{"id":"W4206158718","doi":"10.2514/6.2022-1669","title":"Aerodynamic state estimation from sparse sensor data by pairing Bayesian statistics with transition networks","year":2022,"lang":"en","type":"article","venue":"AIAA SCITECH 2022 Forum","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Aerodynamics; Computer science; Control theory (sociology); Noise (video); Angle of attack; Pairing; Flow (mathematics); State (computer science); Wake; Algorithm; Artificial intelligence; Engineering; Control (management); Aerospace engineering; Physics; Mechanics","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":[0.0011988,0.0009430355,0.001021271,0.0009292637,0.0003843683,0.0009227761,0.001064954,0.0008190286,0.0017247],"category_scores_gemma":[0.005988275,0.0008933601,0.0008156869,0.0008618893,0.0007554661,0.001322796,0.001418311,0.001926207,0.0005401143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007833436,"about_ca_system_score_gemma":0.001164131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007037033,"about_ca_topic_score_gemma":0.006904254,"domain_scores_codex":[0.9994412,0.0001805962,0.00003121544,0.0001360871,0.0001554201,0.00005551523],"domain_scores_gemma":[0.997541,0.001709989,0.0002583641,0.0001607279,0.0002656572,0.00006433424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006957333,0.0000408477,0.0008261522,0.0000461743,0.00004637846,0.00003056444,0.00002955744,0.9355102,0.00126367,0.006329833,0.0006088853,0.05519826],"study_design_scores_gemma":[0.000001965553,0.000005987077,0.00008904005,0.000002742919,0.000001831725,0.000003752106,0.000001538499,0.9971064,0.000260084,0.002417261,0.0001066007,0.000002902355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007167135,0.0000782822,0.9917113,0.00009402181,0.00001975614,0.00002704677,0.00006489754,0.0002852283,0.0005523966],"genre_scores_gemma":[0.6377411,0.0004126076,0.3560232,0.0001920796,0.0001952561,0.0004123411,0.0009758163,0.0001848692,0.003862715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007037033,"threshold_uncertainty_score":0.01399219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009711020802161647,"score_gpt":0.223332186849832,"score_spread":0.2136211660476704,"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."}}