{"id":"W3118700037","doi":"10.3390/mti5010002","title":"Forming Cognitive Maps of Ontologies Using Interactive Visualizations","year":2021,"lang":"en","type":"article","venue":"Multimodal Technologies and Interaction","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ontology; Computer science; Visualization; Human–computer interaction; Process ontology; Ontology-based data integration; Upper ontology; Set (abstract data type); Cognition; Interactive visualization; Data science; Domain knowledge; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006283343,0.0001047544,0.0001632645,0.0001870176,0.0001084733,0.0001087884,0.0001841146,0.00008935516,0.000009197152],"category_scores_gemma":[0.000706954,0.00009841048,0.00004261901,0.0004064573,0.00008659477,0.001064553,0.0004218119,0.0001291157,0.000002296724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004072214,"about_ca_system_score_gemma":0.00003337151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004964786,"about_ca_topic_score_gemma":0.00002438665,"domain_scores_codex":[0.9992365,0.0000328756,0.0002403393,0.0002594294,0.0001012043,0.0001296426],"domain_scores_gemma":[0.9991682,0.0001429019,0.0001789568,0.0001924544,0.0003004142,0.00001703306],"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":[0.00005119145,0.0005523298,0.006500959,0.0001400661,0.000233918,0.00004426211,0.002569619,0.000457804,0.04105471,0.1736696,0.0004082179,0.7743173],"study_design_scores_gemma":[0.0004382499,0.0001139794,0.0005600309,0.0002810247,0.00002972012,0.0000864274,0.02726633,0.7213133,0.24315,0.004599981,0.001922426,0.0002385442],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08993613,0.0001656947,0.9082475,0.0002700837,0.0002258925,0.00009040193,0.00003044852,0.0003727377,0.0006611198],"genre_scores_gemma":[0.9743086,0.0002286664,0.02531121,0.00004839744,0.000007431292,0.000004818711,0.00004128608,0.000005271505,0.0000443104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8843725,"threshold_uncertainty_score":0.4013063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04643971259994645,"score_gpt":0.3590202224822334,"score_spread":0.312580509882287,"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."}}