The Impact of the Addition of Urea Ammonium Nitrate to 2,4-D on the Control of Multiple-Herbicide-Resistant Canada Fleabane
Bibliographic record
Abstract
Limited information exists on the impact of the addition of 28% urea ammonium nitrate (UAN) to 2,4-D, applied preplant (PP) to soybean, on the control of multiple-herbicide-resistant (MHR) Canada fleabane. A total of five field experiments were conducted over a two-year period (2020, 2021) in southwestern Ontario to determine if MHR Canada fleabane control with 2,4-D ester applied PP can be improved by adding UAN to the spray solution. Glyphosate + 2,4-D ester applied PP with various rates of UAN caused no soybean injury. The control of MHR Canada fleabane decreased when UAN was added to the spray solution, especially at higher rates evaluated. At 4 weeks after treatment (WAT), the predicted percent UAN concentration in the spray solution that caused a 5, 10, 20, or 50% decrease in MHR Canada fleabane control was 12, 17, 25, and 52%, respectively. At 8 WAT, the predicted percent UAN concentration that caused a 5, 10, 20, or 50% decrease in MHR Canada fleabane control was 10, 13, 18, and 33%, respectively. At 8 WAT, the predicted percent UAN concentration that caused a 5 and 10% increase in MHR Canada fleabane density was 36 and 73%, respectively. At 8 WAT, the predicted percent UAN concentration that caused a 5, 10, 20, or 50% increase in MHR Canada fleabane biomass was 2, 4, 8, and 19%, respectively. The predicted percent UAN concentration that caused a 5, 10, and 20% decrease in soybean yield was 1, 3, and 12%, respectively. This study concludes that the addition of 28% UAN, especially at the higher concentration, to 2,4-D ester reduces control and increases the density and biomass of MHR Canada fleabane.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".