{"id":"W2104990432","doi":"10.1136/amiajnl-2011-000523","title":"The National Center for Biomedical Ontology","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":280,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Common Fund; National Institutes of Health","keywords":"Open Biomedical Ontologies; Ontology; Computer science; Biomedicine; Variety (cybernetics); World Wide Web; Data science; Semantic Web; Process ontology; Resource (disambiguation); Upper ontology; Ontology-based data integration; Analytics; Ontology alignment; Bioinformatics; Artificial intelligence","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":[0.002093364,0.0000744031,0.0001666173,0.00003110589,0.0001474318,0.00001628059,0.0005297406,0.0001330799,0.0000087305],"category_scores_gemma":[0.007016874,0.00003705833,0.0001816431,0.0001090748,0.000386717,0.000005240379,0.00009126699,0.0002215782,0.000002790015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007880968,"about_ca_system_score_gemma":0.0002876297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005010699,"about_ca_topic_score_gemma":0.00001257841,"domain_scores_codex":[0.9982786,0.00009983119,0.000587112,0.00004339701,0.0007812936,0.0002097686],"domain_scores_gemma":[0.9978923,0.0002509085,0.001291301,0.00009132214,0.0003612628,0.0001128693],"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":[0.0006805031,0.0004657862,0.03255371,0.00003424638,0.001084416,0.000001877889,0.0017694,0.000003604596,0.001140355,0.0009751337,0.7291471,0.2321438],"study_design_scores_gemma":[0.001773909,0.001451989,0.02372262,0.00004702862,0.00006234917,0.0001489137,0.001349666,0.001068513,0.001255954,0.00211204,0.9668351,0.0001719594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9173146,0.0003477504,0.04336715,0.03008135,0.003995583,0.0003960043,0.00007801563,0.00002202023,0.004397559],"genre_scores_gemma":[0.9718668,0.0005571692,0.01424521,0.01127512,0.001477236,0.00001766521,0.00002142682,0.00001686008,0.0005224645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2376879,"threshold_uncertainty_score":0.840036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141549515800975,"score_gpt":0.3000184411159791,"score_spread":0.2786029459579693,"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."}}