{"id":"W2767154063","doi":"10.1109/iccsnt.2016.8070282","title":"Semantic ontology of knowledge on ethnic groups in Thailand","year":2016,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Humanities Research Group, University of Windsor","keywords":"Ontology; Computer science; Upper ontology; Ontology-based data integration; Ethnic group; Body of knowledge; Scope (computer science); Process ontology; Knowledge-based systems; Knowledge extraction; Suggested Upper Merged Ontology; Information retrieval; Knowledge management; Natural language processing; Semantic Web; Artificial intelligence; Sociology; Epistemology; Anthropology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008165105,0.0002795738,0.0002283901,0.003405876,0.002115818,0.004098812,0.0004873584,0.0004725339,0.001850441],"category_scores_gemma":[0.001367604,0.0001522251,0.0005448532,0.005642735,0.003620533,0.004587871,0.002076624,0.000744512,0.0002445555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002685552,"about_ca_system_score_gemma":0.005264387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02631844,"about_ca_topic_score_gemma":0.01847762,"domain_scores_codex":[0.9990246,0.0003926194,0.0001059429,0.0001061336,0.0002702742,0.0001004629],"domain_scores_gemma":[0.9993767,0.0002198034,0.00009846673,0.00007377168,0.0001634706,0.0000678011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000041336,0.00004345647,0.006104011,0.0004121443,0.0000411873,0.001426139,0.02425947,0.003968094,0.00148229,0.898486,0.003413798,0.06032202],"study_design_scores_gemma":[0.00002808537,0.00003056528,0.01569926,0.0009652433,0.0001426478,0.001769515,0.04952908,0.01495641,0.002383173,0.6269017,0.2875085,0.00008578296],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.2749898,0.003794125,0.2503632,0.005112578,0.0002923511,0.0004035551,0.002695186,0.0003335384,0.4620158],"genre_scores_gemma":[0.9289159,0.002866545,0.05478471,0.0002970963,0.00005096853,0.0001637656,0.001576891,0.00005011812,0.01129417],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02631844,"threshold_uncertainty_score":0.05233049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04029616321591908,"score_gpt":0.2915557632777264,"score_spread":0.2512596000618074,"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."}}