{"id":"W4293085252","doi":"10.1108/ijdrbe-09-2021-0122","title":"Urban search and rescue (USAR) simulation in earthquake environments using queuing theory: estimating the appropriate number of rescue teams","year":2022,"lang":"en","type":"article","venue":"International Journal of Disaster Resilience in the Built Environment","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Urban search and rescue; Queueing theory; Originality; Vulnerability (computing); Computer science; Geospatial analysis; Operations research; Transport engineering; Computer security; Engineering; Geography; Artificial intelligence; Cartography; Computer network","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":[0.001649528,0.0001072929,0.0001278224,0.0001123351,0.00006993202,0.0000398783,0.0005229072,0.00002612919,0.00006806281],"category_scores_gemma":[0.00005161076,0.00007777313,0.000046252,0.00008868233,0.0001083314,0.0001950779,0.0001719741,0.0003824165,0.000002076238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002767277,"about_ca_system_score_gemma":0.00001506949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004817909,"about_ca_topic_score_gemma":0.00001775108,"domain_scores_codex":[0.9980472,0.0003310999,0.000528903,0.0001160048,0.0008300043,0.0001467751],"domain_scores_gemma":[0.9994991,0.0001958548,0.00009985769,0.0001707676,0.000008791573,0.0000255888],"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":[0.00005740653,0.00006265262,0.02242825,0.000005945643,0.00002091901,0.00001243907,0.007606228,0.96336,0.0020872,0.0001605988,0.000001328967,0.004197077],"study_design_scores_gemma":[0.0004758486,0.00003368052,0.03874958,0.0000627776,0.00001010526,0.00007078012,0.005860385,0.9535946,0.0001818733,0.0008016198,0.00007148417,0.00008721708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746915,0.0001081333,0.02468197,0.0001467714,0.0001487312,0.0001494275,0.000006951586,0.00000216969,0.00006442125],"genre_scores_gemma":[0.9987719,0.00003141402,0.001045213,0.00004935873,0.00005172306,0.000007637655,0.000002164426,0.00001379343,0.00002679887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02408047,"threshold_uncertainty_score":0.3171496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424408104462445,"score_gpt":0.2823911628554738,"score_spread":0.2681470818108493,"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."}}