{"id":"W2734916492","doi":"10.1145/3079628.3079634","title":"Impact of Individual Differences on User Experience with a Real-World Visualization Interface for Public Engagement","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visualization; Computer science; Human–computer interaction; Information visualization; Perception; User interface; Eye tracking; Usability; User experience design; User satisfaction; Data visualization; User interface design; Multimedia; Artificial intelligence; Psychology","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":[0.003691185,0.0003370437,0.0003020873,0.0004525524,0.0003870962,0.001897379,0.0004218273,0.0005799068,0.008845816],"category_scores_gemma":[0.03906012,0.000162772,0.0003693675,0.0002620566,0.0004303677,0.0008802535,0.0009160641,0.0004084886,0.0007009262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002483868,"about_ca_system_score_gemma":0.0001772555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006388169,"about_ca_topic_score_gemma":0.000792518,"domain_scores_codex":[0.9972889,0.001494765,0.0002215348,0.0002962078,0.000502742,0.000195831],"domain_scores_gemma":[0.9750472,0.01991507,0.001291876,0.001496791,0.001108269,0.001140763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01350391,0.008338783,0.4587418,0.001913639,0.0009317984,0.001031216,0.07877401,0.004044694,0.1030716,0.001103152,0.006443521,0.3221018],"study_design_scores_gemma":[0.0003535483,0.009536274,0.9378218,0.0002458723,0.0004906636,0.0008859683,0.01775578,0.008013685,0.0154115,0.001480686,0.007717538,0.000286582],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964276,0.0001201222,0.001148674,0.0000981069,0.00001724327,0.00004986765,0.00005906531,0.00008573291,0.001993595],"genre_scores_gemma":[0.9973934,0.00006791591,0.001622721,0.00005309435,0.00001186864,0.00006943862,0.00006733528,0.00004104661,0.0006731633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008845816,"threshold_uncertainty_score":0.02959216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1286158271501661,"score_gpt":0.4179219222526938,"score_spread":0.2893060951025277,"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."}}