Control of annual ryegrass with spring-applied herbicides prior to seeding corn
Bibliographic record
Abstract
Four field experiments were conducted over a 2 yr period (2017 and 2018) in Ontario to determine the control of annual ryegrass (ARG) seeded in the fall of 2016 and 2017 (as a cover crop) with spring-applied glyphosate alone and in a tankmixure with clethodim, fluazifop-P-butyl, quizalofop-P-butyl, sethoxydim, or saflufenacil prior to seeding glyphosate-resistant corn. The doses of glyphosate needed to provide 50%, 80%, and 90% control of ARG were 439, 1757, and >2700 g a.e. ha −1 at 3 wk after treatment application (WAA); 526, 2105, and >2700 g a.e. ha −1 at 4 WAA; and 703, >2700, and >2700 g a.e. ha −1 at 6 WAA, respectively. Glyphosate (1350 g a.e. ha −1 ) controlled ARG 27%, 61%, 77%, 72%, and 68% at 1, 2, 3, 4, and 6 WAA, respectively. The tankmix of glyphosate (1350 a.e. ha −1 ) with clethodim (30 g a.i. ha −1 ), fluazifop-P-butyl (125 g a.i. ha −1 ), quizalofop-P-ethyl (36 g a.i. ha −1 ), sethoxydim (150 g a.i. ha −1 ), or saflufenacil (25 g a.i. ha −1 ) controlled ARG as much as 82%, 79%, 82%, 84%, and 81%, respectively. ARG control with the tankmixes of glyphosate (1350 a.e. ha −1 ) with the Group 1 herbicides evaluated increased corn yield as much as 66%. Additionally, reduced ARG interference with the tankmix of glyphosate (1350 a.e. ha −1 ) +saflufenacil (25 g a.i. ha −1 ) increased corn yield 69%. The best control of ARG was achieved with high doses of glyphosate alone and glyphosate (1350 g a.e. ha −1 ) tankmixed with a Group 1 herbicide or saflufenacil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".