{"id":"W2980307239","doi":"10.1016/b978-0-12-816514-0.00004-7","title":"A model for quantitative fire risk assessment integrating agent-based model with automatic event tree analysis","year":2019,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Event tree; Event tree analysis; Probabilistic logic; Computer science; Tree (set theory); Event (particle physics); Fault tree analysis; Probabilistic risk assessment; Monte Carlo method; Quantitative analysis (chemistry); Risk assessment; Risk analysis (engineering); Engineering; Reliability engineering; Artificial intelligence; Statistics; Mathematics","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":[0.0005269035,0.0007419739,0.0009615266,0.0006680341,0.0003862826,0.001302322,0.001484098,0.001180211,0.003609029],"category_scores_gemma":[0.001592053,0.0004217487,0.001057908,0.0006690089,0.0004067908,0.001297328,0.0006748803,0.0009384477,0.0009541688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007551073,"about_ca_system_score_gemma":0.001126842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009058786,"about_ca_topic_score_gemma":0.00677402,"domain_scores_codex":[0.9997295,0.00006173844,0.00002743164,0.00006663514,0.00009069494,0.00002399863],"domain_scores_gemma":[0.9994536,0.0003224722,0.00005133189,0.00004377187,0.0001033295,0.00002545207],"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":[0.00002117098,0.00002906499,0.0003026514,0.00003768647,0.0000261107,0.00004578387,0.00003074293,0.9630566,0.0008989004,0.01674968,0.001051111,0.01775057],"study_design_scores_gemma":[0.000002456951,0.00000396148,0.00003911567,0.000003003569,0.00000576641,0.00000933808,0.000002087111,0.993637,0.00011627,0.005546547,0.0006310855,0.000003339478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004774496,0.00008595199,0.9919458,0.00007597174,0.00003285387,0.00002863166,0.0002865454,0.0006097957,0.002160061],"genre_scores_gemma":[0.4065108,0.0005389545,0.5810533,0.0001153153,0.0001034755,0.0004800286,0.001282101,0.0003263342,0.009589784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009058786,"threshold_uncertainty_score":0.01801211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02287910311179605,"score_gpt":0.2784108357342189,"score_spread":0.2555317326224228,"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."}}