{"id":"W836961123","doi":"10.1007/s10291-015-0471-3","title":"Improving MEMS-IMU/GPS integrated systems for land vehicle navigation applications","year":2015,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"York University","keywords":"Global Positioning System; Inertial measurement unit; Attitude and heading reference system; GPS/INS; Inertial navigation system; Computer science; Heading (navigation); Dead reckoning; GPS signals; Real-time computing; Noise (video); Sensor fusion; Simulation; Assisted GPS; Engineering; Inertial frame of reference; Artificial intelligence; Aerospace engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004094595,0.001058563,0.0004812422,0.0007823022,0.0004549807,0.0009374146,0.00117464,0.0009424728,0.007685545],"category_scores_gemma":[0.001090476,0.0003671161,0.0003587874,0.0009165613,0.000134375,0.001465098,0.0005710182,0.0005387948,0.003109546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006053624,"about_ca_system_score_gemma":0.0006253961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003171247,"about_ca_topic_score_gemma":0.006755575,"domain_scores_codex":[0.9993586,0.00006272676,0.00002829793,0.00006647038,0.0004278987,0.00005588693],"domain_scores_gemma":[0.9992901,0.00005352385,0.0000436734,0.00004824845,0.0005478783,0.0000165619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004259665,0.00025866,0.007914496,0.0005383486,0.0001933892,0.0001192519,0.0001205407,0.01516199,0.5802901,0.003112748,0.006949393,0.3849151],"study_design_scores_gemma":[0.0001165954,0.001613381,0.02789033,0.0001176312,0.0006455624,0.0004730821,0.0001851012,0.1804702,0.7108272,0.001199281,0.07638055,0.00008111539],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2790393,0.006450664,0.6747139,0.001060065,0.001305754,0.0003443757,0.0009350559,0.006905923,0.0292451],"genre_scores_gemma":[0.6875567,0.001888507,0.2851492,0.0006163021,0.0004266053,0.0001287484,0.001205629,0.0003362915,0.02269211],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007685545,"threshold_uncertainty_score":0.0257107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02553862432658887,"score_gpt":0.2398204874595552,"score_spread":0.2142818631329663,"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."}}