{"id":"W2012300203","doi":"10.1088/0964-1726/16/4/035","title":"Aircraft flight parameter detection based on a neural network using multiple hot-film flow speed sensors","year":2007,"lang":"en","type":"article","venue":"Smart Materials and Structures","topic":"Biomimetic flight and propulsion mechanisms","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Airspeed; Wind tunnel; Artificial neural network; Angle of attack; Flight control surfaces; Wind speed; Engineering; Aerospace engineering; Airflow; Micro air vehicle; Wing; Flow (mathematics); Simulation; Control theory (sociology); Aerodynamics; Automotive engineering; Acoustics; Computer science; Artificial intelligence; Control (management); Mechanical engineering; Mechanics; Physics; Meteorology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003545757,0.0005344567,0.0003623197,0.0003442977,0.0002121085,0.0004145904,0.0004517436,0.0006902523,0.0006067337],"category_scores_gemma":[0.0008643611,0.0003109622,0.0002114948,0.0002555287,0.0003497942,0.000741902,0.0002418034,0.0004014988,0.0001243184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003871593,"about_ca_system_score_gemma":0.0003172111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003732412,"about_ca_topic_score_gemma":0.004129414,"domain_scores_codex":[0.999844,0.00002572424,0.000008879316,0.00005358488,0.00004970024,0.00001819882],"domain_scores_gemma":[0.9996731,0.0001525342,0.00005442093,0.00002312669,0.00008300679,0.00001373456],"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":[0.0006173313,0.0002802906,0.007026561,0.0001704632,0.0001332769,0.0002237333,0.0001304958,0.4473116,0.118855,0.001916718,0.0007981881,0.4225364],"study_design_scores_gemma":[0.000009182832,0.00006304239,0.00100742,0.000004850993,0.00001091148,0.00002281344,0.000004965151,0.9901688,0.008392352,0.0001776495,0.0001301587,0.000007944881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2558208,0.0004052056,0.7405651,0.0001984818,0.0001099976,0.00006269875,0.00006843179,0.0009112011,0.001858062],"genre_scores_gemma":[0.8978926,0.0001862312,0.1001504,0.00004718619,0.00003560578,0.00005894685,0.00005048512,0.00001013149,0.001568418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003732412,"threshold_uncertainty_score":0.007421374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185549854460018,"score_gpt":0.2111260751689434,"score_spread":0.1992705766243432,"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."}}