The $1000 Genome: Ethical and Legal Issues in Whole Genome Sequencing of Individuals
Bibliographic record
Abstract
Progress in gene sequencing could make rapid whole genome sequencing of individuals affordable to millions of persons and useful for many purposes in a future era of genomic medicine. Using the idea of $1000 genome as a focus, this article reviews the main technical, ethical, and legal issues that must be resolved to make mass genotyping of individuals cost-effective and ethically effective. It presents the case for individual ownership of a person's genome and its formation, and shows the implications of that position for rights to informed consent and privacy over sequencing, testing, and disclosing genomic information about identifiable individuals. Legal recognition of a person's right to control his or her genome and the information that it contains is essential for further progress in applying genomic discoveries to human lives.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.131 | 0.058 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".