{"id":"W2144892186","doi":"10.1109/icsmc.2009.5346205","title":"Diagnosis of hybrid systems: Part 2- residual generator selection and diagnosis in the presence of unreliable residual generators","year":2009,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Automaton; Hybrid system; Computer science; Generator (circuit theory); Fault (geology); Fault detection and isolation; Selection (genetic algorithm); Representation (politics); Algorithm; Artificial intelligence; Machine learning; Power (physics)","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":[0.0004546405,0.0001455545,0.0002821742,0.000136907,0.0000459102,0.00004135326,0.000127799,0.00006184734,0.00001812185],"category_scores_gemma":[0.00009161241,0.0001109016,0.00003468119,0.0003461805,0.00002388498,0.0001302924,0.000008921221,0.0001136493,0.000002080561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003222323,"about_ca_system_score_gemma":0.00001798274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000611586,"about_ca_topic_score_gemma":0.0002554235,"domain_scores_codex":[0.9987184,0.0001585328,0.0004839788,0.0001771282,0.0002604052,0.0002015219],"domain_scores_gemma":[0.9994135,0.0002129062,0.0000699278,0.0001975541,0.00006118834,0.00004487671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001449184,0.0005036292,0.1750953,0.0009921512,0.0002964806,0.00003384116,0.001816578,0.5068795,0.03715027,0.003634264,0.2597778,0.01367516],"study_design_scores_gemma":[0.002160628,0.0008391401,0.0208908,0.0005537813,0.0001143171,0.00008315283,0.001907886,0.1957329,0.7516474,0.0001243339,0.0252106,0.0007350859],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953286,0.003061628,0.0001252877,0.000126908,0.000337879,0.0003954157,0.00001907706,0.00008126794,0.0005239164],"genre_scores_gemma":[0.9986322,0.000778417,0.0000633362,0.00003819042,0.000186309,0.0002315429,0.000002303831,0.00001259371,0.00005514223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7144971,"threshold_uncertainty_score":0.4522434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009473571344771677,"score_gpt":0.2098397626656829,"score_spread":0.2003661913209112,"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."}}