{"id":"W2509194620","doi":"10.1007/s10694-016-0619-x","title":"Real-Time Forecasting of Building Fire Growth and Smoke Transport via Ensemble Kalman Filter","year":2016,"lang":"en","type":"article","venue":"Fire Technology","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Smoke; Ensemble Kalman filter; Environmental science; Computer science; Kalman filter; Simulation; Meteorology; Engineering; Extended Kalman filter; Artificial intelligence; Waste management","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":[0.0005049221,0.0005247581,0.0006966388,0.0005239507,0.0002716255,0.0005615906,0.0004691542,0.0005826273,0.0004392605],"category_scores_gemma":[0.001538087,0.0003771981,0.0005183601,0.0006273926,0.0001798581,0.001079006,0.0003201508,0.0008358955,0.0001456106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004926195,"about_ca_system_score_gemma":0.0005676408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0343389,"about_ca_topic_score_gemma":0.03105694,"domain_scores_codex":[0.999872,0.00001736559,0.00001025061,0.00003848391,0.00004004179,0.00002181124],"domain_scores_gemma":[0.99946,0.00025262,0.00008386842,0.0000464109,0.0001334245,0.0000236486],"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":[0.00006043577,0.00004201466,0.005670521,0.00001897093,0.00004329377,0.00002091702,0.00001968153,0.9682805,0.002660389,0.0004312447,0.0002291892,0.02252281],"study_design_scores_gemma":[0.000001250667,0.00000263165,0.001023876,6.986571e-7,0.000002774753,0.000001238021,0.00000141584,0.9986647,0.0001962758,0.00008075647,0.00002245629,0.000001883972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6356193,0.0003797159,0.361382,0.0001403833,0.0001057017,0.00001384353,0.0003980694,0.0006313766,0.001329572],"genre_scores_gemma":[0.9871259,0.0001111638,0.01198114,0.000008408952,0.00001539952,0.00000883243,0.0002779923,0.00001413881,0.0004570073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0343389,"threshold_uncertainty_score":0.06827801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134206882565012,"score_gpt":0.2063278091294432,"score_spread":0.1949857403037931,"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."}}