{"id":"W2164735154","doi":"10.1109/tpwrs.2009.2021225","title":"Fuzzy Partitioning of a Real Power System for Dynamic Vulnerability Assessment","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Hydro-Québec","funders":"","keywords":"Medoid; Electric power system; Computer science; Context (archaeology); Cluster analysis; Fuzzy logic; Vulnerability (computing); Grid; Data mining; Fuzzy clustering; Real-time computing; Power (physics); Machine learning; Artificial intelligence; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00106903,0.0007706857,0.0006937158,0.00131691,0.0005713579,0.000828304,0.0006544187,0.0005702075,0.00214749],"category_scores_gemma":[0.002734161,0.0002972347,0.000799205,0.0007750109,0.000554179,0.0008778764,0.0006464894,0.0006689707,0.0003281319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001242226,"about_ca_system_score_gemma":0.0008281301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006802098,"about_ca_topic_score_gemma":0.006626153,"domain_scores_codex":[0.9994804,0.0002025584,0.00002987021,0.0001204802,0.0001269401,0.00003978631],"domain_scores_gemma":[0.9994411,0.0002975578,0.0000565066,0.00005607922,0.0001210761,0.00002771303],"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":[0.0001192204,0.0000331306,0.0007211649,0.00008324021,0.00005517606,0.0000681488,0.0001793721,0.9073991,0.003735174,0.0114678,0.001004269,0.07513423],"study_design_scores_gemma":[0.000004447022,0.00002019096,0.0002834252,0.000007496206,0.000006141451,0.0000167022,0.00003646918,0.9933169,0.0006434894,0.004946534,0.0007106681,0.000007492604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02229776,0.000172451,0.9757673,0.00007806502,0.00001894994,0.00005036109,0.00007265836,0.0001152369,0.001427188],"genre_scores_gemma":[0.6096897,0.0001814375,0.387534,0.00006622732,0.00003199623,0.0001529865,0.0003354889,0.00005456462,0.001953707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006802098,"threshold_uncertainty_score":0.01352501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01076424841988341,"score_gpt":0.2603378092586532,"score_spread":0.2495735608387698,"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."}}