{"id":"W4280642526","doi":"10.1016/j.ast.2022.107631","title":"Enhancing navigation integrity for Urban Air Mobility with redundant inertial sensors","year":2022,"lang":"en","type":"article","venue":"Aerospace Science and Technology","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Key Research and Development Program of China; University of Toronto; National Natural Science Foundation of China","keywords":"GNSS applications; Inertial measurement unit; Air navigation; Computer science; Inertial navigation system; Global Positioning System; Units of measurement; Real-time computing; Sensor fusion; GNSS augmentation; Satellite system; Inertial frame of reference; Telecommunications; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003982021,0.0006866771,0.0004595343,0.0003044266,0.0003399101,0.0005759161,0.0005596744,0.0005047678,0.001002779],"category_scores_gemma":[0.001970729,0.000196718,0.000272769,0.0003417921,0.0003861091,0.001139406,0.000916236,0.0005251357,0.0002888002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000257926,"about_ca_system_score_gemma":0.0006584097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001799904,"about_ca_topic_score_gemma":0.002491497,"domain_scores_codex":[0.9995474,0.00008796141,0.0000157246,0.00007616645,0.0001796485,0.00009300727],"domain_scores_gemma":[0.9993962,0.0001575921,0.0001224388,0.0001258846,0.0001766737,0.00002114853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008153777,0.0001486263,0.007053636,0.000165765,0.0001169537,0.0001933536,0.0002128141,0.5750942,0.1245808,0.01614045,0.002084702,0.2733933],"study_design_scores_gemma":[0.0000125956,0.0002545945,0.002225511,0.00001601009,0.00003491001,0.0001286397,0.00006932072,0.9639149,0.02901356,0.002837049,0.001476551,0.00001634786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2104822,0.0004013057,0.783271,0.0003014316,0.0001169098,0.00001845814,0.0001174907,0.0003834625,0.004907789],"genre_scores_gemma":[0.9726916,0.0001185568,0.02592252,0.0000306486,0.00003156726,0.00000946077,0.00008650166,0.00002189803,0.001087245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001799904,"threshold_uncertainty_score":0.003578782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004792830021528959,"score_gpt":0.203483773310731,"score_spread":0.1986909432892021,"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."}}