{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009646335,0.001029941,0.0004358135,0.003379579,0.0006775874,0.003000633,0.0008539342,0.001109434,0.02042472],"category_scores_gemma":[0.008001583,0.0004055371,0.0007336931,0.00380343,0.0003813572,0.002667842,0.00184561,0.001313319,0.005578944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004364278,"about_ca_system_score_gemma":0.0006837594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001649546,"about_ca_topic_score_gemma":0.001940816,"domain_scores_codex":[0.9989966,0.0002576685,0.0001167084,0.0001988004,0.0003746503,0.00005559361],"domain_scores_gemma":[0.9970818,0.001214706,0.0003299292,0.0006249356,0.0006256213,0.000123017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005872821,0.0001443371,0.003969994,0.001798064,0.0001306146,0.0007155154,0.001585106,0.01038404,0.01489764,0.1395303,0.2466058,0.5796514],"study_design_scores_gemma":[0.0001463055,0.0001241038,0.003862518,0.0004923315,0.00008843854,0.00202364,0.0009928937,0.1673696,0.01863527,0.2567533,0.549392,0.0001196268],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01507396,0.002897341,0.8758913,0.006178261,0.001125083,0.0003942508,0.03309495,0.03733261,0.0280122],"genre_scores_gemma":[0.1474718,0.001836665,0.8188649,0.0007936941,0.0002847677,0.0004340748,0.01809926,0.002366171,0.009848636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02042472,"threshold_uncertainty_score":0.06832749,"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."}}