Suppression of Glyphosate-resistant Canada Fleabane (<i>Conyza canadensis</i>) in Corn with Cover Crops Seeded after Wheat Harvest the Previous Year
Bibliographic record
Abstract
Abstract Glyphosate-resistant (GR) and multiple herbicide–resistant (groups 2 and 9) Canada fleabane have been confirmed in 30 and 23 counties in Ontario, respectively. The widespread incidence of herbicide-resistant Canada fleabane highlights the importance of developing integrated weed management strategies. One strategy is to suppress Canada fleabane using cover crops. Seventeen different cover crop monocultures or polycultures were seeded after winter wheat harvest in late summer to determine GR Canada fleabane suppression in corn grown the following growing season. All cover crop treatments seeded after wheat harvest suppressed GR Canada fleabane in corn the following year. At 4 wk after cover crop emergence (WAE), estimated cover crop ground cover ranged from 31% to 68%, a density of 124 to 638 plants m–2, and a range of biomass from 29 to 109 g m–2, depending on cover crop species. All of the cover crop treatments suppressed GR Canada fleabane in corn grown the following growing season from May to September compared to the no cover crop control. Among treatments evaluated, annual ryegrass (ARG), crimson clover (CC)/ARG, oilseed radish (OSR)/CC/ARG, and OSR/CC/cereal rye (CR) were the best treatments for the suppression of GR Canada fleabane in corn. ARG alone or in combination with CC provided the most consistent GR Canada fleabane suppression, density reduction, and biomass reduction in corn. Grain corn yields were not affected by the use of the cover crops evaluated for Canada fleabane suppression.
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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) | 0.001 | 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".