{"id":"W2471598254","doi":"","title":"ACCURATE INSIDGPS POSITIONING USING INS DATA DE-NOISING AND AUTOREGRESSIVE (AR) MODELING OF INERTIAL SENSOR ERRORS","year":2019,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive model; Geography; Inertial navigation system; Inertial measurement unit; Geodesy; Computer science; Inertial frame of reference; Cartography; Mathematics; Statistics; Artificial intelligence; Physics","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":[0.0001298432,0.0001257283,0.000194065,0.0000833275,0.00006658146,0.00003848361,0.0001521519,0.00008328461,0.00001805439],"category_scores_gemma":[0.0000780257,0.0001258415,0.0000217091,0.0001062564,0.00002294984,0.0003684667,0.0001150074,0.0001219768,0.000007377209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004601148,"about_ca_system_score_gemma":0.00002173978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001622632,"about_ca_topic_score_gemma":0.000006830997,"domain_scores_codex":[0.9991733,0.00002956784,0.0002897902,0.0001575778,0.000133924,0.0002157802],"domain_scores_gemma":[0.9994109,0.00004425386,0.00006167942,0.0003819885,0.00004901108,0.00005217004],"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":[0.00001191743,0.000006981625,0.0003586776,0.0001596988,0.0000320295,0.00000533611,0.001027501,0.7680587,0.2297382,0.00007680517,0.0000113014,0.0005128059],"study_design_scores_gemma":[0.0002524326,0.00001360145,0.000668212,0.0002537485,0.00003971004,0.00002429136,0.0001960463,0.9909812,0.007293845,0.0001244942,0.000007459336,0.0001450209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772826,0.00009996584,0.02166843,0.00002907362,0.000164725,0.0001387858,0.00002672637,0.00009596397,0.0004937026],"genre_scores_gemma":[0.986737,0.000009327661,0.01306035,0.00001683997,0.00008834914,9.491756e-7,0.00005520091,0.00002757554,0.000004433434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2229224,"threshold_uncertainty_score":0.5131668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02814432922435131,"score_gpt":0.2609868449108953,"score_spread":0.232842515686544,"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."}}