{"id":"W1972924322","doi":"10.1145/1385569.1385603","title":"An empirical evaluation of interactive visualizations for preferential choice","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Task (project management); Set (abstract data type); Visualization; Empirical research; Process (computing); Outcome (game theory); Choice set; Interactive visualization; Human–computer interaction; Measure (data warehouse); Artificial intelligence; Data mining; Econometrics; Statistics; Mathematics; Engineering","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":[0.02452299,0.001215206,0.0008902145,0.002408694,0.0008254797,0.002604586,0.002040721,0.002036572,0.003585248],"category_scores_gemma":[0.2915041,0.0007395067,0.0007821576,0.001571513,0.001359405,0.004450953,0.002528808,0.001938715,0.0005102117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00103983,"about_ca_system_score_gemma":0.001093621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001265927,"about_ca_topic_score_gemma":0.001374837,"domain_scores_codex":[0.9701386,0.02307545,0.001930787,0.001119896,0.003240069,0.0004952576],"domain_scores_gemma":[0.3189709,0.6466721,0.01132171,0.01431012,0.006613234,0.002111946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.02075165,0.02479613,0.09292511,0.01046515,0.001212631,0.001098296,0.02803434,0.04373771,0.02413968,0.007684845,0.01337761,0.7317768],"study_design_scores_gemma":[0.01322107,0.087169,0.3844039,0.005813782,0.002478892,0.002465817,0.01882715,0.3374467,0.06400505,0.02471922,0.05800334,0.001446116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743738,0.0007540071,0.01782966,0.0004686861,0.00008810643,0.001343102,0.0005823399,0.001207799,0.00335249],"genre_scores_gemma":[0.9464272,0.0004214262,0.04976973,0.0001480531,0.00006621596,0.00168614,0.000569579,0.0002062689,0.000705347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02452299,"threshold_uncertainty_score":0.1296915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1749196764614371,"score_gpt":0.4778877704673096,"score_spread":0.3029680940058725,"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."}}