{"id":"W2578082361","doi":"","title":"Using Semantic Web Technologies for Explaining and Predicting Abnormal Expenses.","year":2016,"lang":"en","type":"article","venue":"International Semantic Web Conference","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Semantic Web; Information retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000245723,0.0001544118,0.000155345,0.0001561641,0.000192517,0.0002992458,0.0009707398,0.00006767273,0.00001483498],"category_scores_gemma":[0.0002633253,0.0001198097,0.00003819299,0.0001199455,0.0001247034,0.0007637994,0.0005371036,0.00007818225,0.00001269543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003899474,"about_ca_system_score_gemma":0.0001201331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002721899,"about_ca_topic_score_gemma":0.00001724167,"domain_scores_codex":[0.998697,0.00001462682,0.0002890361,0.0004808262,0.0002430325,0.0002754979],"domain_scores_gemma":[0.9989245,0.0003160928,0.0001597043,0.0003478877,0.0002024198,0.00004937677],"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":[0.00001477674,0.0001016155,0.01764966,0.00005254911,0.0001343479,0.00002601331,0.0007767005,0.00002043558,0.1241509,0.3697863,0.0006945655,0.4865922],"study_design_scores_gemma":[0.0007191084,0.00004790189,0.001029627,0.0004002303,0.00001412127,0.0001310891,0.0003442003,0.9801475,0.006747793,0.005837888,0.00428343,0.0002970578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1966282,0.00004855968,0.7983922,0.003437583,0.0003626939,0.0001868403,0.00005912987,0.0003436164,0.0005411335],"genre_scores_gemma":[0.8987337,0.00006928021,0.100842,0.00004871327,0.00007772524,0.00006491126,0.000005230361,0.000009541289,0.000148883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9801271,"threshold_uncertainty_score":0.4885696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05009066489494174,"score_gpt":0.2993295164954154,"score_spread":0.2492388516004737,"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."}}