{"id":"W4385807068","doi":"10.1049/blc2.12036","title":"Mixing detection on Bitcoin transactions using statistical patterns","year":2023,"lang":"en","type":"article","venue":"IET Blockchain","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Mixing (physics); Cryptocurrency; Anonymity; Computer science; Set (abstract data type); Computer security; Traceability; Precision and recall; Internet privacy; Artificial intelligence","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":[0.001510688,0.0005108349,0.0006469924,0.004528284,0.0005679663,0.001744125,0.0006498927,0.0008422357,0.0006767674],"category_scores_gemma":[0.008107419,0.0001687844,0.0003952949,0.002734198,0.0004748837,0.001389879,0.0009128389,0.0006874566,0.0005577427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007285363,"about_ca_system_score_gemma":0.0005925812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003488084,"about_ca_topic_score_gemma":0.003788314,"domain_scores_codex":[0.9979031,0.0004644631,0.0002333455,0.0005093334,0.0006685769,0.0002211762],"domain_scores_gemma":[0.990546,0.00403466,0.001677663,0.000918499,0.002444135,0.00037893],"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":[0.001677145,0.0006757128,0.4193857,0.0003303368,0.0002482175,0.001491242,0.001261346,0.07763763,0.03459777,0.00615235,0.00311649,0.4534262],"study_design_scores_gemma":[0.00001650726,0.0001678999,0.03409934,0.00003749688,0.00003825293,0.0005077648,0.00041571,0.9462926,0.01240554,0.004001739,0.001984521,0.00003254857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8918901,0.0003471367,0.1029215,0.0002898965,0.00004766068,0.0001745403,0.001108145,0.0009151506,0.002305862],"genre_scores_gemma":[0.9793002,0.00006520882,0.01919945,0.00001974782,0.00001230845,0.00003274527,0.0007361239,0.00001601469,0.0006182626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004528284,"threshold_uncertainty_score":0.007989407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02197667173046484,"score_gpt":0.2673946915409075,"score_spread":0.2454180198104427,"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."}}