{"id":"W4404493737","doi":"10.1108/jdal-04-2024-0007","title":"A genetic algorithm-based solution for multi-type maximal covering location problem (MMCLP): application to defense and deterrence","year":2024,"lang":"en","type":"article","venue":"Journal of Defense Analytics and Logistics","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; University of Waterloo","funders":"","keywords":"Genetic algorithm; Deterrence (psychology); Type (biology); Algorithm; Computer science; Mathematical optimization; Mathematics; Criminology; Psychology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007768706,0.0007131922,0.0005975518,0.0007625965,0.0005072692,0.001079484,0.00116465,0.001399896,0.002388997],"category_scores_gemma":[0.00257658,0.0003303823,0.000879316,0.001082856,0.0005901634,0.0006403756,0.0008208706,0.0008172566,0.0002411765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001238563,"about_ca_system_score_gemma":0.002174809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01046117,"about_ca_topic_score_gemma":0.009324639,"domain_scores_codex":[0.9995034,0.0002047611,0.00001495203,0.0001003377,0.0001097324,0.0000668842],"domain_scores_gemma":[0.9993033,0.00040356,0.0001072151,0.00003786624,0.0001086545,0.00003933498],"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":[0.00002338665,0.00004071619,0.0007294933,0.00007651137,0.00003366062,0.000117515,0.0000456265,0.9615622,0.0009007357,0.008890524,0.001209501,0.02637017],"study_design_scores_gemma":[0.00001271681,0.00005109867,0.0002104967,0.00002397019,0.0000163835,0.00007430871,0.00005016714,0.9929147,0.0004480753,0.004169995,0.002021399,0.000006663281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05892285,0.0007543842,0.9245613,0.0009837149,0.0001047799,0.0002208792,0.0001917556,0.0003008387,0.01395945],"genre_scores_gemma":[0.4813603,0.0006175532,0.5126857,0.0002309841,0.00004290845,0.0002743364,0.0002212301,0.00006402065,0.004502959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01046117,"threshold_uncertainty_score":0.02080053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04944531585717768,"score_gpt":0.2798166233885177,"score_spread":0.23037130753134,"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."}}