{"id":"W2146845812","doi":"10.1109/tpwrs.2007.907383","title":"Automatic Segmentation of Large Power Systems Into Fuzzy Coherent Areas for Dynamic Vulnerability Assessment","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Hydro-Québec","funders":"","keywords":"Initialization; Electric power system; Phasor measurement unit; Computer science; Cluster analysis; Fuzzy logic; Vulnerability (computing); Data mining; Phasor; Fuzzy clustering; Units of measurement; Set (abstract data type); Fuzzy set; Power (physics); Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001846565,0.0003786703,0.0006489983,0.0003049824,0.0001963906,0.0000885222,0.0002113065,0.0002355226,0.0001102709],"category_scores_gemma":[0.00001148335,0.0003696855,0.0002491765,0.0003829343,0.0000480341,0.0002506695,0.000001734551,0.0002389944,0.0000198479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006749,"about_ca_system_score_gemma":0.0000740628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009157683,"about_ca_topic_score_gemma":0.0001736878,"domain_scores_codex":[0.9969399,0.0002035303,0.001391628,0.0004223768,0.0005542231,0.0004883164],"domain_scores_gemma":[0.9982507,0.000324786,0.0002323574,0.0006916151,0.0002995796,0.0002009776],"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":[0.0001529561,0.001640609,0.0008194454,0.005774643,0.0008604403,0.000005878462,0.005222525,0.9748533,0.007878683,0.001258529,0.0008538058,0.0006792047],"study_design_scores_gemma":[0.002454175,0.000478624,0.002161986,0.0004984672,0.0001342355,0.00002359738,0.005282332,0.9834237,0.003006917,0.00002708701,0.001769395,0.0007395131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06584536,0.0002333149,0.9217118,0.00001861048,0.007224369,0.002574093,0.0003224147,0.0005041974,0.00156581],"genre_scores_gemma":[0.9981651,0.000006875635,0.001007087,0.00001172102,0.000005113736,0.0005174681,0.00003294552,0.00007117145,0.000182467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9323198,"threshold_uncertainty_score":0.9998755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00949918997693194,"score_gpt":0.2848725987692824,"score_spread":0.2753734087923504,"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."}}