Control of Volunteer Corn with the AAD-1 (aryloxyalkanoate dioxygenase-1) Transgene in Soybean
Bibliographic record
Abstract
Volunteer Enlist corn with the AAD-1 (aryloxyalkanoate dioxygenase-1) transgene can become a problem when glyphosate-resistant (GR) soybean follows Enlist corn in the rotation. Field trials were conducted at Ridgetown, Ontario in 2013 and 2014 to evaluate the control of volunteer Enlist corn in GR soybean. Glyphosate plus clethodim at 30 g ai ha−1provided 75 to 92% control of volunteer Enlist corn at 1, 2, 4, and 8 weeks after treatment application (WAT) and reduced volunteer Enlist corn density and dry weight 95 to 97%. Glyphosate plus clethodim at 60 g ai ha−1provided 84 to 98% control of volunteer Enlist corn at 1, 2, 4, and 8 WAT and reduced volunteer Enlist corn density and dry weight 97 to 99%. Glyphosate plus sethoxydim at 150 g ai ha−1provided 66 to 86% control of volunteer Enlist corn at 1, 2, 4, and 8 WAT and reduced volunteer Enlist corn density and dry weight 91 to 97%. Glyphosate plus sethoxydim at 300 g ha−1provided 84 to 96% control of volunteer Enlist corn at 1, 2, 4, and 8 WAT and reduced volunteer Enlist corn density and dry weight 96 to 98%. Glyphosate plus fenoxaprop-p-ethyl, fluazifop-p-butyl, and quizalofop-p-ethyl applied POST provided 0 to 9% control of volunteer Enlist corn at 1, 2, 4, and 8 WAT and reduced volunteer Enlist corn density and dry weight 18 to 44%. Soybean yields closely reflected the level of volunteer Enlist corn control. Based on these results, the cyclohexanedione herbicides, clethodim and sethoxydim, provide adequate control of volunteer Enlist corn in GR soybean. In contrast, the aryloxyphenoxypropionate herbicides, fenoxaprop-p-ethyl, fluazifop-p-butyl and quizalofop-p-ethyl do not provide control of volunteer Enlist corn in GR soybean.
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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) | 0.000 | 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 | 0.001 |
| 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".