{"id":"W4387891525","doi":"10.1109/tvcg.2023.3326571","title":"Designing for Ambiguity in Visual Analytics: Lessons from Risk Assessment and Prediction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sensemaking; Visual analytics; Ambiguity; Computer science; Analytics; Visualization; Cultural analytics; Data science; Data visualization; Human–computer interaction; Interactive visual analysis; Knowledge management; Semantic analytics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005131867,0.0001998965,0.0002289281,0.0007514028,0.0003987087,0.0002990715,0.0001926132,0.0001228407,0.000003627981],"category_scores_gemma":[0.000008484032,0.0002136588,0.00006661966,0.001433441,0.00006431204,0.0005285222,0.00001274105,0.0001742407,0.000002847814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003840428,"about_ca_system_score_gemma":0.00006200125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006060935,"about_ca_topic_score_gemma":0.0001051519,"domain_scores_codex":[0.9983374,0.0001765276,0.0003969853,0.0005694608,0.0002776557,0.000241915],"domain_scores_gemma":[0.9990752,0.0003003478,0.0001286649,0.0002406418,0.0001271219,0.0001280671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008634393,0.001479483,0.02203252,0.0002110743,0.0004494757,0.00001838127,0.004740991,0.03478626,0.0002508188,0.8045736,0.002502286,0.1288688],"study_design_scores_gemma":[0.0009093248,0.000200159,0.01480723,0.00005441382,0.00004195351,0.000001543944,0.00008621091,0.9797052,0.0003387967,0.003330763,0.0003206429,0.0002037435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01559206,0.0000161995,0.9830731,0.0001266187,0.0004061732,0.000276674,0.0001678777,0.000334121,0.000007240039],"genre_scores_gemma":[0.9898407,0.001549282,0.007716505,0.0005622783,0.0000652295,0.0000509893,0.0001594956,0.00002530418,0.00003018505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9753565,"threshold_uncertainty_score":0.8712753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04540525373415249,"score_gpt":0.3542721706343093,"score_spread":0.3088669169001568,"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."}}