{"id":"W141974873","doi":"","title":"The Aiding of MEMS INS/GPS Integration Using Artificial Intelligence for Land Vehicle Navigation","year":2006,"lang":"en","type":"article","venue":"International MultiConference of Engineers and Computer Scientists","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; GPS/INS; Kalman filter; Computer science; Fuzzy logic; Artificial intelligence; Sensor fusion; Filter (signal processing); Navigation system; Control engineering; GPS signals; Adaptive neuro fuzzy inference system; Fuzzy control system; Machine learning; Engineering; Computer vision; Assisted GPS","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001515633,0.0001828151,0.0001290418,0.0002098731,0.0001396881,0.0002366303,0.0001659079,0.0002408655,0.0006329737],"category_scores_gemma":[0.0004754183,0.00008251722,0.0001571841,0.0001606598,0.0001586948,0.0003645447,0.0001998905,0.0002360676,0.0001670838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001399612,"about_ca_system_score_gemma":0.0001614303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006676675,"about_ca_topic_score_gemma":0.001504459,"domain_scores_codex":[0.9999201,0.00001411722,0.000003881157,0.00001054744,0.00004565524,0.000005561485],"domain_scores_gemma":[0.999892,0.00005033068,0.00001383282,0.00001091173,0.00002969281,0.00000320012],"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":[0.0001599596,0.00006890376,0.004101157,0.0002559748,0.00006187388,0.0002243978,0.0001916764,0.04621282,0.2234236,0.01428219,0.001874051,0.7091435],"study_design_scores_gemma":[0.00003117248,0.0003364956,0.01022492,0.00005461141,0.0001386559,0.0005797907,0.00007971584,0.7619368,0.1751814,0.00905683,0.04233348,0.00004602968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1052993,0.002133275,0.880357,0.000440591,0.0001600816,0.00003264458,0.00003871566,0.0008040036,0.01073436],"genre_scores_gemma":[0.7391761,0.001135882,0.2550895,0.0001314808,0.0001046558,0.00002365502,0.00004562229,0.00003137856,0.004261669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006676675,"threshold_uncertainty_score":0.002117515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191717351308286,"score_gpt":0.2557421609284469,"score_spread":0.2338249874153641,"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."}}