{"id":"W2937246245","doi":"10.1049/iet-gtd.2019.0133","title":"Enhanced probabilistic approach for substation reliability assessment","year":2019,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Manitoba Hydro","funders":"","keywords":"Reliability engineering; Probabilistic logic; Reliability (semiconductor); Contingency; Power flow; Electric power system; Computer science; Probabilistic method; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005769309,0.0001980583,0.0002292457,0.00002839235,0.0001136994,0.00005687584,0.0001057065,0.0001611774,0.00005990098],"category_scores_gemma":[0.00003932866,0.0001833922,0.0001215142,0.0001774972,0.00002459547,0.0002636204,0.000004658295,0.0001271343,0.00001558694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003717513,"about_ca_system_score_gemma":0.00006550398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004568341,"about_ca_topic_score_gemma":0.000001759156,"domain_scores_codex":[0.9985318,0.00007632268,0.0004664269,0.0004023581,0.0002481435,0.0002749482],"domain_scores_gemma":[0.9992387,0.00005137661,0.00006078354,0.0003188815,0.0002313987,0.00009887401],"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":[0.00007236558,0.0002274065,0.00009899483,0.00132054,0.00002648242,1.36065e-7,0.0002914417,0.5837131,0.3903746,0.006356025,0.004147397,0.01337155],"study_design_scores_gemma":[0.000829341,0.0001040845,0.001240297,0.00003824271,0.00002515234,0.000001211464,0.00004467605,0.9187666,0.06606264,0.0004797361,0.01213915,0.0002689296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1484683,0.00006773962,0.8481002,0.00009815061,0.0005430283,0.001577172,0.0001822096,0.0002551947,0.0007079704],"genre_scores_gemma":[0.9852917,0.00003476559,0.009374855,0.00001681714,0.00009582788,0.0004663131,0.004528704,0.00002225208,0.0001687683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8387254,"threshold_uncertainty_score":0.7478516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142333794033186,"score_gpt":0.2356610779331794,"score_spread":0.2242377399928476,"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."}}