Benefits of genome-edited crops: expert opinion
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
Innovation in agriculture is pervasive. However, in spite of the success stories of twentieth century plant breeding, the twenty-first century has ushered in a set of challenges that solutions from the past century are unlikely to address. However, sustained research and the amalgamation of a number of disciplines has resulted in new breeding techniques (NBTs), such as genome editing, which offer the promise of new opportunities to resolve some of the issues. Here we present the results of an expert survey on the added potential benefits of genome-edited crops compared to those developed through genetic modification (GM) and conventional breeding. Overall, survey results reveal a consensus among experts on the enhanced agronomic performance and product quality of genome-edited crops over alternatives. The majority of experts indicated that the regulations for health and safety, followed by export markets, consumers, and the media play a major role in determining where and how NBTs, including genome editing, will be developed and used in agriculture. Further research is needed to gauge expert opinion after the Court of Justice of the European Union ruling establishing that site-specific mutagenic breeding technologies are to be regulated in the same fashion as GM crops, regardless of whether foreign DNA is present in the final variety.
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 | 0.000 | 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) | 0.000 | 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