{"id":"W1977817503","doi":"10.3844/ajassp.2014.1507.1518","title":"A DATA WAREHOUSE DESIGN FOR THE DETECTION OF FRAUD IN THE SUPPLY CHAIN BY USING THE BENFORDS LAW","year":2014,"lang":"en","type":"article","venue":"American Journal of Applied Sciences","topic":"Benford’s Law and Fraud Detection","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Supply chain; Benford's law; Warranty; Computer science; Supply chain management; Procurement; Analytics; Data warehouse; Database; Risk analysis (engineering); Business; Marketing; Law","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":[0.006306485,0.0001013213,0.0002201128,0.00006135224,0.0004688696,0.00008463243,0.001263628,0.0000256022,0.000003639779],"category_scores_gemma":[0.0001692108,0.00004392642,0.00005893117,0.0005601492,0.001073527,0.0001802338,0.0000542779,0.0002003914,2.727648e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000212049,"about_ca_system_score_gemma":0.00006392934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002375616,"about_ca_topic_score_gemma":0.0002380863,"domain_scores_codex":[0.9986393,0.0001494028,0.0004202388,0.0001344297,0.0004539447,0.0002026406],"domain_scores_gemma":[0.996411,0.002372897,0.0007521806,0.0003794538,0.00005813968,0.000026297],"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":[0.001344939,0.0004992447,0.0001852424,0.00008385832,0.0002526336,0.000001350781,0.02207474,0.02005988,0.08312158,0.195319,0.003016424,0.6740411],"study_design_scores_gemma":[0.002819322,0.00745404,0.0002865897,0.0001936205,0.0007429085,0.0003337617,0.1248874,0.5060143,0.08962812,0.2195874,0.04717195,0.0008805642],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1876921,0.00007379286,0.8104852,0.000625553,0.0001529922,0.0004798062,0.00001365725,0.000006879853,0.0004700215],"genre_scores_gemma":[0.9883165,0.00001506546,0.01121381,0.0003173147,0.0001151066,0.00001085906,2.783725e-7,0.000008498179,0.000002589883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8006244,"threshold_uncertainty_score":0.3955454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09503120109016087,"score_gpt":0.3253784809954282,"score_spread":0.2303472799052673,"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."}}