{"id":"W2949228618","doi":"10.1016/j.ssci.2019.06.009","title":"An open multi-physics framework for modelling wildland-urban interface fire evacuations","year":2019,"lang":"en","type":"article","venue":"Safety Science","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada; National Institute of Standards and Technology; U.S. Department of Commerce; Fire Protection Research Foundation; Lunds Universitet; Imperial College London","keywords":"Wildland–urban interface; Vulnerability (computing); Timeline; Population; Poison control; Transport engineering; Firefighting; Computer science; Engineering; Environmental resource management; Environmental science; Geography; Cartography; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001278848,0.0001643701,0.0001903229,0.00002195796,0.0005162155,0.0002458144,0.002016388,0.00006862466,0.0002425577],"category_scores_gemma":[0.0001021892,0.0001487827,0.00003983433,0.0006587397,0.0002753449,0.001973063,0.0004892157,0.0001565773,0.001004562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003003942,"about_ca_system_score_gemma":0.00006390206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006230157,"about_ca_topic_score_gemma":0.00006222737,"domain_scores_codex":[0.9980447,0.00005137393,0.0002375836,0.0007421484,0.0004480417,0.0004761761],"domain_scores_gemma":[0.9986637,0.0001940678,0.0001234235,0.0008031203,0.00002888623,0.000186775],"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":[0.00006827868,0.0002548369,0.04610392,0.00002712488,0.000007125828,5.601336e-7,0.002673089,0.9144408,0.01625386,0.004368321,0.0002437537,0.0155584],"study_design_scores_gemma":[0.0003073554,0.0001807081,0.00618587,0.00006439454,0.000004960006,0.000001300703,0.0000963736,0.9881154,0.001046634,0.002221271,0.00154133,0.0002344185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3617035,0.00001260253,0.6350874,0.0001766077,0.0004944026,0.001397725,0.00002864431,0.00005527984,0.001043863],"genre_scores_gemma":[0.9042072,0.000002847811,0.09516741,0.0001520022,0.00004913005,0.00005129877,0.000005938456,0.00001949321,0.0003446523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5425037,"threshold_uncertainty_score":0.9997733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251394851176893,"score_gpt":0.3037752043840659,"score_spread":0.2786357192663766,"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."}}