{"id":"W2130368679","doi":"10.1109/vetecs.2008.335","title":"Improved Vehicle Navigation Using Aiding with Tightly Coupled Integration","year":2008,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Global Positioning System; GPS/INS; Heading (navigation); Computer science; GPS signals; Time to first fix; Real-time computing; Inertial navigation system; Assisted GPS; GPS disciplined oscillator; Precision Lightweight GPS Receiver; Geography; Telecommunications; Inertial frame of reference; Geodesy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00003930367,0.00009339717,0.00008125939,0.00003845813,0.0001206557,0.00001701309,0.00003273378,0.00005161255,0.00002512713],"category_scores_gemma":[0.000004226819,0.00007520094,0.00001960958,0.0001762555,0.00001870792,0.000271525,0.000003502932,0.00008763223,0.0000108698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008129088,"about_ca_system_score_gemma":0.00000953765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001933656,"about_ca_topic_score_gemma":0.00002314746,"domain_scores_codex":[0.9995426,0.000007402309,0.0001311418,0.00009110723,0.00009971229,0.0001280264],"domain_scores_gemma":[0.9997933,0.00001175531,0.00002003035,0.00008026114,0.00006053246,0.00003410641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001650314,0.000006497821,0.0003914659,0.00000939751,0.000009324802,0.000003984228,0.0002881061,0.02041801,0.9776519,0.0001530067,0.00002301965,0.001028792],"study_design_scores_gemma":[0.0002231155,0.00002675021,0.0006659681,0.00002050209,0.000006522675,0.00001852864,0.00003358772,0.7243029,0.2745747,0.00001443026,0.00001919857,0.00009382042],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8550773,0.00001544881,0.1434907,0.00000943405,0.00009428524,0.0001045851,6.123832e-7,0.0002706012,0.000937079],"genre_scores_gemma":[0.9951858,0.000005698112,0.004570599,0.000016197,0.00009475766,0.000004017392,0.00002868993,0.00002160829,0.00007261131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7038849,"threshold_uncertainty_score":0.3066605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508973512401986,"score_gpt":0.2105212491955781,"score_spread":0.1954315140715582,"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."}}