{"id":"W2784052864","doi":"10.1287/opre.2019.1969","title":"Ambulance Emergency Response Optimization in Developing Countries","year":2020,"lang":"en","type":"preprint","venue":"Operations Research","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Grand Challenges Canada","keywords":"Emergency response; Emergency vehicle; Emergency medical services; Travel time; Routing (electronic design automation); Computer science; Vehicle routing problem; Transport engineering; Operations research; Medical emergency; Medicine; Engineering; Real-time computing; Computer network","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":[0.0008181877,0.0005945647,0.0005218937,0.0007792895,0.0004517064,0.001150412,0.000330775,0.0003491406,0.002151964],"category_scores_gemma":[0.002393766,0.0002475249,0.0003032242,0.00172255,0.0002773503,0.0005678965,0.0003413909,0.0004823108,0.0001536758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00133932,"about_ca_system_score_gemma":0.001885409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03983736,"about_ca_topic_score_gemma":0.024894,"domain_scores_codex":[0.9995982,0.0002080387,0.00001446182,0.00004135964,0.00002477479,0.0001132046],"domain_scores_gemma":[0.9990857,0.000562389,0.0001078865,0.00003344749,0.0001435873,0.00006691978],"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":[0.000382273,0.0001292517,0.009288874,0.0001042946,0.00006245536,0.0001238837,0.00006509609,0.9537141,0.001076025,0.004486226,0.003010184,0.02755731],"study_design_scores_gemma":[0.0000584824,0.0001676869,0.0135781,0.00004066751,0.00006943792,0.00004287769,0.0008034229,0.9769912,0.002720185,0.002984986,0.002524258,0.00001861386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9481663,0.001884923,0.03302864,0.001451894,0.00005615239,0.0000767068,0.0008967541,0.0001157875,0.01432302],"genre_scores_gemma":[0.9896042,0.0007562205,0.008172882,0.00004686074,0.00001075744,0.00003009783,0.0002433299,0.00002478783,0.001110757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03983736,"threshold_uncertainty_score":0.07921094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1510531713982814,"score_gpt":0.3811192627914298,"score_spread":0.2300660913931484,"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."}}