Control of Volunteer Glyphosate-Resistant Corn (<i>Zea mays</i>) in Glyphosate-Resistant Soybean (<i>Glycine max</i>)
Bibliographic record
Abstract
Volunteer corn in soybean can reduce yields, interfere with harvest, and cause unacceptable levels of contamination by its presence in the harvested soybean. In Ontario, soybean frequently follow corn in rotation. The use of glyphosate-resistant corn and soybean varieties has increased dramatically in Ontario. Field studies were conducted at two locations in southwestern Ontario to determine whether quizalofop-p-ethyl, clethodim, and fenoxaprop-p-ethyl can be tank mixed with glyphosate to provide effective control of volunteer glyphosate-resistant corn in glyphosate-resistant soybean. Soybean plots were overseeded with glyphosate-resistant corn and treatments consisting of glyphosate applied alone and tank mixed with full and reduced rates of each graminicide with and without a recommended surfactant. Tank mixing the graminicides and adjuvants with glyphosate did not affect glyphosate weed control or crop tolerance. Use of a recommended adjuvant significantly improved the effectiveness of the graminicides, particularly when reduced rates were applied. Quizalofop-p-ethyl was the most effective graminicide for controlling glyphosate-resistant volunteer corn, followed by clethodim and fenoxaprop-p-ethyl.
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".