Preplant followed by postemergence herbicide programs and biologically effective rate of metribuzin for control of glyphosate-resistant common ragweed (<i>Ambrosia artemisiifolia</i>) in soybean
Bibliographic record
Abstract
With no new herbicides with a novel mode of action in the marketplace in corn–soybean cropping systems, control of glyphosate-resistant (GR) weeds requires the re-evaluation of existing herbicides. This necessitates a renewed focus on using herbicide tank-mixes and sequential herbicide programs while also striving to minimize the environmental impact of weed management. Preliminary research identified four preplant (PP) herbicides (2,4-D, saflufenacil/dimethenamid-P, linuron, and metribuzin) and one postemergence (POST) herbicide (fomesafen) that provided good but inconsistent control of GR common ragweed when applied alone in soybean. The objectives of this study were to determine the biologically effective rate of metribuzin and evaluate PP followed by POST herbicide programs for control of GR common ragweed in soybean. The aforementioned PP herbicides reduced GR common ragweed density and aboveground biomass by 82%–94% and 55%–89%, respectively. In contrast, a PP herbicide followed by fomesafen applied POST decreased common ragweed densities and aboveground biomass by 97%–99% and 93%–98%, respectively. Metribuzin applied at 824 and 1015 g a.i. ha −1 controlled GR common ragweed 90% at 4 and 8 wk after application, respectively. This study concludes that GR common ragweed can be controlled with a PP followed by POST herbicide program and metribuzin has potential for control of GR common ragweed in 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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".