{"id":"W2793633690","doi":"10.1109/tvcg.2018.2802520","title":"Exploration Strategies for Discovery of Interactivity in Visualizations","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Interactivity; Computer science; Visualization; Discoverability; Human–computer interaction; Data visualization; Data science; Set (abstract data type); Interactive visualization; Process (computing); Visual analytics; World Wide Web; Multimedia; Information visualization; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002240209,0.0001631506,0.0002037687,0.0006158546,0.0001791207,0.0003203464,0.0002405471,0.00008227662,0.000004288626],"category_scores_gemma":[0.000007147771,0.0001680056,0.00006868785,0.001147516,0.0001321132,0.002515804,0.000007017551,0.00007354103,0.000001826812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001932751,"about_ca_system_score_gemma":0.00007512045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002121377,"about_ca_topic_score_gemma":0.0001891429,"domain_scores_codex":[0.9987471,0.0001055363,0.0004209391,0.0003605295,0.0002037552,0.0001622179],"domain_scores_gemma":[0.9990789,0.000144067,0.0001565101,0.0002798202,0.0002890418,0.00005163287],"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":[0.00002262947,0.0003557868,0.00005370268,0.00004384803,0.00002108963,3.115513e-7,0.001665843,0.0007553044,0.00008662086,0.9938128,0.0001104063,0.003071672],"study_design_scores_gemma":[0.0005684252,0.0004240414,0.0001562389,0.00006436901,0.00001300609,0.000001975851,0.000195739,0.9848801,0.005314999,0.007716192,0.0004814874,0.000183347],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006215437,0.000007242202,0.9928126,0.00005430842,0.0004732629,0.0002688046,0.00003098918,0.0000930773,0.00004426916],"genre_scores_gemma":[0.99673,0.00009152092,0.002540526,0.0004715136,0.00005134548,0.00003261278,0.00002920122,0.0000145982,0.00003863513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905146,"threshold_uncertainty_score":0.685107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0402462888941082,"score_gpt":0.3333350168119537,"score_spread":0.2930887279178455,"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."}}