Effect of Crimson® NG Adjuvant on Glyphosate Efficacy in Corn
Bibliographic record
Abstract
There is limited published data on the impact of the addition of Crimson® NG to glyphosate on weed control efficacy in corn under Ontario environmental conditions. Four field experiments were conducted at the University of Guelph, Ridgetown Campus, Ridgetown, Ontario during 2022 and 2023 with two water sources to evaluate weed control with glyphosate alone and in combination with Crimson® NG. Glyphosate was applied at rates of 900, 1029, or 1221 g ae ha-1, with and without Crimson® NG at 1.0 and 2.5% v/v, using water with 56 ppm (Ridgetown) and 1600 ppm (Plattsville) hardness. Results showed that glyphosate at 900 g ae ha-1 controlled velvetleaf, Powell amaranth, common ragweed, common lambsquarters, barnyardgrass, and giant foxtail 97-100%, independent of water hardness. Neither increased glyphosate rates nor the addition of Crimson® NG significantly improved weed control. No corn injury was observed with all herbicide/adjuvant treatments evaluated. These findings confirm that glyphosate at 900 g ae ha-1 is highly effective for the control of common annual grass and broadleaf weeds. Weed control efficacy with glyphosate was not influenced by water source and there was no improvement in weed control efficacy from increasing the rate of glyphosate or from the addition of Crimson® NG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".