{"id":"W4210545313","doi":"10.15353/juhr.v1i1.january.4675","title":"Genetic Engineering - Programmable Humans","year":2022,"lang":"en","type":"article","venue":"University of Waterloo Journal of Undergraduate Health Research","topic":"Neuroethics, Human Enhancement, Biomedical Innovations","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Engineering ethics; Field (mathematics); Risk analysis (engineering); Bioethics; Environmental ethics; Computer science; Biology; Engineering; Business; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001761798,0.0004877462,0.0003571165,0.0004506342,0.0009270916,0.001996117,0.0008811178,0.001216086,0.01181975],"category_scores_gemma":[0.003344007,0.0002663207,0.0005018148,0.0003334529,0.004214181,0.002178594,0.001699548,0.002256008,0.00257235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264675,"about_ca_system_score_gemma":0.001276926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001127889,"about_ca_topic_score_gemma":0.001300622,"domain_scores_codex":[0.9989185,0.0003275577,0.00004049898,0.0002440745,0.0003467468,0.0001225914],"domain_scores_gemma":[0.999044,0.0004152643,0.00009083359,0.0002359921,0.0001264984,0.00008739065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001172378,0.00006157771,0.0006498836,0.0003124182,0.00002317181,0.0002857416,0.001226117,0.001444313,0.02945233,0.8618655,0.01342729,0.09113436],"study_design_scores_gemma":[0.00006291694,0.0002648654,0.0007746909,0.0003655308,0.00003890989,0.001114436,0.0004408395,0.00175383,0.02997889,0.2228069,0.7423382,0.00005989125],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05831994,0.01704733,0.3600677,0.01924289,0.003862366,0.0005168797,0.0006814189,0.003052647,0.5372088],"genre_scores_gemma":[0.5304676,0.02234239,0.2425114,0.01059781,0.0005926739,0.0009855414,0.000788244,0.0009411104,0.1907733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01181975,"threshold_uncertainty_score":0.03954101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1890730305343867,"score_gpt":0.3678784401607569,"score_spread":0.1788054096263702,"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."}}