{"id":"W3012983464","doi":"10.1002/fam.2824","title":"Burning biases: Mitigating cognitive biases in fire engineering","year":2020,"lang":"en","type":"article","venue":"Fire and Materials","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Fire protection engineering; Cognitive bias; Heuristics; Probabilistic logic; Fire protection; Risk analysis (engineering); Cognition; Computer science; Engineering design process; Resource (disambiguation); Fire safety; Engineering; Architectural engineering; Artificial intelligence; Psychology; Civil engineering","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":[0.02636944,0.0008564428,0.000793181,0.001930882,0.001590924,0.005588751,0.001394606,0.001137311,0.002946453],"category_scores_gemma":[0.153205,0.0003472134,0.0007671217,0.001147188,0.002721076,0.00387584,0.004168807,0.002324425,0.000349637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002311985,"about_ca_system_score_gemma":0.004345144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004251554,"about_ca_topic_score_gemma":0.006316555,"domain_scores_codex":[0.9853315,0.008817305,0.0006908968,0.001010547,0.003372249,0.0007774917],"domain_scores_gemma":[0.7962648,0.1566287,0.0208837,0.00927233,0.0142387,0.002711718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001867147,0.002182397,0.1716687,0.002248632,0.0006186673,0.0003625715,0.03184547,0.0223363,0.00645464,0.04203839,0.006411089,0.711966],"study_design_scores_gemma":[0.00072669,0.004327563,0.3041512,0.004884802,0.001776112,0.0008135244,0.04846746,0.1377696,0.02083818,0.4202761,0.05519442,0.0007743948],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8847073,0.001839181,0.07247794,0.005329672,0.000272094,0.0004972101,0.00009318189,0.000307079,0.03447628],"genre_scores_gemma":[0.9735491,0.0004308833,0.02416996,0.0005992026,0.00008582415,0.0001837571,0.00005412614,0.00002847217,0.0008987581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02636944,"threshold_uncertainty_score":0.1394566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1241739252698512,"score_gpt":0.3460194262357056,"score_spread":0.2218455009658544,"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."}}