Effect of Class Act NG Adjuvant on Glyphosate Efficacy in Corn
Bibliographic record
Abstract
There is little information on the effect of the co-application of glyphosate with Class Act NG adjuvant on weed control efficacy and corn yield under Ontario environmental conditions. This study consisted of 6 field experiments that were conducted in Ontario during 2021 and 2022 to determine if the addition of Class Act NG (2.5% v/v) to glyphosate at 450, 900 and 1350 g ae ha-1 would improve weed control and result in a concomitant increase in corn yield. The co-application of glyphosate with Class Act NG resulted in no visible corn injury at 1 and 4 weeks after herbicide application (WAA). The addition of Class Act NG to glyphosate at 450 g ae ha-1 improved control of common lambsquarters, velvetleaf, Powell amaranth, common ragweed, and barnyardgrass as much as 20, 14, 9, 8, and 7%, respectively but there was no improvement in control of giant foxtail, or green foxtail and there was no increase in corn yield. The addition of Class Act NG to glyphosate at 900 g ae ha-1 improved common lambsquarters control 6 and 5% at 4 and 8 WAA, respectively and improved barnyardgrass control 4% at 4 WAA. The addition of Class Act NG to glyphosate at 1350 g ae ha-1 provided no improvement in control of velvetleaf, Powell amaranth, common ragweed, common lambsquarters, barnyardgrass, giant foxtail, or green foxtail and there was no increase in corn yield. Based on this data the co-application of glyphosate with Class Act NG results in improved control of some annual broadleaf and grass weeds (common lambsquarters, velvetleaf, Powell amaranth, common ragweed and barnyardgrass) when glyphosate is applied at 450 or 900 g ae ha-1; however, when glyphosate is applied at 1350 g ae ha-1 there was no improvement in weed control. The addition of Class Act NG to glyphosate at 450, 900 and 1350 g ae ha-1 did not result in an increase in corn yield.
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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".