{"id":"W2317221425","doi":"10.2514/6.2001-4260","title":"In-flight technique for calibrating air data systems using Kalman filtering and smoothing","year":2001,"lang":"en","type":"article","venue":"AIAA Atmospheric Flight Mechanics Conference and Exhibit","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Biological Sciences","funders":"","keywords":"Kalman filter; Smoothing; Computer science; Extended Kalman filter; Fast Kalman filter; Artificial intelligence; Computer vision","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":[0.001216471,0.000692641,0.0006929503,0.001450183,0.0008982007,0.0009954865,0.0007896714,0.0008630941,0.007847416],"category_scores_gemma":[0.003924915,0.0004268279,0.000328557,0.0009842536,0.0003101057,0.001015514,0.0006236415,0.001053045,0.003628506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003302283,"about_ca_system_score_gemma":0.0008611878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005533598,"about_ca_topic_score_gemma":0.008906647,"domain_scores_codex":[0.9992188,0.0001712873,0.00004594282,0.0001316851,0.0003709002,0.00006127564],"domain_scores_gemma":[0.9978268,0.000332671,0.0001570469,0.000463585,0.001169299,0.00005061448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003354385,0.0001618554,0.008411043,0.0002950388,0.0001345028,0.0003498397,0.0004433234,0.04083874,0.08603465,0.007359167,0.009587524,0.8460489],"study_design_scores_gemma":[0.00007608954,0.0003909589,0.0202506,0.0000531873,0.0001908292,0.001174279,0.0002356806,0.7134693,0.1880969,0.004213545,0.07173689,0.0001118063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02633643,0.0002065636,0.9653819,0.0001577143,0.0003467621,0.00007981656,0.0001615627,0.002331888,0.00499727],"genre_scores_gemma":[0.4225468,0.0003672834,0.5480252,0.0001446285,0.0002303138,0.00008518868,0.0006400201,0.0005237185,0.02743692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007847416,"threshold_uncertainty_score":0.02625227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03090925251884515,"score_gpt":0.2446900276994944,"score_spread":0.2137807751806492,"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."}}