Herbicide-resistant crops as weeds in North America.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 | 0.001 |
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 it