Glyphosate-Resistant Crops and Weeds: Now and in the Future
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
Glyphosate-resistant (GR) crops represent more than 80% of the 120 million ha of transgenic crops grown annually worldwide. GR crops have been rapidly adopted in soybean, maize, cotton, canola, and sugarbeet in large part because of the economic advantage of the technology, as well as the simple and superior weed control that glyphosate delivers. Furthermore, the GR crop/glyphosate technology is generally more environmentally benign than the weed management technologies that it replaced. In the Americas, except for Canada, adoption has meant continuous and intense selection pressure with glyphosate, resulting in evolution of GR weeds and shifts to weed species that are only partially controlled by glyphosate. This development is jeopardizing the benefits of this valuable technology. New transgenic crops with resistance to other herbicide classes -- in some cases coupled with glyphosate resistance -- will be introduced soon. If used wisely, these tools can be integrated into resistance management and prevention strategies. Greater diversity in weed management technologies is badly needed to preserve the utility of the GR crop/ glyphosate technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 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 it