{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001780499,0.000213737,0.0002504662,0.00007142876,0.00008963954,0.000004287666,0.0002195793,0.0001136812,0.0006101471],"category_scores_gemma":[0.00001262525,0.0001504699,0.00004257602,0.00008183645,0.0003066363,0.000002420333,0.0001411511,0.00007988823,0.00001499394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008796778,"about_ca_system_score_gemma":0.00003204379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006075346,"about_ca_topic_score_gemma":0.00000590262,"domain_scores_codex":[0.9988592,0.00003446013,0.0002540023,0.0004092579,0.0001514483,0.0002915888],"domain_scores_gemma":[0.999127,0.000005979663,0.00008383189,0.0004212741,0.00008102413,0.0002808537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005043964,0.0002347771,0.009851735,0.0002552183,0.000450237,0.0002652103,0.005307792,0.000005154396,0.940697,0.0006037012,0.02615847,0.01566635],"study_design_scores_gemma":[0.001125101,0.0008815126,0.1727701,0.00001380931,0.00008667038,0.0001101283,0.00102962,0.000002521011,0.0005707695,0.0002764119,0.8228959,0.0002374193],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9189957,0.05084566,0.0002077534,0.006118663,0.0006968975,0.0004295433,0.00003928215,0.00003021041,0.02263627],"genre_scores_gemma":[0.9777162,0.004533286,0.0002317875,0.001162183,0.01435285,0.00002529826,0.0003348585,0.00004012947,0.001603368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9401262,"threshold_uncertainty_score":0.6680683,"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."}}