{"id":"W2915946090","doi":"10.1007/s13198-019-00774-0","title":"An algorithm for performance evaluation of resilience engineering culture based on graph theory and matrix approach","year":2019,"lang":"en","type":"article","venue":"International Journal of Systems Assurance Engineering and Management","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pillar; Strengths and weaknesses; Graph theory; Petrochemical; Resilience (materials science); Matrix (chemical analysis); Computer science; Engineering; Algorithm; Risk analysis (engineering); Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.001930864,0.0001483214,0.0002066907,0.0004938933,0.00003105009,0.0001395884,0.0002633433,0.00003685265,0.000003389037],"category_scores_gemma":[0.00003486818,0.0001209212,0.00005357876,0.000140427,0.00001453801,0.0005798396,0.00003521007,0.00007834854,0.000001088144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004047104,"about_ca_system_score_gemma":0.000008240445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008548236,"about_ca_topic_score_gemma":1.768063e-7,"domain_scores_codex":[0.9986069,0.00001387182,0.0003470584,0.0001866487,0.0007082418,0.0001372953],"domain_scores_gemma":[0.9991405,0.00005402105,0.0002858746,0.0001320162,0.000370139,0.00001744661],"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":[0.00006936076,0.00005940658,0.001666367,0.0007645724,0.0001246437,0.000002434472,0.00004639438,0.9650552,0.0002231809,0.009739588,0.00009539416,0.02215346],"study_design_scores_gemma":[0.001155738,0.00006934268,0.01032811,0.0005806462,0.0000811787,0.000005366032,0.0004105147,0.9839256,0.00003603618,0.00004014721,0.003226926,0.0001404236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5705218,0.001394906,0.4230936,0.00008393752,0.002549193,0.001349311,0.000008754105,0.0000472258,0.0009512976],"genre_scores_gemma":[0.9941833,0.0001921294,0.005127288,0.00003704064,0.0003330822,0.00004121559,0.00000724371,0.00001582463,0.00006286685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4236616,"threshold_uncertainty_score":0.4931025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00607734035967177,"score_gpt":0.2344651905958968,"score_spread":0.228387850236225,"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."}}