{"id":"W4400400937","doi":"10.1007/978-3-031-59543-1_2","title":"The Crime-Crypto Nexus: Nuancing Risk Across Crypto-Crime Transactions","year":2024,"lang":"en","type":"book-chapter","venue":"Ius gentium","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Cryptocurrency; Cybercrime; Nexus (standard); Computer security; Evasion (ethics); Hacker; Organised crime; Child pornography; Business; Sanctions; Criminology; Internet privacy; The Internet; Political science; Law; Computer science; Psychology","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":[0.0007182375,0.000554365,0.0003468519,0.001647647,0.002160773,0.008772384,0.0008557988,0.001679087,0.01945635],"category_scores_gemma":[0.002792477,0.0002590283,0.00025967,0.001840002,0.005876167,0.008322143,0.002724134,0.003403035,0.002178273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002537715,"about_ca_system_score_gemma":0.001992975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056934,"about_ca_topic_score_gemma":0.003511845,"domain_scores_codex":[0.9993975,0.0002101337,0.00001539375,0.00007725778,0.0002336234,0.00006614274],"domain_scores_gemma":[0.9981969,0.001201593,0.0001622721,0.0001574332,0.0001539235,0.0001278086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001181493,0.0000270358,0.0007628014,0.00007040947,0.00000445156,0.0001307441,0.001843293,0.0007266034,0.0001065085,0.9271407,0.021611,0.04756451],"study_design_scores_gemma":[0.000006802647,0.00003520725,0.001973888,0.0009185523,0.000009725849,0.0004480652,0.005284719,0.003124991,0.0002899653,0.6845331,0.3033513,0.00002368753],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01879436,0.02037221,0.01282474,0.01651905,0.0005937896,0.00005975684,0.0001033281,0.00009451502,0.9306383],"genre_scores_gemma":[0.6148681,0.03762851,0.009067794,0.004483595,0.001525008,0.0002067245,0.000166745,0.0002317725,0.3318218],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01945635,"threshold_uncertainty_score":0.06508797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794133679846404,"score_gpt":0.2660139142909128,"score_spread":0.2480725774924487,"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."}}