{"id":"W2985057827","doi":"10.1108/jmlc-03-2019-0024","title":"Data mining for statistical analysis of money laundering transactions","year":2019,"lang":"en","type":"article","venue":"Journal of Money Laundering Control","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Roads University","funders":"","keywords":"Money laundering; Process (computing); Originality; Relevance (law); Computer science; Value (mathematics); Business; Data science; Computer security; Finance; Political science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01383036,0.001083491,0.00131377,0.009156331,0.001131556,0.004180193,0.001601637,0.0009959381,0.002882638],"category_scores_gemma":[0.07578981,0.0006041235,0.00197003,0.01111298,0.001507046,0.0033022,0.001990175,0.003201868,0.00199311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553555,"about_ca_system_score_gemma":0.004259371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002773484,"about_ca_topic_score_gemma":0.002635928,"domain_scores_codex":[0.983364,0.007675891,0.002630597,0.002177999,0.003909273,0.0002423086],"domain_scores_gemma":[0.9114974,0.06429283,0.008252105,0.008466832,0.006907153,0.0005837157],"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":[0.000477161,0.0007530624,0.08827478,0.004211978,0.0011765,0.0009591679,0.002797905,0.04792228,0.007067155,0.1146495,0.02427755,0.707433],"study_design_scores_gemma":[0.0001281091,0.0007880999,0.05249823,0.002856779,0.0004011888,0.001848369,0.003987561,0.5348583,0.01641196,0.2840545,0.101842,0.0003249744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0271451,0.001733906,0.9452925,0.003591549,0.0002051988,0.001792903,0.0110122,0.003134211,0.00609243],"genre_scores_gemma":[0.156269,0.001239502,0.832119,0.0004248459,0.0001604376,0.001980467,0.006748309,0.0001685633,0.0008898284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01383036,"threshold_uncertainty_score":0.07314283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05236307176384071,"score_gpt":0.3375742701140016,"score_spread":0.2852111983501609,"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."}}