{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004884225,0.000235332,0.0004702072,0.0006142006,0.0002017548,0.00010525,0.0005440486,0.000533411,0.00005863154],"category_scores_gemma":[0.001220697,0.0001644251,0.00007757267,0.0003712627,0.002854093,0.00004116835,0.0004882996,0.0008013648,0.00001215362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003894024,"about_ca_system_score_gemma":0.0005884717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001651265,"about_ca_topic_score_gemma":0.00001101534,"domain_scores_codex":[0.994912,0.00008561644,0.002095805,0.0001027587,0.00213521,0.0006685523],"domain_scores_gemma":[0.9974808,0.0002219258,0.0005205655,0.000257351,0.0005883694,0.0009310155],"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":[0.0005968969,0.0006746916,0.004749354,0.002367235,0.000513103,0.0001122974,0.01093121,0.0000169373,0.03239473,0.001726404,0.2183588,0.7275583],"study_design_scores_gemma":[0.008335934,0.009974547,0.006275607,0.000922377,0.00009896177,0.004148501,0.02880555,0.01499538,0.003558458,0.003335784,0.9186656,0.000883267],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772904,0.001779764,0.01194147,0.002239518,0.0004508888,0.0002591943,0.00001997064,0.00001217711,0.006006655],"genre_scores_gemma":[0.9440447,0.01272963,0.03661048,0.001757408,0.003645505,0.00000868806,0.0001803773,0.00005228803,0.0009709195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.726675,"threshold_uncertainty_score":0.9998596,"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."}}