{"id":"W3103087864","doi":"10.3390/s20226567","title":"Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Inertial navigation system; Odometry; Real-time computing; Air navigation; Computer science; Kalman filter; Navigation system; Heading (navigation); GNSS augmentation; Extended Kalman filter; Satellite system; Simulation; Engineering; Artificial intelligence; Global Positioning System; Mobile robot; Inertial frame of reference; Telecommunications; Robot; Aerospace engineering","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":[0.0001668143,0.0006690281,0.000312287,0.0009066564,0.0002360251,0.0004256982,0.0005630355,0.0005485929,0.001182784],"category_scores_gemma":[0.0003260616,0.0002382271,0.0003075982,0.0004689595,0.0002199081,0.0009048622,0.00042418,0.0004145093,0.0004262948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003644813,"about_ca_system_score_gemma":0.0004585749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004071004,"about_ca_topic_score_gemma":0.004899309,"domain_scores_codex":[0.999828,0.00001570889,0.000005472137,0.00004405105,0.00008549443,0.00002126707],"domain_scores_gemma":[0.9998947,0.00001490558,0.00001772066,0.000008335756,0.00005638687,0.000007882823],"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":[0.0002463583,0.0001829471,0.0070904,0.0003905781,0.00006175415,0.0002938695,0.0002152422,0.03598956,0.2113945,0.007647515,0.005746601,0.7307406],"study_design_scores_gemma":[0.0000600863,0.0004755659,0.01307588,0.0001472427,0.0001705509,0.0006841517,0.0002016119,0.7849313,0.1511206,0.00497198,0.04403262,0.0001284029],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09873736,0.002630746,0.8838032,0.000376408,0.0006060544,0.0001537849,0.000429474,0.002643366,0.01061959],"genre_scores_gemma":[0.7536469,0.001457208,0.234936,0.0002705342,0.0001701967,0.00009817968,0.0004729147,0.00005720951,0.008890713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004071004,"threshold_uncertainty_score":0.008094609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343364616968989,"score_gpt":0.1965664023825713,"score_spread":0.1831327562128814,"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."}}