Available online at www.internationalscholarsjournals.org © International Scholars Journals Review Genetic engineering, ecosystem change, and
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.272 | 0.118 |
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".