{"id":"W2062289613","doi":"10.1111/j.1467-8519.2004.00376.x","title":"The Inevitability of Genetic Enhancement Technologies","year":2004,"lang":"en","type":"article","venue":"Bioethics","topic":"Neuroethics, Human Enhancement, Biomedical Innovations","field":"Neuroscience","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Dalhousie University; Associated Medical Services","keywords":"Human enhancement; Genetic engineering; Emerging technologies; Bioethics; Engineering ethics; Environmental ethics; Epistemology; Sociology; Psychology; Political science; Computer science; Biology; Law; Philosophy; Genetics; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006110124,0.0001302409,0.000132784,0.00007382368,0.0003711475,0.00003617433,0.00068737,0.0001653598,0.0000206728],"category_scores_gemma":[0.006385251,0.0000916,0.00005001537,0.0006932148,0.004982446,0.00006005774,0.0002694951,0.0006096098,0.00004849814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007650349,"about_ca_system_score_gemma":0.000182166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002664236,"about_ca_topic_score_gemma":0.00004007362,"domain_scores_codex":[0.998171,0.00009130614,0.0004987242,0.0003588652,0.00058287,0.0002972445],"domain_scores_gemma":[0.9980854,0.000799191,0.0002193893,0.0007123352,0.0001528768,0.00003083745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001169577,0.0001949432,0.00009874292,0.00004859747,0.000004686141,0.000003276213,0.0002474723,0.00001114037,0.6570208,0.3345232,0.0001083257,0.00772717],"study_design_scores_gemma":[0.0001896549,0.0001525114,0.0003532985,0.00002423383,0.000004719101,0.000001987476,0.0001895698,0.000009050732,0.8720537,0.1208036,0.006126216,0.00009141341],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8525686,0.0005024623,0.02788592,0.1128147,0.001600531,0.001201663,0.00004002517,0.0006351643,0.002750998],"genre_scores_gemma":[0.9961301,0.0007989231,0.00196381,0.0009007511,0.00002665412,0.00004744318,6.663706e-7,0.00001120107,0.0001204207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.215033,"threshold_uncertainty_score":0.9977254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1170579501904245,"score_gpt":0.3621847435672438,"score_spread":0.2451267933768194,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}