{"id":"W3020822728","doi":"10.2196/17176","title":"Data Integration in the Brazilian Public Health System for Tuberculosis: Use of the Semantic Web to Establish Interoperability","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Interoperability; Computer science; Semantic interoperability; Semantic Web; World Wide Web; System integration; Data science; Knowledge management; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002065786,0.0001167865,0.0002743986,0.00005811026,0.0000875084,0.0003028934,0.003612275,0.00007970164,0.000002900326],"category_scores_gemma":[0.002886386,0.00005789549,0.00005511437,0.0007425073,0.00009933118,0.001300012,0.0009772981,0.0002687911,0.000006746457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005896526,"about_ca_system_score_gemma":0.0004319241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006836073,"about_ca_topic_score_gemma":0.000561078,"domain_scores_codex":[0.9977399,0.0002343791,0.0008806305,0.0001675608,0.0006955575,0.0002820125],"domain_scores_gemma":[0.9977735,0.0004850278,0.0001941373,0.00125351,0.000107188,0.0001865799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000543397,0.0005841088,0.01168301,0.005466798,0.00008352107,0.00000536032,0.3136224,0.00007270151,0.00003381434,0.06053422,0.1736253,0.4342344],"study_design_scores_gemma":[0.0004113525,0.0002011981,0.00606873,0.0004619074,0.000005580117,0.00001530982,0.01866929,0.9523854,0.00003421067,0.00005150689,0.02157453,0.0001209246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2852192,0.00003885749,0.4829553,0.228374,0.0006192093,0.002375192,0.00008879886,0.000165808,0.0001636903],"genre_scores_gemma":[0.9759543,0.000007672639,0.00843999,0.01546728,0.0000418705,0.00006494506,0.00001832779,0.000003751253,0.000001877379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9523128,"threshold_uncertainty_score":0.6712567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1095636091011746,"score_gpt":0.3299670796978312,"score_spread":0.2204034705966566,"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."}}