{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03729006,0.001434763,0.0008404712,0.003021774,0.004487307,0.0169371,0.003046614,0.003398989,0.003322873],"category_scores_gemma":[0.1117425,0.001053064,0.0013357,0.002013333,0.01855128,0.02651632,0.01088962,0.005960219,0.0007019078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002771929,"about_ca_system_score_gemma":0.00376665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002034399,"about_ca_topic_score_gemma":0.001995139,"domain_scores_codex":[0.9692467,0.02314403,0.0012227,0.001513565,0.003923347,0.0009496456],"domain_scores_gemma":[0.8760719,0.1094016,0.002958241,0.005834377,0.004199644,0.00153414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002199516,0.0001657961,0.01002376,0.001940709,0.0000921536,0.001625061,0.4736072,0.009099822,0.004424071,0.3234864,0.008364468,0.1669507],"study_design_scores_gemma":[0.00008529691,0.0001604715,0.002390566,0.001850889,0.00007556131,0.001464423,0.1229757,0.01945862,0.00539504,0.7305365,0.1154011,0.0002058216],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1374462,0.004193355,0.7833055,0.03477215,0.000407814,0.0004364633,0.0001503022,0.0009732744,0.03831496],"genre_scores_gemma":[0.7240058,0.002495419,0.2680792,0.001432695,0.0001419926,0.0003523889,0.0001228395,0.0005715175,0.002798212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03729006,"threshold_uncertainty_score":0.1972111,"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."}}