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 The global area sown to genetically modified (GM) varieties of leading commercial crops (soybean, maize, canola, and cotton) has expanded over 100-fold over two decades. Thirty countries are producing GM crops and just five countries (United States, Brazil, Argentina, Canada, and India) account for almost 90% of the GM production. Only four crops account for 99% of worldwide GM crop area. Almost 100% of GM crops on the market are genetically engineered with herbicide tolerance (HT), and insect resistance (IR) traits. Approximately 70% of cultivated GM crops are HT, and GM HT crops have been credited with facilitating no-tillage and conservation tillage practices that conserve soil moisture and control soil erosion, and that also support carbon sequestration and reduced greenhouse gas emissions. Crop production and productivity increased significantly during the era of the adoption of GM crops; some of this increase can be attributed to GM technology and the yield protection traits that it has made possible even if the GM traits implemented to-date are not yield traits per se. GM crops have also been credited with helping to improve farm incomes and reduce pesticide use. Practical concerns around GM crops include the rise of insect pests and weeds that are resistant to pesticides. Other concerns around GM crops include broad seed variety access for farmers and rising seed costs as well as increased dependency on multinational seed companies. Citizens in many countries and especially in European countries are opposed to GM crops and have voiced concerns about possible impacts on human and environmental health. Nonetheless, proponents of GM crops argue that they are needed to enhance worldwide food production. The novelty of the technology and its potential to bring almost any trait into crops mean that there needs to remain dedicated diligence on the part of regulators to ensure that no GM crops are deregulated that may in fact pose risks to human health or the environment. The same will be true for the next wave of new breeding technologies, which include gene editing technologies.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 it