{"id":"W3002058140","doi":"10.3390/s20030590","title":"Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Filter (signal processing); Inertial navigation system; Fault (geology); Fault detection and isolation; Kalman filter; Real-time computing; GNSS augmentation; Inertial measurement unit; Computer science; Fault coverage; Satellite system; Global Positioning System; Navigation system; Engineering; Fault indicator; Artificial intelligence; Inertial frame of reference; Telecommunications","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":[0.00006489237,0.0001028245,0.0001260928,0.00005713179,0.00006349522,0.00003241727,0.00002843648,0.00005870355,0.000001594499],"category_scores_gemma":[0.0000333724,0.0001078093,0.00002216222,0.00008639064,0.00001374437,0.00008039609,0.00001280179,0.0001212972,0.000007925632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008179463,"about_ca_system_score_gemma":0.000005688968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005999932,"about_ca_topic_score_gemma":0.00007872224,"domain_scores_codex":[0.9994015,0.00001606695,0.0001875409,0.0001592341,0.00007129635,0.0001643879],"domain_scores_gemma":[0.9997824,0.00004011335,0.0000237183,0.00005945842,0.00003346661,0.00006084405],"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":[0.0001354919,0.00001403501,0.0007676627,0.0009828376,0.00005044909,0.00001228122,0.006633713,0.6554786,0.3310445,0.0001204388,0.000137111,0.004622864],"study_design_scores_gemma":[0.0004465504,0.00009583132,0.001259399,0.0001423086,0.000008808483,0.00001349754,0.0005810802,0.9725118,0.02459912,0.0000201241,0.0002178104,0.0001036683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978132,0.00005551397,0.02052296,0.00004767525,0.0002488304,0.0002256068,0.00002414488,0.0003888386,0.0003544098],"genre_scores_gemma":[0.9995384,0.0000303682,0.0002811905,0.00001693094,0.00005636507,0.00002053864,0.000007407808,0.00002102628,0.00002773459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3170332,"threshold_uncertainty_score":0.4396338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410938682150624,"score_gpt":0.2033191217677352,"score_spread":0.189209734946229,"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."}}