{"id":"W1933952696","doi":"10.1109/pacrim.1991.160721","title":"An aircraft Kalman filter integrated navigation system prototype","year":2002,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Kalman filter; Computer science; Extended Kalman filter; Navigation system; Computer vision; Artificial intelligence","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":[0.0006036498,0.0002787069,0.000570129,0.0003158781,0.0003780136,0.000505079,0.0007719267,0.0006694458,0.02300672],"category_scores_gemma":[0.001065076,0.0002146003,0.0001749611,0.0001632306,0.0001110574,0.0007085763,0.0002211573,0.0003779368,0.0112579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002135821,"about_ca_system_score_gemma":0.0004575139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003341219,"about_ca_topic_score_gemma":0.004273817,"domain_scores_codex":[0.9996972,0.00002981621,0.00001507191,0.00005506664,0.0001743228,0.00002856316],"domain_scores_gemma":[0.9990646,0.00007490358,0.00001434658,0.0001218616,0.0006598845,0.00006439798],"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":[0.002695517,0.0006616505,0.007824624,0.0002760715,0.0001185051,0.0007802523,0.0004322302,0.007373513,0.1998477,0.003035456,0.09085315,0.6861014],"study_design_scores_gemma":[0.001672529,0.005340635,0.02984429,0.00009421766,0.0003849001,0.001520967,0.0002942198,0.3255134,0.2904597,0.001505999,0.3431543,0.0002149438],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1866498,0.0004943467,0.687072,0.001346114,0.002557731,0.001433309,0.002315627,0.05597352,0.06215763],"genre_scores_gemma":[0.6153497,0.0002162561,0.2284047,0.0005600494,0.0002559945,0.0003995228,0.005511724,0.001391329,0.1479108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02300672,"threshold_uncertainty_score":0.07696509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147610072729874,"score_gpt":0.2106518102154079,"score_spread":0.1991757094881092,"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."}}