Behavior of Five Sulfonylurea Herbicides and a Low-Dose Glyphosate on Cynodon nlemfuensis Pasture
Bibliographic record
Abstract
African star grass (Cynodon nlemfuensis Vanderyst) is an excellent forage for animal feed, especially in tropical and subtropical climates. However, there is little information on weed management in African star grass pastures. Two experiments were carried out in 2017 and 2018 to evaluate the response of African star grass to five herbicides of the sulfonylureas chemical group and glyphosate at a low dose. The treatments were as follows: metsulfuron-methyl (Ally®) (7.8 and 15.6 g ai ha-1 plus 0.1% v/v mineral oil); chlorimuron-ethyl (Staron®) (15.0 and 30.0 g ai ha-1 plus 0.05% v/v mineral oil); halosulfuron (Sempra ®) (112.5 and 225.0 g ai ha-1 plus 0.1% v/v surfactant); ethoxysulfuron (Gladium®) (150.0 and 300.0 g ai ha-1); nicosulfuron (Sanson®) (60.0 and 120.0 g ai ha-1); glyphosate (Roundup Original®) (360 g ae ha-1); and a control without herbicide application. The herbicides nicosulfuron (60.0 and 120.0 g ai ha-1) and glyphosate were the most phytotoxic treatments; however, none of the treatments caused the total death of African star grass plants. The herbicides metsulfuron-methyl, chlorimuron-ethyl, halosulfuron and ethoxysulfuron were selective and are potential products for use in African star grass pastures.
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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 | 0.000 | 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".