{"id":"W2058239738","doi":"10.1145/2484838.2484857","title":"A multidimensional data model with subcategories for flexibly capturing summarizability","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Extension (predicate logic); Aggregate (composite); Dimension (graph theory); Hierarchy; Data warehouse; Cube (algebra); Data cube; Property (philosophy); Online analytical processing; Materialized view; Data modeling; Theoretical computer science; Expressive power; Data mining; Database; View; Programming language; Database design; Mathematics","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":[0.0002522786,0.0001233447,0.0001129581,0.00004754758,0.0001184244,0.0002898572,0.001425351,0.00002184849,0.00002284931],"category_scores_gemma":[0.0000222412,0.00008432797,0.00001898631,0.0001382875,0.00004916189,0.003185153,0.001384145,0.00004934265,0.0000479636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001248766,"about_ca_system_score_gemma":0.00004876692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002912489,"about_ca_topic_score_gemma":0.00003610531,"domain_scores_codex":[0.9988028,0.00001055868,0.0001431745,0.0005692145,0.0002105765,0.0002636806],"domain_scores_gemma":[0.9982979,0.00006626,0.00003848696,0.001428118,0.00009954997,0.00006968133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003662497,0.0003542612,0.002114518,0.0001257722,0.0001207267,0.000004976408,0.0003614016,0.008043944,0.0003714582,0.8255038,0.076775,0.08618751],"study_design_scores_gemma":[0.0003223645,0.00002959997,0.0004542081,0.000003399728,0.000005492237,7.924702e-7,0.00002529871,0.986908,0.0001985973,0.009840138,0.002058618,0.0001534826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002989042,0.00001106137,0.9932662,0.001563418,0.00008030132,0.0004746637,0.00002899596,0.0001957815,0.00139059],"genre_scores_gemma":[0.1280754,0.000001738262,0.8675173,0.000311643,0.00003363823,0.00006926579,0.000203307,0.000008233258,0.003779455],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9788641,"threshold_uncertainty_score":0.3438795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05581736004896914,"score_gpt":0.2605118643018415,"score_spread":0.2046945042528724,"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."}}