When using glyphosate plus dicamba, 2,4-D, halauxifen or pyraflufen/2,4-D for glyphosate-resistant horseweed (<i>Erigeron canadensis</i>) control in soybean, which third mix partner is better, saflufenacil or metribuzin?
Bibliographic record
Abstract
Abstract Glyphosate-resistant (GR) horseweed interference in soybean can reduce soybean yield up to 93%. Glyphosate plus dicamba, 2,4-D ester, halauxifen-methyl or pyraflufen-ethyl/2,4-D applied preplant (PP) provide variable GR horseweed control in soybean. The objective of this study was to determine if the addition of saflufenacil or metribuzin to glyphosate plus dicamba, 2,4-D ester, halauxifen-methyl, or pyraflufen-ethyl/2,4-D will improve the level and consistency of GR horseweed control. Four trials were conducted over the 2020 and 2021 field seasons in fields with GR horseweed populations. Glyphosate plus dicamba, 2,4-D ester, halauxifen-methyl, or pyraflufen-ethyl/2,4-D controlled GR horseweed 96%, 77%, 71%, and 52%, respectively, at 8 wk after application (WAA). When saflufenacil or metribuzin was added to glyphosate plus dicamba or 2,4-D ester, GR horseweed control was not improved at 8 WAA. When saflufenacil or metribuzin was added to glyphosate plus halauxifen-methyl, GR horseweed control improved by 27% and 25%, respectively, at 8 WAA. When saflufenacil or metribuzin was added to glyphosate plus pyraflufen-ethyl/2,4-D, GR horseweed control was improved by 47% and 37%, respectively, at 8 WAA. The consistency of GR horseweed control was improved when saflufenacil or metribuzin was added to glyphosate plus dicamba, 2,4-D ester, halauxifen-methyl, or pyraflufen-ethyl/2,4-D compared to each herbicide applied alone. Synergism was observed when metribuzin was added to glyphosate plus halauxifen-methyl and when saflufenacil or metribuzin was added to glyphosate plus pyraflufen-ethyl/2,4-D at 8 WAA. Though GR horseweed control was improved with the addition of saflufenacil or metribuzin to glyphosate plus halauxifen-methyl or pyraflufen-ethyl/2,4-D, all treatments including saflufenacil resulted in the highest level and most consistent 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) | 0.000 | 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".