Evaluation of 2,4-D Amine, Glyphosate, 2,4-D Amine plus Glyphosate DMA and 2,4-D Choline/Glyphosate DMA for Their Efficacy on Glyphosate Susceptible and Resistant Canada Fleabane Populations
Bibliographic record
Abstract
The 2,4-D choline/glyphosate DMA formulation has reduced drift and volatility compared to the amine or ester formulation of 2,4-D and therefore is advantageous compared to a tank mix of 2,4-D amine or ester with glyphosate. The objective of this research was to compare the control of glyphosate susceptible and glyphosate resistant Canada fleabane with 2,4-D choline/glyphosate DMA with 2,4-D amine, glyphosate, and a tank mix of 2,4-D amine and glyphosate. Ten rates of 2,4-D amine (0 - 6708 g·ae·ha-1), glyphosate (0 - 7052 g·ae·ha-1), a tank mix of glyphosate plus 2,4-D amine (0 - 7052 g·ae·ha-1 + 0 - 6708), and 2,4-D choline/glyphosate DMA (0 - 13760 g·ae·ha-1) were examined in the greenhouse for the control of two susceptible (GS) and two resistant to glyphosate (GR) Canada fleabane biotypes. The tank mix of 2,4-D amine plus glyphosate and 2,4-D choline/glyphosate DMA provided equivalent control of the GR Canada fleabane biotypes at 35 days after the application (DAA). The 2,4-D choline/glyphosate DMA treatment was more efficacious than the tank mix on the GS biotypes. Glyphosate (880 g·ae·ha-1) provided 50% and 100% control of the resistant and susceptible biotypes, respectively. The 2,4-D choline/glyphosate DMA formulation and the tankmix of 2,4-D amine and glyphosate provided similar control of GR 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".