Response of non-dicamba-resistant soybean (<i>Glycine max</i>) varieties to dicamba
Bibliographic record
Abstract
Abstract Introduction and rapid adoption of dicamba-resistant (DR) soybean led to an increase of postemergent applications of dicamba. This resulted in a widespread increase in nontarget dicamba injury to non-DR soybean in 2017. Field studies were conducted in Manhattan, KS, in 2018 and 2019 and in Ottawa, KS, in 2019 to investigate the injury and yield response of soybean varieties with varying herbicide-resistance traits and maturity groups when exposed to dicamba. Four varieties were tested: ‘Credenz 3841LL’ (glufosinate resistant), ‘Credenz 4748LL’ (glufosinate resistant), ‘Asgrow AG4135RR2Y’ (glyphosate resistant), and ‘Stine 40BA02’ (glyphosate and isoxaflutole resistant), abbreviated as CR3841, CR4748, AG4135, and ST40B, respectively. Soybeans were treated with 5.6 g ae ha −1 of dicamba at V3 and R1 stages. Percent soybean injury, soybean height, soybean yield and yield components, and injury to offspring were evaluated. Four weeks after treatment (WAT) at V3, the greatest injury was observed in AG4135 and ST40B. Dicamba application at R1 resulted in the greatest injury to ST40B both 4 WAT and at senescence. Minimal injury was observed in all varieties treated at V3 at senescence and yield loss was 5% or less. Dicamba application at R1 resulted in 19 to 34% yield loss, with the least yield loss in CR4748, and the greatest in ST40B. Varieties with greater injury at senescence generally yielded less than other varieties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 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".