Control of Glyphosate-Resistant Giant Ragweed in Winter Wheat
Bibliographic record
Abstract
Four field experiments were conducted over a 2-yr period (2012 and 2013) in winter wheat to evaluate POST herbicides for the control of glyphosate-resistant (GR) giant ragweed. POST herbicides were evaluated for winter wheat injury and GR giant ragweed control, population density, and aboveground biomass. The herbicides used in this study provided 54 to 90% and 51 to 97% control of GR giant ragweed at 4 and 8 wk after treatment (WAT), respectively. At 8 WAT, auxinic herbicide treatments or herbicide tank mix/premix treatments that contained auxinics provided 78 to 97% control of GR giant ragweed. Reductions in GR giant ragweed population density and aboveground biomass were 62 to 100% and 83 to 100%, respectively, and generally reflected the level of control. The results of this research indicate that Ontario, Canada, corn and soybean growers should continue to incorporate winter wheat into their crop rotation as one component of an integrated weed management (IWM) strategy for the control of GR giant ragweed.
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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 | 0.000 |
| 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 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".