{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001308379,0.0001359556,0.0006948357,0.0002552486,0.00021281,0.00008797785,0.0005195153,0.0000954217,0.0001656001],"category_scores_gemma":[0.0004744789,0.0001336043,0.0002639692,0.0003507154,0.0001194533,0.000627044,0.0000192664,0.0002041502,0.000001383273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000141664,"about_ca_system_score_gemma":0.0002761261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007777407,"about_ca_topic_score_gemma":0.002183125,"domain_scores_codex":[0.9980796,0.0001154817,0.0006394156,0.0002186856,0.0005995696,0.0003472588],"domain_scores_gemma":[0.9971143,0.001533385,0.0006556074,0.0003274472,0.0002018883,0.000167394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007861559,0.003397863,0.08741707,0.001412711,0.05196593,0.0001800297,0.2952426,0.2655262,0.07167856,0.04440933,0.0122686,0.1586395],"study_design_scores_gemma":[0.02503446,0.002152536,0.09171115,0.001033222,0.02365675,0.00005453069,0.2084963,0.513094,0.001617827,0.004105384,0.1262372,0.002806662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3526496,0.0004021804,0.6444554,0.0004812714,0.0005804296,0.0002446464,0.0004259042,0.00001766463,0.0007428479],"genre_scores_gemma":[0.9943781,0.0001355949,0.004774361,0.00007792281,0.0002225683,0.000003549151,0.000006819801,0.00001760346,0.0003834763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6417285,"threshold_uncertainty_score":0.5448224,"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."}}