{"id":"W2120715664","doi":"10.1186/gm220","title":"Genome Medicine: past, present and future","year":2011,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Center for Research Resources; National Institute of Neurological Disorders and Stroke","keywords":"Genomic medicine; Human genetics; Genome Biology; Personalized medicine; Genomics; Genome; Precision medicine; Data science; Translational bioinformatics; Section (typography); Computational biology; Engineering ethics; Medicine; Biology; Bioinformatics; Genetics; Computer science; Engineering; Gene","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":[0.01477946,0.0007028221,0.001271057,0.002511545,0.002476912,0.01026147,0.001557908,0.009583442,0.008861391],"category_scores_gemma":[0.01281643,0.0004250823,0.0006928652,0.002722501,0.009831772,0.01708747,0.003007483,0.01323588,0.002625142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003688254,"about_ca_system_score_gemma":0.006246585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001859998,"about_ca_topic_score_gemma":0.004444266,"domain_scores_codex":[0.9954833,0.001943307,0.0003936181,0.000375614,0.00139052,0.0004136943],"domain_scores_gemma":[0.9812046,0.007888034,0.001121126,0.0009185218,0.003988406,0.004879253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001685907,0.0001084345,0.001958446,0.002494406,0.00007061564,0.0006202165,0.001139642,0.0002086189,0.0008058804,0.07098158,0.5841193,0.3373243],"study_design_scores_gemma":[0.00002147743,0.0000570695,0.001652453,0.001873555,0.00003220806,0.001098808,0.001812882,0.0001489179,0.0001185713,0.04078697,0.9523472,0.00005002117],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.0005563264,0.3006218,0.001288722,0.6513532,0.03895238,0.00000902038,0.00005896922,0.0001016601,0.007057968],"genre_scores_gemma":[0.0280219,0.5623301,0.008071366,0.2836939,0.109491,0.00005584122,0.0001739788,0.0001031982,0.008058767],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01477946,"threshold_uncertainty_score":0.07816219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01246405713817794,"score_gpt":0.2256288279505729,"score_spread":0.2131647708123949,"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."}}