{"id":"W2063624295","doi":"10.1109/pmaps.2006.360220","title":"Utilizing Bulk Electric System Reliability Performance Index Probability Distributions in a Performance Based Regulation Framework","year":2006,"lang":"en","type":"article","venue":"","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Monte Carlo method; Index (typography); Incentive; Probability distribution; Computer science; Electric power industry; Power (physics); Engineering; Statistics; Electricity; Mathematics; Electrical engineering; Economics","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":[0.008334154,0.00134511,0.0008430082,0.001594183,0.000364085,0.002350848,0.001517053,0.001013019,0.002008339],"category_scores_gemma":[0.0203799,0.000424326,0.0007431974,0.001127556,0.002503598,0.004674833,0.00112016,0.002253684,0.0004895917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001471616,"about_ca_system_score_gemma":0.001054247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001181322,"about_ca_topic_score_gemma":0.0006003238,"domain_scores_codex":[0.9962691,0.001723414,0.0001116113,0.0004728704,0.00125424,0.0001686522],"domain_scores_gemma":[0.9891321,0.007828119,0.0009924915,0.001042511,0.0008997997,0.0001049615],"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":[0.00002850958,0.00004184375,0.0005229927,0.00003092827,0.00001982039,0.00005970444,0.00007943404,0.591898,0.0009626945,0.3862852,0.0004518205,0.01961904],"study_design_scores_gemma":[0.000006999184,0.0000581047,0.0001772618,0.00001232856,0.000008593934,0.00003996652,0.00002056451,0.89004,0.0005510676,0.1081026,0.0009651061,0.00001741436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004555634,0.00005672994,0.9920844,0.0001125116,0.0000102183,0.00002245111,0.00002292103,0.0001013098,0.003033941],"genre_scores_gemma":[0.7927007,0.0005756409,0.2021343,0.0001586957,0.0001847485,0.0003770617,0.0001127992,0.0001420144,0.003614068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008334154,"threshold_uncertainty_score":0.04407579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006021948229836808,"score_gpt":0.1830852808926177,"score_spread":0.1770633326627809,"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."}}