{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006779299,0.001603856,0.0007927804,0.005372155,0.001681003,0.005916605,0.001620292,0.001730871,0.004039334],"category_scores_gemma":[0.04437836,0.0009826944,0.001198478,0.002187165,0.003625275,0.008532423,0.005997648,0.001187937,0.0005976973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007380659,"about_ca_system_score_gemma":0.0008749398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001075039,"about_ca_topic_score_gemma":0.001405935,"domain_scores_codex":[0.9915835,0.005077046,0.0004512672,0.0009878,0.001359877,0.000540496],"domain_scores_gemma":[0.9580245,0.03328226,0.0020404,0.003985995,0.001904197,0.0007627456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001382538,0.0002752084,0.05416348,0.003506834,0.0004521162,0.003517358,0.4615825,0.004713615,0.08710847,0.1269435,0.005398852,0.2509555],"study_design_scores_gemma":[0.0004513497,0.001372057,0.07436568,0.00322249,0.0007739015,0.01114944,0.215586,0.1191279,0.06166186,0.3372847,0.1742297,0.0007749739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5127822,0.002906936,0.4540416,0.001955386,0.00006702331,0.0006096251,0.0006087923,0.002434685,0.02459372],"genre_scores_gemma":[0.835857,0.0006414182,0.1592639,0.0001432589,0.00002775005,0.0006147393,0.0004256065,0.0003577449,0.002668615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006779299,"threshold_uncertainty_score":0.03585279,"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."}}