{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0007738842,0.0001205861,0.000370379,0.00006933772,0.00009171898,0.0002613599,0.000147192,0.00005542206,0.0005553639],"category_scores_gemma":[0.01218609,0.00009437159,0.00005067314,0.0004559679,0.00002959701,0.0002480008,0.0001047266,0.00005806864,0.00009230104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006083082,"about_ca_system_score_gemma":0.00001896896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00018187,"about_ca_topic_score_gemma":0.00001250093,"domain_scores_codex":[0.9985596,0.0001133149,0.0005022825,0.000325749,0.0002939867,0.0002050804],"domain_scores_gemma":[0.9977039,0.001902456,0.0001276174,0.0000946822,0.00005734103,0.0001139757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005709776,0.0001620993,0.03863197,0.0002326443,0.000304432,0.001001768,0.04518906,0.01031397,0.3291032,0.0004066022,0.007166855,0.5669165],"study_design_scores_gemma":[0.005140268,0.0008622226,0.1502024,0.005700899,0.0003970065,0.00007611551,0.04133695,0.2591044,0.5157862,0.005264367,0.01315574,0.002973482],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980175,0.0003842028,0.0001384477,0.001047717,0.00009854887,0.00008110554,0.00007909502,0.00003368669,0.0001196674],"genre_scores_gemma":[0.9988694,0.0002303122,0.0002912406,0.0003648055,0.0001500167,0.000008986417,0.00002087937,0.000008578421,0.00005576507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.563943,"threshold_uncertainty_score":0.9961347,"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."}}