{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003353536,0.001261662,0.0005653891,0.003525031,0.0009632863,0.005258559,0.001525085,0.001328502,0.00927844],"category_scores_gemma":[0.01845775,0.0005178218,0.001148713,0.001664603,0.001345849,0.005854416,0.005529949,0.001322726,0.0008942331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007289547,"about_ca_system_score_gemma":0.000865976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001402081,"about_ca_topic_score_gemma":0.001747614,"domain_scores_codex":[0.9985477,0.0007664871,0.00008857092,0.0001838659,0.0003056412,0.0001076864],"domain_scores_gemma":[0.9883723,0.00895657,0.0005091394,0.00113153,0.0006771082,0.0003533463],"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":[0.001398738,0.0004677733,0.01238549,0.003738204,0.0003106427,0.002330404,0.1111232,0.04547022,0.06187994,0.1628794,0.03304211,0.5649739],"study_design_scores_gemma":[0.0005327153,0.0007648965,0.01947057,0.002132652,0.0003275835,0.002545105,0.02526879,0.2754461,0.05363199,0.2630793,0.3562697,0.0005305954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09976549,0.000604784,0.8629494,0.001472235,0.0001108657,0.0004520802,0.001253035,0.01269489,0.02069717],"genre_scores_gemma":[0.3722034,0.0006044467,0.6215006,0.0001845554,0.00005157896,0.0008105855,0.0008958423,0.0009213892,0.00282769],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00927844,"threshold_uncertainty_score":0.03103948,"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."}}