{"id":"W1543782282","doi":"10.1089/omi.2006.10.185","title":"National Center for Biomedical Ontology: Advancing Biomedicine through Structured Organization of Scientific Knowledge","year":2006,"lang":"en","type":"article","venue":"OMICS A Journal of Integrative Biology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Human Genome Research Institute","keywords":"Ontology; Biomedicine; Computer science; Dissemination; Open Biomedical Ontologies; Context (archaeology); Resource (disambiguation); Knowledge management; Data science; Upper ontology; World Wide Web; Quality (philosophy); Semantic Web; Suggested Upper Merged Ontology; Bioinformatics","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":[0.0005257669,0.0001571746,0.0003682601,0.0001889945,0.00008211561,0.00001012251,0.0002627851,0.0002979423,0.00001803162],"category_scores_gemma":[0.001582799,0.0001011004,0.0001230729,0.0002707418,0.001008355,0.000006634241,0.00005685505,0.0001353891,7.614449e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005437788,"about_ca_system_score_gemma":0.000449277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008651985,"about_ca_topic_score_gemma":0.00005957281,"domain_scores_codex":[0.9987367,0.00009423845,0.0006075521,0.0002257443,0.0001180182,0.0002177665],"domain_scores_gemma":[0.9976599,0.0001211276,0.0005247256,0.0001026009,0.001539253,0.00005241235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001936748,0.000241793,0.004622601,0.00002440456,0.0001375499,0.000001286938,0.0002075491,0.000004056083,0.9664029,0.006812103,0.01643942,0.00491264],"study_design_scores_gemma":[0.004729817,0.003541905,0.002940605,0.000139917,0.0000743173,0.0003247062,0.0008646572,0.0001733515,0.5172005,0.01489577,0.4547881,0.0003263879],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8004605,0.002090266,0.1932342,0.00108804,0.002121966,0.0001932526,0.0003580835,0.000008637447,0.0004450179],"genre_scores_gemma":[0.9679149,0.0000753904,0.03035553,0.00009840266,0.0006309539,0.0000031648,0.000766602,0.00001393395,0.0001411401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4492024,"threshold_uncertainty_score":0.4122756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310860981620077,"score_gpt":0.3132670196152181,"score_spread":0.3001584097990173,"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."}}