{"id":"W2005906503","doi":"10.1109/pacificvis.2014.43","title":"Using Entropy-Related Measures in Categorical Data Visualization","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Science Foundation","keywords":"Categorical variable; Visualization; Computer science; Entropy (arrow of time); Data visualization; Joint entropy; Information visualization; Data mining; Visual analytics; Mutual information; Data science; Artificial intelligence; Machine learning; Principle of maximum entropy","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":[0.006017752,0.001175681,0.0008105908,0.007102731,0.001004867,0.004212766,0.0008824058,0.001095871,0.001896897],"category_scores_gemma":[0.04578799,0.0004650747,0.0007461477,0.006132048,0.001796129,0.006514338,0.003432237,0.001843211,0.0003342843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006669302,"about_ca_system_score_gemma":0.0007122779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007657114,"about_ca_topic_score_gemma":0.00080619,"domain_scores_codex":[0.9956857,0.002129476,0.0002940563,0.0003458358,0.001426952,0.0001180308],"domain_scores_gemma":[0.9680818,0.0232844,0.003025264,0.002303378,0.002722565,0.0005826354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006294322,0.0004029215,0.0231858,0.00134132,0.0003473675,0.0006462566,0.004346463,0.2099614,0.05678838,0.2290789,0.009116646,0.4641552],"study_design_scores_gemma":[0.0000861513,0.0003077415,0.01103377,0.0002932373,0.0001102712,0.0008698148,0.0008166904,0.59229,0.04951037,0.3325086,0.01185291,0.0003203466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05527839,0.001185651,0.9366382,0.0008154079,0.000111895,0.0001332735,0.0004330561,0.001713332,0.00369083],"genre_scores_gemma":[0.517715,0.0008845663,0.4792733,0.0001579671,0.0001459084,0.0002619638,0.0004723241,0.0004564339,0.000632446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007102731,"threshold_uncertainty_score":0.03182524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1077904259267571,"score_gpt":0.3643159058469443,"score_spread":0.2565254799201873,"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."}}