Herbicide-resistant crops as weeds in North America.
Bibliographic record
Abstract
Abstract Growers have rapidly adopted transgenic herbicide-resistant (HR) crops, such as canola ( Brassica napus L.), soyabean [ Glycine max (L.) Merr.], maize ( Zea mays L.) and cotton ( Gossypium hirsutum L.), across North America (USA and Canada) since their commercial introduction in the 1990s. With their widespread cultivation, increasing attention is focused on management of HR volunteers in crops that follow in rotation. In this review, we describe the impact and management of HR crop volunteers in different agroecosystems in North America. The relative risks of planting HR crops and subsequent potential for volunteerism of these crops are assessed. HR volunteers are common weeds and the relative weediness depends on species, genotype, seed shatter prior to harvest and disbursement of seed at harvest, management practices, and environment. Chemical control options may be more limited if the crop volunteers are HR. There are generally no marked changes in volunteer weed problems associated with these crops, except in no-tillage systems when glyphosate (GLY) is used alone to control volunteers. The increasing use of GLY in North American cropping systems, spurred by increasing area and frequency in rotation of GLY - HR crops, may require increased alternative herbicide use or other novel tactics to control GLY-HR crop volunteers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".