{"id":"W4253990414","doi":"10.1504/ijmso.2018.096454","title":"Ontology of folktales in the Greater Mekong Subregion","year":2018,"lang":"en","type":"article","venue":"International Journal of Metadata Semantics and Ontologies","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Humanities Research Group, University of Windsor; Khon Kaen University","keywords":"Ontology; Upper ontology; Documentation; Scope (computer science); Computer science; Domain (mathematical analysis); Ontology-based data integration; Process ontology; Digital library; Field (mathematics); Suggested Upper Merged Ontology; Information retrieval; Data science; Knowledge management; Linguistics; Semantic Web; Epistemology; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006294525,0.00007019471,0.0001707469,0.0001905175,0.00002508395,0.0001432865,0.001564916,0.00004429502,0.000001120032],"category_scores_gemma":[0.0002630979,0.00003950562,0.00004055169,0.00008503238,0.0001976534,0.0008017762,0.0002240737,0.0001234656,2.452229e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001197008,"about_ca_system_score_gemma":0.00002996927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005899661,"about_ca_topic_score_gemma":0.0001750629,"domain_scores_codex":[0.9991289,0.00007673037,0.0003116969,0.00009822501,0.0002955328,0.00008887627],"domain_scores_gemma":[0.9989877,0.0001606305,0.0003146001,0.0001728698,0.0003515803,0.00001260327],"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":[0.000224511,0.0002279245,0.05892163,0.00005560632,0.0003974628,0.001026935,0.007994169,0.000002606163,0.007772276,0.7865618,0.002875239,0.1339398],"study_design_scores_gemma":[0.002982209,0.002254273,0.1850433,0.0009875704,0.000185923,0.01112437,0.00293503,0.006584537,0.1388197,0.6373197,0.01096846,0.0007947957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4735782,0.006288951,0.5039054,0.01514534,0.0007780562,0.00007894224,0.000004690911,0.0000345499,0.0001857734],"genre_scores_gemma":[0.8856187,0.0001996185,0.1138926,0.0002082854,0.00006960787,4.625356e-7,5.820433e-7,0.000001732526,0.000008411726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4120405,"threshold_uncertainty_score":0.290803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03199908701069654,"score_gpt":0.3192780661648376,"score_spread":0.2872789791541411,"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."}}