{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01039737,0.0002219406,0.001131674,0.001424647,0.0006474247,0.0001028594,0.003267229,0.00006652581,0.00112131],"category_scores_gemma":[0.002742352,0.0001791867,0.0007683056,0.01281288,0.00004783135,0.0002352673,0.001472662,0.0002364459,0.0002115533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007031469,"about_ca_system_score_gemma":0.00006771878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004888292,"about_ca_topic_score_gemma":0.0004303362,"domain_scores_codex":[0.9932036,0.00160909,0.00126425,0.001272368,0.002335722,0.0003149038],"domain_scores_gemma":[0.991156,0.003526055,0.001007872,0.003911792,0.0001968078,0.0002014412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001864675,0.00009256401,0.311078,0.00000209886,0.001508481,0.000003786118,0.0006906983,0.06832653,0.0005268242,0.00001609826,0.002541846,0.6150267],"study_design_scores_gemma":[0.0006135909,0.0001586982,0.1860303,0.000002069414,0.007311453,0.000001527487,0.002230886,0.7909364,0.0005369796,0.0006934399,0.01111386,0.0003708419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8817257,0.0002477974,0.115918,0.0006269389,0.00007259672,0.0002523559,0.0005495339,0.00003288251,0.0005742025],"genre_scores_gemma":[0.9911022,0.00008675187,0.007950134,0.00006986155,0.00005758776,0.0000780964,0.0002164605,0.00001352833,0.0004254475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7226098,"threshold_uncertainty_score":0.9997918,"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."}}