Bibliographic record
Abstract
Genetically modified organisms (GMOs), alternatively called biotech crops, dominate soybean and cotton production and are rapidly increasing their fraction of market share for maize and rice in the U.S. Engineered canola is important in Canada, soybeans are dominant in Argentina and Brazil, and cotton is prominent in China and India. Adoption is much slower elsewhere, in large part due to concerns for potential ecosystem effects that may occur through development of weedy plants, by selection of herbicide resistant weeds and by effects of insecticidal proteins on nontarget insects. The precautionary principle is invoked by critics concerned that one must know in advance the effects of GMOs before releasing them. Alteration of weed species composition of agricultural fields is well documented to occur under herbicide selection pressure. Gene flow to wild relatives of crop plants can be shown under herbicide selection, and one instance (sunflower) is provided for insect resistance transfer leading to increased seed production by a weedy relative. Detailed stewardship programs have been developed by seed producers to minimize risks of gene flow. Although herbicides and insecticides are known to have major effects on agroecosystems, the ecosystem impacts of GMOs per se, thus far appear to be small. Key words: gene-flow, herbicide-resistant weeds, genetically engineered crops, Bt maize, Roundup Ready soybeans
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".