Establishing an incentive-based multistakeholder approach to dual-use DNA screening
Bibliographic record
Abstract
Fast, accessible, and high-quality deoxyribonucleic acid (DNA) is fundamental to advancement in the life sciences that will drive forward fields such as agriculture, energy, and medicine. Despite their importance in accelerating global progress, bioscience research and biotechnologies can also be misused, endangering humans, animals, and the environment. The ability to accidentally or deliberately endow or enhance the pathogenicity of biological systems is of particular concern. Access to DNA sequences with a clear potential for dual use should be limited to responsible and identifiable groups with legitimate uses. Yet, none of the 195 countries party to the International Health Regulations have national laws that mandate this type of screening. Many DNA providers voluntarily screen orders and absorb increased costs, but this practice is not universally adopted for a variety of reasons. This article explores the incentives and regulatory structures that can bring the screening coverage of DNA orders toward 100%, which may include expedited orders for approved customers, better tools and technology for more efficient screening, funding requirements that grantees use screened DNA, and early education in biosecurity aimed at researchers and students. Ultimately, an incentive-based multistakeholder approach to DNA screening can benefit researchers, industry, and global health security.
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 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 | 0.051 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".