{"id":"W2971039689","doi":"10.1049/iet-stg.2019.0169","title":"Modelling cascading failure of a CPS for topological resilience enhancement","year":2019,"lang":"en","type":"article","venue":"IET Smart Grid","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Key Technologies Research and Development Program; State Grid Corporation of China","keywords":"Cascading failure; Interdependent networks; Interdependence; Resilience (materials science); Computer science; Distributed computing; Process (computing); Topology (electrical circuits); Adaptation (eye); Node (physics); Reliability engineering; Network topology; Percolation (cognitive psychology); Telecommunications network; Computer network; Complex network; Electric power system; Engineering; Power (physics)","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":[0.0003429645,0.000902779,0.0004877095,0.000736184,0.0004992854,0.0007782732,0.0008508194,0.001210039,0.002299482],"category_scores_gemma":[0.001211069,0.0003314909,0.0009454213,0.00053514,0.0009779712,0.001002094,0.001161025,0.001017935,0.0001660448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008265505,"about_ca_system_score_gemma":0.0008233509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01169581,"about_ca_topic_score_gemma":0.005368042,"domain_scores_codex":[0.9997804,0.00008173816,0.000009628597,0.00004471242,0.00004222425,0.00004137148],"domain_scores_gemma":[0.9995995,0.0002095565,0.00007907866,0.00002770318,0.00005160942,0.00003248193],"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":[0.00001138785,0.000009393722,0.0003365907,0.00001717645,0.0000131505,0.0000777835,0.0000289764,0.990778,0.0007639476,0.006857118,0.0001098951,0.0009967104],"study_design_scores_gemma":[0.000001986268,0.000008334666,0.00008945275,0.000002062056,0.000004316959,0.000008421469,0.000009930848,0.9974878,0.0000662631,0.002132548,0.0001865675,0.000002357031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2033798,0.0006748823,0.7762891,0.0007453315,0.0001321185,0.0001340053,0.0002558656,0.000397307,0.01799156],"genre_scores_gemma":[0.9809337,0.0003570584,0.01507325,0.00003574882,0.00003024785,0.00009588501,0.00005521926,0.00003428979,0.003384627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01169581,"threshold_uncertainty_score":0.02325547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008525364621628558,"score_gpt":0.2215239301747419,"score_spread":0.2129985655531134,"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."}}