The potential benefits, risks and costs of genetic use restriction technologies
Bibliographic record
Abstract
Genetic use restriction technologies (GURTs) are designed to restrict access to genetic materials and their associated phenotypic traits. Originally GURTs were developed to ensure that new crop varieties could be protected against unauthorized use, but recently there has been interest in the use of GURTs to facilitate novel trait confinement. There is controversy over the potential use of GURTs in food and feed plant varieties. Considerable discourse exists amongst many groups representing both public and private, and government and non-government interests, about whether GURTs should be adopted based on the potential benefits versus the potential risks and costs. Potential benefits include intellectual property rights protection, stimulation of private crop breeding research and development, enhancement of genetic diversity in breeding programs, and novel trait confinement. Potential risks and costs associated with GURTs include intra- and interspecific escape of the technology, reduced access and increased cost of genetic material for breeders, increased regulation, liability risks in the event of GURT failure or escape, increased seed costs for farmers, further limits on access to novel genetic material for farmers, greater industrial control over agriculture, and a further decrease in agro-biodiversity. Although topical and controversial, the potential benefits versus the potential risks and costs of implementing GURTs are difficult to adequately assess because they are in the developmental stage and there has been no known field-based testing to-date. Until the results of peer-reviewed research on the environmental, social, economic and political impacts of GURTs are publicly available, no fair and useful assessment for the commercial release of the technology can occur. Key words: Genetic use restriction technology, plants with novel traits, genetically modified, genetically engineered, plant breeding
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".