{"id":"W2326864419","doi":"10.3166/jesa.48.185-210","title":"Hybrid Method for Fault Detection and Identification Based on State Observers and Decision Trees","year":2014,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Decision tree; State (computer science); Computer science; Fault detection and isolation; Fault (geology); Artificial intelligence; Pattern recognition (psychology); Data mining; Algorithm; Seismology; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008062234,0.000668458,0.001295902,0.0006809942,0.0004624318,0.0007005168,0.000832964,0.0007508508,0.001708298],"category_scores_gemma":[0.00155962,0.0004456401,0.0007167661,0.0004830818,0.0004163147,0.0008421511,0.000590454,0.001051004,0.0002872222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006496639,"about_ca_system_score_gemma":0.001100061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005545745,"about_ca_topic_score_gemma":0.005377044,"domain_scores_codex":[0.9994326,0.0001324923,0.00004593679,0.0001252458,0.0002153356,0.00004836259],"domain_scores_gemma":[0.9988525,0.0007051153,0.00008812075,0.00005502998,0.000267398,0.00003180429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005721541,0.0001536781,0.0008285417,0.0003289726,0.0002131156,0.00009832295,0.0001999466,0.4588094,0.01729262,0.01692978,0.001171361,0.5034021],"study_design_scores_gemma":[0.00001336421,0.0000409248,0.0001268395,0.000005310591,0.00001564259,0.00001082002,0.000003677834,0.9968377,0.0009277907,0.001726784,0.0002843124,0.000006707277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006276749,0.0001355146,0.9929055,0.00002881803,0.00003696717,0.00002015977,0.00001120654,0.0002279612,0.0003570459],"genre_scores_gemma":[0.6455569,0.0002453462,0.350376,0.00008178241,0.00008205459,0.0002168123,0.0001222025,0.00006101561,0.003257847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005545745,"threshold_uncertainty_score":0.01102692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133174772148298,"score_gpt":0.2497926166285269,"score_spread":0.2384608689070439,"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."}}