{"id":"W3091088451","doi":"10.1287/trsc.2019.0947","title":"Probabilistic Envelope Constrained Multiperiod Stochastic Emergency Medical Services Location Model and Decomposition Scheme","year":2020,"lang":"en","type":"article","venue":"Transportation Science","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematical optimization; Probabilistic logic; Solver; Constraint (computer-aided design); Computer science; Stochastic programming; Integer programming; Scheme (mathematics); Decomposition; Operations research; Envelope (radar); Facility location problem; Service (business); Engineering; Mathematics; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001508011,0.001096111,0.001223021,0.0006405092,0.0003685202,0.001602023,0.002051686,0.001783277,0.006191972],"category_scores_gemma":[0.002252519,0.0007975693,0.001231808,0.001377266,0.0006941675,0.001357742,0.001287546,0.001741808,0.0007368592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001626905,"about_ca_system_score_gemma":0.001678998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007336466,"about_ca_topic_score_gemma":0.004502478,"domain_scores_codex":[0.9988806,0.0004613053,0.00003695917,0.0001712264,0.0002245927,0.0002254638],"domain_scores_gemma":[0.9990144,0.0004712569,0.000198621,0.00006431318,0.0001502534,0.0001011232],"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":[0.00001757943,0.00001164978,0.0001241242,0.00001472053,0.000007734297,0.00004155339,0.000009920962,0.9831249,0.0001948082,0.01433373,0.0003630386,0.001756292],"study_design_scores_gemma":[0.000005848232,0.00001028272,0.0000658223,0.000003028784,0.000003625557,0.00001468477,0.000006048727,0.9964516,0.0000514709,0.003023787,0.0003595071,0.000004170031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02278155,0.0002955144,0.9637269,0.0006226244,0.00006343474,0.0001097765,0.000623389,0.0001660521,0.01161073],"genre_scores_gemma":[0.8581448,0.0008632312,0.1172996,0.000227804,0.0000920107,0.00054661,0.0007774914,0.0001043009,0.02194421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007336466,"threshold_uncertainty_score":0.02071422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02765895838037207,"score_gpt":0.2697996535182052,"score_spread":0.2421406951378332,"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."}}