{"id":"W2773752798","doi":"10.1109/icdmw.2017.109","title":"Finding Suspicious Activities in Financial Transactions and Distributed Ledgers","year":2017,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Ledger; Money laundering; Order (exchange); Ranking (information retrieval); Financial transaction; Payment; Popularity; Cryptocurrency; Domain (mathematical analysis); Computer security; Finance; Business; Artificial intelligence; World Wide Web; Database transaction","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":[0.00006110889,0.00005203419,0.00006500634,0.00005130174,0.0004627081,0.0001734918,0.000263613,0.00003906168,0.0000104142],"category_scores_gemma":[0.00001122983,0.00005074942,0.00002089871,0.00006471443,0.00005483497,0.0004007138,0.00003439289,0.00007877807,0.000002177015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002265309,"about_ca_system_score_gemma":0.00002206964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001617699,"about_ca_topic_score_gemma":0.000222393,"domain_scores_codex":[0.9996147,0.000006468481,0.00007720722,0.0001523023,0.00004246676,0.0001068213],"domain_scores_gemma":[0.9996179,0.00002020022,0.00004012226,0.0002829604,0.000009232064,0.00002959936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001148105,0.0001926879,0.01180816,0.00002173966,0.00001194194,0.00001704337,0.00104469,0.0001009176,0.004969119,0.2964061,0.001130384,0.6842858],"study_design_scores_gemma":[0.001637169,0.0002837192,0.6916256,0.00007116304,0.0000176621,0.00008499875,0.0004509119,0.1113127,0.1170994,0.04908348,0.02715331,0.001179918],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09600938,0.000005851156,0.9012269,0.0008678385,0.00004128332,0.00007655043,0.000003608655,0.0001106331,0.00165797],"genre_scores_gemma":[0.9922472,0.00001546979,0.007216945,0.00003347989,0.00001117209,0.00003696753,4.556524e-7,0.000002064285,0.0004362252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8962379,"threshold_uncertainty_score":0.3558823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587290116271142,"score_gpt":0.2647130117016127,"score_spread":0.2488401105389013,"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."}}