{"id":"W2098354438","doi":"10.1017/s0373463307004560","title":"A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units","year":2008,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":179,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; Inertial measurement unit; Inertial navigation system; Calibration; Allan variance; GPS/INS; Computer science; Kinematics; Noise (video); Microelectromechanical systems; Scale factor (cosmology); Position (finance); Orthogonality; Navigation system; Simulation; Assisted GPS; Real-time computing; Orientation (vector space); Artificial intelligence; Standard deviation; Telecommunications; Mathematics","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":[0.0001507691,0.00008467565,0.0001351204,0.00006457892,0.0001039536,0.00002853039,0.00002245404,0.00006805294,9.430523e-7],"category_scores_gemma":[0.0002504904,0.00007622653,0.0000168128,0.0001851334,0.00002708078,0.0003586121,0.000003897083,0.0001224962,1.428728e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004486418,"about_ca_system_score_gemma":0.00003394794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005289846,"about_ca_topic_score_gemma":0.000001941158,"domain_scores_codex":[0.9993981,0.0000131067,0.0002847821,0.00005988909,0.0001476527,0.00009653345],"domain_scores_gemma":[0.9993787,0.00009478218,0.00011918,0.00002972129,0.0003136233,0.00006394429],"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":[0.0007651828,0.00004283034,0.02258325,0.0009152903,0.0001115943,0.00006447308,0.005384786,0.1365397,0.7812563,0.0001523804,0.002281026,0.0499032],"study_design_scores_gemma":[0.003854804,0.0007377834,0.02581009,0.0007944461,0.0001121602,0.001912472,0.0002937874,0.6897981,0.2747977,0.0005861875,0.0008644604,0.0004379394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945179,0.0001177716,0.004865848,0.00007733998,0.0001432793,0.0002092718,0.00001258818,0.00002952257,0.00002647842],"genre_scores_gemma":[0.9979114,0.00004971294,0.001651443,0.00001445403,0.0003318715,0.000003460475,0.00001327406,0.0000162808,0.000008131339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5532584,"threshold_uncertainty_score":0.3108428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0223903892094889,"score_gpt":0.2345444161395114,"score_spread":0.2121540269300225,"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."}}