Use of Herbicide‐Tolerant Crops as a Component of an Integrated Weed Management Program
Bibliographic record
Abstract
Integrated weed management (IWM) advocates the use of a combination of preventive, cultural, mechanical, and chemical tools to keep weed pressure below threshold levels that reduce yields and profits. Herbicide‐tolerant crops (HTCs) represent a relatively new weed control technology that can be used in an IWM program and have been readily adopted by farmers in the US and Canada. HTCs enhance weed control options and greatly expand market demand for certain herbicides. HTCs provide many benefits to the producers and to the companies that own the intellectual property rights to this technology. However, HTCs should be considered only as one component of an IWM approach that also utilizes other management tools to ensure the long‐term benefits of a profitable and environmentally sound weed management program. Widespread use and over‐reliance on HTCs without the benefit of an integrated weed management program can result in the development of herbicide‐tolerant weeds or a shift in weed populations dominated by species that are more tolerant of the herbicide in question. Therefore, our objective is to provide a brief overview of advantages and disadvantages regarding the use of HTCs in order to help those involved in weed management at the field level be aware of both benefits and risks associated with this technology.
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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 | 0.000 |
| 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".