Referee report. For: Taking stock: Is gene drive research delivering on its principles? [version 1; peer review: 1 approved with reservations]
Bibliographic record
Abstract
Gene drive technology has been recognized for its potential to provide durable and cost-effective solutions for previously intractable problems in public health, conservation, and agriculture. In recognition of the rapid advances in this field, in 2016 the U.S. National Academies of Sciences, Engineering, and Medicine issued a report making several recommendations aimed at researchers, funders, and policymakers for the safe and responsible research and development of gene drive technology. Subsequently, in 2017 sixteen global organizations self-identifying as sponsors and supporters of gene drive research became public signatories committed to the ‘Principles for Gene Drive Research’ which were inspired by the report’s recommendations. Herein we reflect on the progress of gene drive research in relation to the ethical principles laid out and committed to by the signatories to the Principles. Our analysis indicates high levels of alignment with the Principles in the field of gene drive research. The manuscript also discusses the Gene Drive Research Forum, which had its genesis in the publication of the Principles. Discussions between participants at the latest meeting of the Forum point to the work that lies ahead for gene drive research in line with the Principles. Going forward the gene drive research community can productively focus on: i) safety and efficacy criteria for open release, ii) risk assessment frameworks and methods, iii) more downstream technical, regulatory and policy considerations for field evaluations and implementation, iv) continued transparency and developing mechanisms of accountability, and v) strengthening capacity in locales of potential release and expected drive spread.
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 | 0.008 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.578 | 0.461 |
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".