{"id":"W2140813273","doi":"10.1016/j.jbi.2006.10.002","title":"Bio*Medical informatics and genomic medicine: Research and training","year":2006,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"U.S. National Library of Medicine; National Human Genome Research Institute","keywords":"Genomic medicine; Health informatics; Informatics; Computer science; Precision medicine; Translational bioinformatics; Training (meteorology); Data science; Medicine; Medical education; Genomics; Computational biology; Biology; Genome; Genetics; Pathology; Engineering; Public health; 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":[0.01666163,0.001063263,0.0007365043,0.002355499,0.001833164,0.006966025,0.001116509,0.002523897,0.02592169],"category_scores_gemma":[0.01847354,0.0005183104,0.0003056559,0.002472207,0.004727032,0.008023432,0.00473922,0.006414287,0.01263535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002502036,"about_ca_system_score_gemma":0.009384075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001404546,"about_ca_topic_score_gemma":0.00195893,"domain_scores_codex":[0.9963549,0.001812197,0.0002182295,0.0003101161,0.00100892,0.0002957174],"domain_scores_gemma":[0.9677005,0.01096626,0.0009134163,0.003306749,0.005562561,0.01155048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007017764,0.0004085692,0.003574655,0.0004121334,0.00001539073,0.00006362901,0.001611591,0.0006220471,0.001285113,0.2013339,0.2520401,0.5385627],"study_design_scores_gemma":[0.00003696109,0.0002069855,0.005467084,0.001099217,0.00001241248,0.0005194487,0.001042879,0.003336672,0.001319,0.1518937,0.8350309,0.00003459589],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.01594243,0.07157441,0.09527683,0.5923621,0.0237965,0.0002942554,0.0003856286,0.0009613526,0.1994065],"genre_scores_gemma":[0.2432369,0.1277021,0.1521952,0.08126821,0.03967532,0.0005969649,0.001339241,0.0007987162,0.3531874],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02592169,"threshold_uncertainty_score":0.08811617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04023676088796305,"score_gpt":0.3432184090250433,"score_spread":0.3029816481370803,"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."}}