{"id":"W4317826942","doi":"10.1109/iv56949.2022.00045","title":"Data. Information and Knowledge Visualization for Frequent Patterns","year":2022,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Computer science; Visualization; Big data; Data visualization; Data mining; Data science; Information visualization; Variety (cybernetics); Knowledge extraction; Business intelligence; Data modeling; Information retrieval; Database; Artificial intelligence","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":[0.0002165672,0.00003409353,0.00003318521,0.00004286664,0.0002199987,0.0001276031,0.0004997094,0.000006565232,0.00001669022],"category_scores_gemma":[0.00001038322,0.00003323864,0.000005376401,0.0001239494,0.000004557673,0.001024263,0.0007895026,0.00002236189,0.000006433315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001435394,"about_ca_system_score_gemma":0.00003165089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003051246,"about_ca_topic_score_gemma":0.00001032855,"domain_scores_codex":[0.9996226,0.00001090649,0.0001048188,0.0001282785,0.00006756028,0.00006582477],"domain_scores_gemma":[0.9995112,0.00002962498,0.00003705437,0.000371216,0.00002724427,0.00002370477],"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":[6.111942e-7,0.00004302266,0.000153138,0.00001480164,0.000004425889,5.163964e-8,0.0006920799,0.00001914248,0.00001346173,0.3817866,0.02475777,0.5925149],"study_design_scores_gemma":[0.00009301011,0.00002092196,0.0003300982,7.865892e-7,0.000001384048,0.000002070219,0.00008631478,0.5873697,0.00002375896,0.0004751755,0.411557,0.00003988181],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000840802,0.00002021348,0.9974802,0.0003774183,0.00009066488,0.0001570352,0.000490965,0.00007246298,0.0004702794],"genre_scores_gemma":[0.4188012,0.00006340082,0.5641341,0.002424974,0.0001617205,0.001194191,0.01219196,0.00001702286,0.001011522],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.592475,"threshold_uncertainty_score":0.1692074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04375335378064978,"score_gpt":0.319662708336515,"score_spread":0.2759093545558652,"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."}}