{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003094717,0.0003906724,0.0004132498,0.0009198863,0.0001536187,0.0004174267,0.0003846429,0.0001768828,0.00001098396],"category_scores_gemma":[0.00007203319,0.0003255433,0.00005500852,0.001421004,0.00004142708,0.0008609422,0.0002734524,0.0004010556,0.00001047828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007440157,"about_ca_system_score_gemma":0.00002482981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001466635,"about_ca_topic_score_gemma":0.0001589815,"domain_scores_codex":[0.9981382,0.00001547162,0.0003695523,0.0005296705,0.0003181171,0.000628953],"domain_scores_gemma":[0.9993682,0.000119262,0.0001161851,0.0002995176,0.00004471958,0.00005215188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004334661,0.0000345889,0.1120392,0.0001256844,0.00007328887,0.0002712308,0.0002534048,0.833992,0.0004333347,0.001352878,0.001293574,0.05008753],"study_design_scores_gemma":[0.003804347,0.0000492951,0.06370057,0.0006706491,0.00005035216,0.000002129411,0.0007548012,0.8894354,0.00007551975,0.0001035475,0.04026401,0.001089446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965338,0.0000571655,0.0006587702,0.0008420697,0.0008204341,0.0005622054,0.000007953466,0.0004268319,0.00009079136],"genre_scores_gemma":[0.997004,0.00003081676,0.0004051992,0.001072862,0.001188146,0.00002959165,0.0001624079,0.00006195613,0.00004496558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05544335,"threshold_uncertainty_score":0.9999197,"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."}}