{"id":"W2901274057","doi":"10.1287/opre.2018.1756","title":"Exact Solution of the Evasive Flow Capturing Problem","year":2018,"lang":"en","type":"article","venue":"Operations Research","topic":"Traffic control and management","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Bilevel optimization; Law enforcement; Enforcement; Computer science; Flow network; Revenue; Flow (mathematics); Process (computing); Revenue management; Mathematical optimization; Operations research; Computer security; Business; Law; Optimization problem; Mathematics; Algorithm; Finance","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":[0.0003092588,0.00003999573,0.00004574436,0.0000680299,0.0002120431,0.00003674937,0.0001448232,0.00002159811,0.00008804177],"category_scores_gemma":[0.0000340412,0.00002841928,0.00002278775,0.0002257686,0.00007496696,0.00007219501,0.00006453505,0.0001160263,0.00008617557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005546073,"about_ca_system_score_gemma":0.00003154191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001174944,"about_ca_topic_score_gemma":0.001838193,"domain_scores_codex":[0.9993929,0.00004613096,0.0000944594,0.00007622771,0.000221792,0.0001685342],"domain_scores_gemma":[0.9995796,0.00001987586,0.000002630931,0.0002061503,0.0001696971,0.00002204089],"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":[0.000007511516,0.00004839116,0.00004614657,0.0000759727,0.00006705523,8.54941e-7,0.002350896,0.8699666,0.06446873,0.004103142,0.01244088,0.04642377],"study_design_scores_gemma":[0.0003234727,0.00006039549,0.004632906,0.00005076855,0.000007973197,0.000001244535,0.0002511115,0.9595217,0.01204397,0.00009222981,0.02292875,0.00008551592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7404528,0.0007339456,0.04724653,0.005127141,0.001123325,0.00333065,0.0000383001,0.000394953,0.2015523],"genre_scores_gemma":[0.9971251,0.00001715098,0.001064039,0.000005118663,0.0001108019,0.00006188027,0.00000187269,0.000007562689,0.001606458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2566723,"threshold_uncertainty_score":0.1630886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03332793387422662,"score_gpt":0.2968236532767957,"score_spread":0.2634957194025691,"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."}}