{"id":"W2094522975","doi":"10.1017/s0373463304003005","title":"Wavelet Analysis For Improving INS and INS/DGPS Navigation Accuracy","year":2005,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial navigation system; GPS/INS; Noise (video); Global Positioning System; Inertial measurement unit; Computer science; Accelerometer; Wavelet; Inertial frame of reference; Real-time computing; Assisted GPS; Artificial intelligence; Telecommunications","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":[0.001131635,0.0006753699,0.0004730074,0.0006572642,0.0001947667,0.0005421209,0.0003175003,0.0004371823,0.001273361],"category_scores_gemma":[0.003698324,0.0002532105,0.0003967512,0.001104176,0.0002343449,0.000653359,0.000444067,0.0005285045,0.0007082783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000172963,"about_ca_system_score_gemma":0.000255405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001283892,"about_ca_topic_score_gemma":0.001083074,"domain_scores_codex":[0.9994819,0.0001111894,0.00004111671,0.00008031153,0.0002402074,0.0000453565],"domain_scores_gemma":[0.9992484,0.0003096018,0.00006083812,0.00009579883,0.000268685,0.00001654792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005776226,0.0001150283,0.004563209,0.0003193589,0.00009944211,0.0002769408,0.0002570237,0.1005168,0.2333881,0.005262445,0.002107115,0.652517],"study_design_scores_gemma":[0.00004110534,0.0001891509,0.00865729,0.00002856389,0.00009418227,0.000201622,0.00008301084,0.8631467,0.1183292,0.002190938,0.007002133,0.00003604394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1082736,0.0003888054,0.8889382,0.0001280708,0.00008838154,0.00002400518,0.0001332549,0.0005871548,0.001438578],"genre_scores_gemma":[0.4703239,0.0009669286,0.5254173,0.00005289708,0.00008451696,0.00005921887,0.000661663,0.0002236655,0.002209853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001283892,"threshold_uncertainty_score":0.005984724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021691983390901,"score_gpt":0.2493931080296057,"score_spread":0.2391761881956966,"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."}}