{"id":"W2025685302","doi":"10.1109/icsmc.2012.6377673","title":"Optimized audit evidence gathering method based on data matching using length-filtering","year":2012,"lang":"en","type":"article","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Audit; Matching (statistics); Data collection; Computer science; Information technology audit; Process (computing); Audit plan; Audit evidence; Internal audit; Accounting; Business; Joint audit; Statistics; Mathematics","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":[0.004593613,0.0007777365,0.001565082,0.005474303,0.001193692,0.00165744,0.001737264,0.0010417,0.002217751],"category_scores_gemma":[0.01511776,0.0005030816,0.001591267,0.004129957,0.00048739,0.00255303,0.001694264,0.0009232189,0.000766283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009412496,"about_ca_system_score_gemma":0.003106293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003667289,"about_ca_topic_score_gemma":0.003263653,"domain_scores_codex":[0.9920007,0.001624973,0.001304056,0.001444857,0.003304022,0.0003213999],"domain_scores_gemma":[0.9902815,0.003082526,0.001140716,0.001579574,0.00366176,0.0002538783],"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":[0.0005875544,0.0004472786,0.009327588,0.0003973333,0.0002057002,0.0001747695,0.0003228574,0.02126045,0.03393281,0.005577608,0.003341221,0.9244248],"study_design_scores_gemma":[0.0003052964,0.0007910852,0.01569002,0.0001123058,0.0004779541,0.001229358,0.0004486751,0.8211122,0.1221833,0.01810794,0.01928497,0.0002568733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01761212,0.0001757,0.979955,0.0001105429,0.00003902804,0.0003446294,0.0002728116,0.0009845385,0.0005057038],"genre_scores_gemma":[0.08738597,0.0001694759,0.9098982,0.00005974744,0.00004819646,0.0003847329,0.0008367888,0.00007374962,0.001143125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005474303,"threshold_uncertainty_score":0.0242936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2063994104280207,"score_gpt":0.3837406504335228,"score_spread":0.1773412400055021,"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."}}