{"id":"W2001582519","doi":"10.1109/pesgm.2012.6345123","title":"Gene expression programming for static security assessment of power systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gene expression programming; Computer science; Machine learning; Electric power system; Data mining; Class (philosophy); Artificial intelligence; Artificial neural network; Network security; Power (physics); Algorithm; Computer security","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":[0.0002142848,0.00008598085,0.0001266266,0.00002680686,0.0000194228,0.00001565513,0.0000512306,0.00004686563,0.00002638478],"category_scores_gemma":[0.00001181114,0.0000761523,0.00003873542,0.00005410949,0.000008519884,0.0001816607,0.00001548307,0.00004638291,0.000003386386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007929448,"about_ca_system_score_gemma":0.000007527759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005082883,"about_ca_topic_score_gemma":2.833613e-7,"domain_scores_codex":[0.9993609,0.00001178474,0.0001953998,0.00006704248,0.0001231568,0.0002417184],"domain_scores_gemma":[0.9996923,0.00003278524,0.00003112325,0.0001277696,0.0000485329,0.00006745519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006032321,0.001524766,0.04346721,0.006631976,0.0004104095,0.00000233807,0.002954246,0.07054067,0.8133082,0.02676753,0.02944836,0.004883908],"study_design_scores_gemma":[0.001703761,0.0004637266,0.009992117,0.0003009397,0.0001045121,0.00001063334,0.001634273,0.4883932,0.4654493,0.0001188157,0.03093904,0.000889631],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3749039,0.0002489473,0.6219878,0.00000492759,0.0005999177,0.0005693771,0.00006534598,0.0001801669,0.001439606],"genre_scores_gemma":[0.9692452,0.000002505205,0.0305413,0.00000173298,0.00002655799,0.000092788,0.00005845139,0.00001609336,0.00001537264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5943413,"threshold_uncertainty_score":0.3105401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01152131755919288,"score_gpt":0.2740046208439079,"score_spread":0.262483303284715,"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."}}