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 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 | 0.000 | 0.000 |
| 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 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".