Response of glufosinate-resistant Bt sweet corn (<i>Zea mays</i>) cultivars to glufosinate and residual herbicides
Bibliographic record
Abstract
Field studies were conducted in 1999 and 2000 at Simcoe and Exeter, Ontario to determine the effect of glufosinate, applied alone or in combination with s-metolachlor/atrazine, on weed control and crop safety on six glufosinate-resistant Bt sweet corn cultivars. A single application of glufosinate at 0.5 or 1.0 kg ha-1 gave 89% or better control of redroot pigweed, common ragweed, wild buckwheat and lady’s-thumb in 1999 and pale smartweed and redroot pigweed in 2000. Glufosinate applied alone gave reduced control of wild buckwheat and barnyard grass in 2000 and reduced control of crab grass in 1999 and 2000 at Simcoe. S-metolachlor/atrazine applied preemergence, followed by glufosinate postemergence, improved control of these species. In the untreated control, yield of all cultivars, except Bonus Bt at Exeter, was reduced at all locations. Yield of Cupola Bt and SS Jubilee Bt was reduced when glufosinate was applied alone at 0.5 kg ha-1. Yield of Cupola Bt, Empire Bt and SS Jubilee Bt was reduced with s-metolachlor/atrazine used alone. Maximum yields were obtained with a sequential treatment of s-metolachlor/atrazine followed by glufosinate. Glufosinate did not cause any injury, plant height reduction or delayed maturity of any glufosinate-resistant Bt sweet corn cultivars. Key words: Cultivar, sensitivity, herbicide injury, glufosinate, Zea mays
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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) | 0.001 | 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".