{"id":"W2990619729","doi":"10.1108/jfc-06-2018-0057","title":"Detecting counterfeit pharmaceutical drugs","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Crime","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Counterfeit; Stakeholder; Originality; Business; Counterfeit Drugs; Forensic accounting; Supply chain; Pharmaceutical industry; Risk analysis (engineering); Harm; Marketing; Public relations; Accounting; Medicine; Law; Political science; Pharmacology","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":[0.007357006,0.0007407573,0.000436371,0.008090473,0.001722106,0.004488121,0.00178105,0.001723464,0.006595368],"category_scores_gemma":[0.02296669,0.000302366,0.000709781,0.002463013,0.002561011,0.005760607,0.00381384,0.001190637,0.0008927312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002912472,"about_ca_system_score_gemma":0.004561443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003789338,"about_ca_topic_score_gemma":0.003329926,"domain_scores_codex":[0.9900302,0.003475409,0.0007093035,0.0008721434,0.004260479,0.0006525486],"domain_scores_gemma":[0.9797081,0.008265237,0.006674965,0.001356967,0.003579021,0.0004157234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002100095,0.0005936439,0.1854011,0.002827953,0.0001249224,0.004968922,0.006003461,0.003279317,0.003531495,0.1139883,0.009057791,0.6700131],"study_design_scores_gemma":[0.00008468924,0.001662534,0.2563528,0.01436465,0.0006889709,0.04116755,0.0495157,0.04907946,0.06601635,0.1300842,0.3906053,0.000377742],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.560156,0.03826721,0.1077168,0.03202917,0.0005616738,0.002799435,0.001020252,0.0005118885,0.2569377],"genre_scores_gemma":[0.9419243,0.0104757,0.03590285,0.001770481,0.0001198672,0.0001734773,0.0003025957,0.00002874413,0.009301952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008090473,"threshold_uncertainty_score":0.038908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01726276755417118,"score_gpt":0.2849143918571094,"score_spread":0.2676516243029383,"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."}}