant foxtail, yellow foxtail, barnyardgrass and witchgrass. All herbicides were affected by amount of rainfall following application; less rainfall resulted in reduced weed control. The addition of pendimethalin, dimethenamid-P, pethoxamid, pyroxasulfone or S-metolachlor to the low rate ofisoxaflutole + metribuzin provided equivalent control of all weed species evaluated compared toisoxaflutole + metribuzin at the low, medium, or high rate.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".