{"id":"W4315929037","doi":"10.1016/j.cie.2023.109007","title":"Angels against demons: Fight against smuggling in an illicit supply chain with uncertain outcomes and unknown structure","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Interdiction; Flow network; Commodity; Law enforcement; Enforcement; Computer science; Operations research; Tying; Business; Computer security; Economics; Engineering; Microeconomics; Mathematical optimization; Law; Mathematics; Political science; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003002313,0.0005811246,0.0002981918,0.000648261,0.00229931,0.001948077,0.0008704317,0.002622864,0.005920449],"category_scores_gemma":[0.0146857,0.0002279201,0.0003332674,0.0003230423,0.002339407,0.003337849,0.002805458,0.002085837,0.0005195027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009604449,"about_ca_system_score_gemma":0.001504201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00413926,"about_ca_topic_score_gemma":0.00539294,"domain_scores_codex":[0.9990621,0.0004587711,0.00002523804,0.00008178646,0.0001866283,0.0001854871],"domain_scores_gemma":[0.993137,0.003922738,0.0009277384,0.0004945516,0.0005491937,0.0009688092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005402257,0.0008915339,0.04512256,0.0004731215,0.0003817584,0.01172639,0.01337619,0.2729024,0.01191435,0.3083217,0.06337097,0.2661168],"study_design_scores_gemma":[0.000226524,0.001162343,0.003733953,0.0003154899,0.0001324191,0.001306181,0.008853573,0.7792588,0.005422533,0.1701905,0.02927745,0.0001202871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8293046,0.0004458752,0.0887863,0.02059359,0.0005858064,0.000167247,0.00008429828,0.0013585,0.05867365],"genre_scores_gemma":[0.9859299,0.0001029924,0.007232037,0.0005129153,0.00005646049,0.00001484363,0.00001862789,0.00004183082,0.006090343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005920449,"threshold_uncertainty_score":0.01980585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02089967658709897,"score_gpt":0.218975142422305,"score_spread":0.198075465835206,"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."}}