{"id":"W2786459959","doi":"10.1139/tcsme-2013-0039","title":"PERFORMANCE ANALYSIS OF AN AKF BASED TIGHTLY-COUPLED INS/GNSS INTEGRATED SCHEME WITH NHC FOR LAND VEHICULAR APPLICATIONS","year":2013,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"GNSS positioning and interference","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"GNSS applications; Scheme (mathematics); Computer science; A priori and a posteriori; Extended Kalman filter; Kalman filter; Global Positioning System; Telecommunications; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000693332,0.0001252352,0.0002198651,0.00008252268,0.0001127436,0.00001770178,0.0002141742,0.0001015936,0.00002398338],"category_scores_gemma":[0.000006488423,0.0001031783,0.0003191426,0.0005854174,0.00002718387,0.0001087723,0.000001440559,0.000146015,5.228038e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001302503,"about_ca_system_score_gemma":0.00008069654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002661286,"about_ca_topic_score_gemma":0.004896297,"domain_scores_codex":[0.9993803,0.000003242349,0.000202655,0.0001265547,0.00008854395,0.0001987176],"domain_scores_gemma":[0.9993848,0.00004728303,0.00003185755,0.0002306262,0.0001490543,0.0001563753],"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":[0.000005193651,0.00001631168,0.00002919537,0.0001760136,0.0006353443,1.145329e-8,0.00007619536,0.980394,0.01799396,0.0002624381,0.00001344703,0.000397886],"study_design_scores_gemma":[0.0002736293,0.00007638377,0.0003464683,0.00004367195,0.000392763,3.701813e-7,0.00002793265,0.9761414,0.02237537,0.00001206579,0.0001883273,0.0001215948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.20721,0.00001389633,0.791866,0.00009620981,0.00003604306,0.0005063993,0.0001885041,0.00006929634,0.00001368364],"genre_scores_gemma":[0.9573066,0.000002872786,0.04210674,0.00002878822,0.00001076526,0.0004481487,0.00006058907,0.00002619991,0.000009320845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7500966,"threshold_uncertainty_score":0.4207489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006221721740349755,"score_gpt":0.1793774818511153,"score_spread":0.1731557601107655,"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."}}