{"id":"W4224279839","doi":"10.1111/risa.13925","title":"Application of data mining to minimize fire‐induced domino effect risks","year":2022,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Domino effect; Fault tree analysis; Reliability engineering; Risk analysis (engineering); Reliability (semiconductor); Risk management; Domino; Risk assessment; Failure mode and effects analysis; Engineering; Computer science; Computer security; Business","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":[0.006900047,0.001283186,0.00177936,0.004561437,0.0006780318,0.001729656,0.00157962,0.0009452254,0.000610597],"category_scores_gemma":[0.01816081,0.0006054325,0.001826427,0.002360984,0.000548537,0.001619292,0.001193985,0.001223724,0.0001074514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200314,"about_ca_system_score_gemma":0.002445299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003017046,"about_ca_topic_score_gemma":0.002963909,"domain_scores_codex":[0.9969374,0.001258502,0.0003749968,0.0005426119,0.0007460355,0.0001405352],"domain_scores_gemma":[0.9831371,0.01201254,0.001717013,0.001050247,0.001863483,0.0002197105],"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":[0.0003040188,0.0004488379,0.03313824,0.0007769762,0.0007716492,0.0003337451,0.0002195524,0.7284259,0.002885547,0.0135258,0.001532044,0.2176378],"study_design_scores_gemma":[0.00002635086,0.0001345217,0.002056966,0.00008241139,0.0001049463,0.00008918023,0.00008357828,0.9779992,0.002644272,0.01550352,0.001256747,0.00001835388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08726501,0.001161265,0.9065496,0.0009298447,0.00004152322,0.0004098963,0.0009321984,0.0005097298,0.002200931],"genre_scores_gemma":[0.6532909,0.0006494887,0.3437532,0.0001491559,0.00004731017,0.0003334989,0.001290001,0.00003863244,0.0004477903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006900047,"threshold_uncertainty_score":0.03649133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1528221931519485,"score_gpt":0.4317731946390445,"score_spread":0.2789510014870961,"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."}}