{"id":"W2144427809","doi":"10.1186/2041-1480-5-14","title":"The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Semantics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":272,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Ontario Institute for Cancer Research; Carleton University","funders":"Instituto de Salud Carlos III; Natural Sciences and Engineering Research Council of Canada; National Science Foundation; University of Texas at El Paso; Canarie; European Federation of Pharmaceutical Industries and Associations; National Aeronautics and Space Administration","keywords":"Computer science; Ontology; License; Simple (philosophy); Semantic Web; Data science; World Wide Web; Open Biomedical Ontologies; Information retrieval; Ontology-based data integration; Suggested Upper Merged Ontology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006278325,0.0007774482,0.0009888267,0.0064556,0.002473816,0.006231953,0.002078126,0.002322368,0.005008464],"category_scores_gemma":[0.008732744,0.0007536242,0.002310559,0.007207863,0.003157814,0.01041476,0.006162736,0.003455842,0.003500776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003677141,"about_ca_system_score_gemma":0.01232822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005364327,"about_ca_topic_score_gemma":0.005855929,"domain_scores_codex":[0.99555,0.00115123,0.0008607197,0.0005617344,0.001580947,0.0002953722],"domain_scores_gemma":[0.994873,0.001572869,0.0005043746,0.001433183,0.001088894,0.0005276478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003346768,0.00006221025,0.0005294012,0.0005039067,0.0000648713,0.0002020566,0.0006402818,0.001200686,0.001945497,0.8858083,0.03858476,0.07042453],"study_design_scores_gemma":[0.00002712851,0.00002236234,0.0005389592,0.0004905508,0.0000547664,0.0005097276,0.0003101719,0.007335669,0.001429745,0.3762624,0.6129711,0.00004726317],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002943728,0.002625762,0.9216031,0.006906926,0.001106561,0.0007151,0.006366709,0.004321621,0.05341048],"genre_scores_gemma":[0.04831728,0.005056381,0.9077468,0.003244189,0.0008599639,0.001410921,0.01958364,0.001040599,0.01274013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0064556,"threshold_uncertainty_score":0.0332033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04075967433146419,"score_gpt":0.3676510272121369,"score_spread":0.3268913528806727,"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."}}