{"id":"W3125361014","doi":"10.3390/informatics8010003","title":"Thai Tattoo Wisdom’s Representation of Knowledge by Ontology","year":2021,"lang":"en","type":"article","venue":"Informatics","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Humanities Research Group, University of Windsor; Khon Kaen University","keywords":"Ontology; Computer science; Automation; Process ontology; Domain (mathematical analysis); Suggested Upper Merged Ontology; Quality (philosophy); Upper ontology; Representation (politics); Knowledge representation and reasoning; Protégé; Ontology-based data integration; Information retrieval; Domain knowledge; Knowledge management; Data science; Artificial intelligence; Engineering; Mathematics; Semantic Web; Political science","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":[0.0002040923,0.00003855201,0.0001157395,0.00004527896,0.0001005477,0.00001101261,0.0002420275,0.0001099348,0.0003019329],"category_scores_gemma":[0.0006081557,0.00004041015,0.00003017874,0.0002819407,0.0003027609,0.0001481345,0.0000769859,0.00009322863,0.00008551947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002739046,"about_ca_system_score_gemma":0.000157655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000226441,"about_ca_topic_score_gemma":0.001390016,"domain_scores_codex":[0.9993419,0.0001027866,0.0002946143,0.00003484061,0.0001118412,0.0001140202],"domain_scores_gemma":[0.9991289,0.0002344617,0.0001488615,0.000284633,0.0001679842,0.00003514404],"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":[0.000008445583,0.0002927425,0.01180578,0.00007015366,0.00006198708,0.000001415314,0.3551263,0.000007265523,0.0008833822,0.3668388,0.07764837,0.1872554],"study_design_scores_gemma":[0.0005092765,0.00002106102,0.0005943152,0.00001692769,0.00001408851,0.000003471683,0.07509688,0.001471849,0.008398257,0.004619977,0.9091319,0.0001219238],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1434699,0.0005632641,0.003908761,0.004687598,0.0003985647,0.0001916298,0.00001312098,0.0001496039,0.8466175],"genre_scores_gemma":[0.990001,0.0005087876,0.007039687,0.0002107742,0.00001679546,0.000009375699,0.00003898071,0.000003102318,0.002171457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8465311,"threshold_uncertainty_score":0.3305954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04248236596258875,"score_gpt":0.3800590985255695,"score_spread":0.3375767325629807,"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."}}