{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005350207,0.0001065549,0.0002389798,0.00009395253,0.00009412725,0.00007043597,0.0005165825,0.00004102454,0.000068343],"category_scores_gemma":[0.00008692674,0.00008744187,0.0001441633,0.0001724355,0.00002320657,0.0005256963,0.0001632593,0.0002771321,0.0001483334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005689977,"about_ca_system_score_gemma":0.0001193668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001487065,"about_ca_topic_score_gemma":0.000001289276,"domain_scores_codex":[0.9988314,0.00002995155,0.0003980659,0.0001258893,0.0003705262,0.0002441861],"domain_scores_gemma":[0.9992861,0.00008799236,0.0001976169,0.0001668629,0.0001860832,0.00007530986],"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":[0.0006440994,0.0006610526,0.0152459,0.0002742552,0.0003312398,0.0004469794,0.01532329,0.0003754668,0.05620929,0.2720371,0.04728476,0.5911666],"study_design_scores_gemma":[0.00546419,0.002060949,0.0639345,0.0003825352,0.0001345182,0.0005323882,0.000319688,0.004993773,0.08553531,0.00439564,0.8312767,0.0009698607],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9371384,0.0008662348,0.02583075,0.0006500681,0.002850204,0.0001336405,8.326307e-7,0.00004077585,0.03248912],"genre_scores_gemma":[0.9968092,0.00003540319,0.001495698,0.0008764692,0.0003553664,6.130779e-7,5.97383e-8,0.000005418328,0.0004217087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7839919,"threshold_uncertainty_score":0.3565776,"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."}}