{"id":"W30673375","doi":"10.1094/pdis-10-17-1602-re","title":"An Empirical Comparison of Methods for Iceberg-CUBE Construction","year":2001,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Pruning; Cube (algebra); Top-down and bottom-up design; Iceberg; Aggregate (composite); Computer science; Set (abstract data type); Function (biology); Data mining; Algorithm; Data cube; Mathematics; Geology; Combinatorics","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":[0.005050582,0.0009346406,0.0006540169,0.001532416,0.0007824826,0.001962947,0.00229534,0.0008994619,0.02670511],"category_scores_gemma":[0.01762309,0.0005199349,0.001087355,0.002279637,0.0008980125,0.002850235,0.002726447,0.001349752,0.009900097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001318238,"about_ca_system_score_gemma":0.001873931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00262691,"about_ca_topic_score_gemma":0.003622654,"domain_scores_codex":[0.9950111,0.001373884,0.0004136144,0.0007525728,0.002151011,0.0002979502],"domain_scores_gemma":[0.988718,0.004728398,0.0004737774,0.003537169,0.002283788,0.0002588723],"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":[0.0004470537,0.0002454404,0.003640938,0.0005504017,0.00006117079,0.00008257572,0.0004917056,0.01303832,0.01092806,0.05324136,0.008996967,0.9082761],"study_design_scores_gemma":[0.000278244,0.001451448,0.0132555,0.0005995404,0.0001865355,0.001788291,0.001500206,0.2777425,0.06366798,0.08542284,0.5538789,0.0002279717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01585881,0.001048528,0.9471833,0.0002714808,0.0002925408,0.0006186077,0.0006381029,0.003985974,0.03010277],"genre_scores_gemma":[0.06515744,0.0009936149,0.9171939,0.0001258834,0.00006373059,0.0006245053,0.001798007,0.001501194,0.01254179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02670511,"threshold_uncertainty_score":0.08933753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2182472176523269,"score_gpt":0.5633181396275331,"score_spread":0.3450709219752062,"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."}}