Efficacy of POST glyphosate applications in combination with other POST herbicides in glyphosate-resistant corn (Zea mays L.)
Bibliographic record
Abstract
The use of glyphosate-resistant corn has facilitated a shift from a reliance on preemergence residual herbicides to postemergence (POST) herbicides, and in some cases exclusively glyphosate. Glyphosate is a non-selective herbicide that is relatively slow-acting, which may allow weeds to continue to compete with corn after application and potentially decrease crop yield. The addition of several POST corn herbicides, with some residual control, to an early-season glyphosate application was examined to determine if the tankmix combination would improve the speed of weed control compared to glyphosate applied alone. Seven field trials were conducted over three years (2009, 2010 and 2011) near Ridgetown and Exeter, Ontario. The control of common ragweed was improved 3 days after application (DAA) with three POST glyphosate tankmixes compared to glyphosate alone. However control was still less than 55%. Depending on the weed species examined, at 28 DAA two of the glyphosate tankmix treatments tested provided better common ragweed, common lambsquarters, or green foxtail control than glyphosate alone. Treatments providing better weed control at 28 DAA also typically decreased weed density compared to glyphosate alone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".