Control of Glyphosate-Resistant Marestail in Identity-Preserved or Glyphosate-Resistant and Glyphosate/Dicamba-Resistant Soybean with Preplant Herbicides
Bibliographic record
Abstract
Two studies, each consisting of six field experiments were conducted in growers’ fields in 2018 and 2019 to determine the optimal herbicide tankmixes, applied preplant (PP) for the control of glyphosate-resistant (GR) marestail in 1) identity-preserved and glyphosate-resistant soybean (Study 1) and, 2) glyphosate/dicamba-resistant soybean (Study 2). There was no significant injury in soybean with the PP herbicides evaluated in both studies. In Study 1, at 8 weeks after treatment (WAA), glyphosate + saflufenacil, glyphosate + 2,4-D ester, glyphosate + pyraflufen/2,4-D, glyphosate +, 4-D choline or glyphosate + halauxifen-methyl, applied PP, controlled GR marestail 93%, 58%, 60%, 67% and 71%, respectively. The addition of metribuzin to the tankmixes of glyphosate + saflufenacil, 2,4-D ester and pyraflufen/2,4-D increased the control to 98%, 91% and 95%, respectively. The addition of metribuzin + chlorimuron-ethyl to 2,4-D choline/glyphosate and glyphosate + halauxifen-methyl increased the control to 94% and 93%, respectively. In Study 2, at 8 WAA, glyphosate/dicamba, applied PP, controlled GR marestail 89% in glyphosate/dicamba-resistant soybean. The addition of metribuzin or saflufenacil to glyphosate/dicamba controlled GR marestail 86% and 97%, respectively. At 8 WAA, S-metolachlor/dicamba controlled GR marestail 83%. The addition of metribuzin or saflufenacil to the above premix controlled GR marestail 87% and 97%, respectively. Density and biomass reductions were similar to visible control. GR marestail interference reduced soybean yield 60% and 53% in Study 1 and 2, respectively. Reduced GR marestail interference with all the herbicide treatments evaluated in both studies resulted in soybean yield that was similar to the weed-free control.
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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) | 0.001 | 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 | 0.001 |
| 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".