{"id":"W2102057962","doi":"10.1109/ias.1989.96890","title":"An expert system for substation grounding design of an industrial power system using fuzzy concepts","year":2003,"lang":"en","type":"article","venue":"Conference Record of the IEEE Industry Applications Society Annual Meeting","topic":"Elevator Systems and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Expert system; Ground; Fuzzy logic; Computer science; Set (abstract data type); Semantics (computer science); Fuzzy set; Fuzzy control system; Artificial intelligence; Data mining; Systems engineering; Engineering; Programming language; Electrical engineering","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":[0.00111103,0.0002384941,0.0004220643,0.00003886503,0.0003442683,0.00005602824,0.0004481153,0.0006004264,0.000002809178],"category_scores_gemma":[0.00004640286,0.0002216628,0.0001665019,0.0003678167,0.0001051216,0.0003623633,0.00001215718,0.000403482,5.638371e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003149659,"about_ca_system_score_gemma":0.0002169582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001215763,"about_ca_topic_score_gemma":0.000004011122,"domain_scores_codex":[0.9980693,0.0002809124,0.0007781796,0.000294837,0.0002505767,0.0003262066],"domain_scores_gemma":[0.9981478,0.0002378381,0.0005107754,0.0005244157,0.0004688521,0.0001102866],"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":[0.00009273922,0.0002314808,0.002265612,0.001777887,0.0005426055,6.871076e-7,0.01326965,0.1598033,0.7748129,0.02919671,0.0004378755,0.01756856],"study_design_scores_gemma":[0.002673651,0.0003269538,0.00007909593,0.002993834,0.0002707711,0.00004590686,0.2588618,0.490426,0.2415613,0.0001893286,0.001476385,0.001094972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6727099,0.00008496369,0.3243229,0.000007304923,0.0007435335,0.001582344,0.00009237036,0.0001563081,0.0003003943],"genre_scores_gemma":[0.9888306,0.000002692053,0.01035116,0.00000559269,0.0002890355,0.0004593565,0.000005494344,0.00004548995,0.0000105777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5332516,"threshold_uncertainty_score":0.9039147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05313913634022318,"score_gpt":0.2912852553318097,"score_spread":0.2381461189915865,"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."}}