The addition of adjuvants on glyphosate enhances the control of aquatic plant Myriophyllum aquaticum (Vell.)
Bibliographic record
Abstract
Background: Knowledge about the action of glyphosate alone and associated with adjuvants in the effectiveness to control aquatic plants is important in the decision-making on its use. Objective: This study aimed to evaluate the effectiveness of glyphosate and five adjuvants in the control of Myriophyllum aquaticum. Methods: Glyphosate (Rodeo®) at doses of 1.5, 3.5, 5.5, and 7.5 L ha-1 was sprayed alone and associated with Aterbane® BR, Veget’oil®, Dash® HC, Assist®, and Agral® (0.5% v v-1), in addition to the control, with a spray solution volume of 200 L ha-1. The effectiveness of control was evaluated using the scores of a visual scale at 3, 7, 15, 21, 30, 45, and 60 days after application (DAA), regrowth, and dry matter accumulation at 60 DAA. Results: The best effectiveness of control of the glyphosate alone was 85% at the dose of 7.5 L ha-1, increasing to 100% when associated with Aterbane® and Veget’oil®. The control reached 100% for all glyphosate doses associated with Dash®. Moreover, glyphosate at the dose of 7.5 L ha-1 associated with Assist® provided a 98% control, while glyphosate doses of 3.5, 5.5, and 7.5 L ha-1 associated with Agral® provided a 100% control. Glyphosate at doses of 5.5 and 7.5 L ha-1 associated with Dash® and Agral® was more effective in reducing the regrowth and dry biomass (100%). Thus, glyphosate + Dash® and Agral® promoted the highest control (above 95%), the lowest regrowth, and the highest reduction in the dry biomass of M. aquaticum. Conclusions: The addition of Aterbane® BR, Dash®, and Agral® to glyphosate improved the effectiveness of control of M. aquaticum and contributed to reducing the applied herbicide dose.
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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) | 0.000 | 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 | 0.000 |
| 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".