The Need for Ethical and Experimental Regulation in Genome Editing
Bibliographic record
Abstract
EditingDr. Robert Edwards (Fig. 1A), the 2010 Nobel Prize in Physiology and Medicine recipient for the development of in vitro fertilization (IVF) research became infamous as the media labeled his work with the term "test tube babies".IVF stirred panic and heated ethical debate, even prompting withdrawal of funds for Edwards' research [1].Nevertheless, today IVF continues to help make parenthood a reality for many people worldwide [2] and its research led to multiple discoveries on the morphology and physiology of developmental diseases [2].We are probably facing a similar predicament today as the term "test tube babies" transitioned to "designer babies" with developments in gene editing [3].Gene editing refers to the manipulation of genes externally and incorporation of those genes into the genome of human somatic cells using viral-vector based delivery to correct rare genetic diseases.Gene editing is responsible for many breakthroughs including a blindness cure for those su↵ering from inherited retinal dystrophy [4].However, most recently, the work of Dr. Jiankui He (Fig. 1B) has sparked further debate on the ethics of gene editing for the first human embryo experimentation involving CRISPR-Cas9 gene editing [5].
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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 | 0.238 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.026 | 0.056 |
| Insufficient payload (model declined to judge) | 0.004 | 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".