{"id":"W4416788416","doi":"10.1016/j.ifacol.2025.11.570","title":"Brain-Inspired Decision Learning for Fault-Tolerant Flight Control under Active/Passive Wing Deformations","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"China Scholarship Council","keywords":"Aerodynamics; Control reconfiguration; Control system; Controller (irrigation); Hierarchical control system; Control (management); Key (lock); Stability (learning theory); Parallels; Control theory (sociology)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002333921,0.0003923112,0.0005940679,0.0002946325,0.0003600233,0.00009007584,0.000279153,0.0002290626,0.00003425856],"category_scores_gemma":[0.0005184819,0.0003643841,0.0002801092,0.0003405114,0.00005014424,0.0003959553,0.00003981648,0.0004096342,0.00006401916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003786425,"about_ca_system_score_gemma":0.00009225447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001800472,"about_ca_topic_score_gemma":0.0001193594,"domain_scores_codex":[0.9981228,0.00006916637,0.0006176587,0.0003608222,0.0002814361,0.0005481659],"domain_scores_gemma":[0.9978885,0.001279753,0.0001280892,0.0003007523,0.0002706793,0.0001321951],"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":[0.0003822595,0.0000900005,0.0001671263,0.0001652733,0.0007753508,0.00001297987,0.000830853,0.8626014,0.04993135,0.001154908,0.0002930927,0.08359542],"study_design_scores_gemma":[0.005312368,0.000098645,0.002101775,0.0003364551,0.0001230281,0.000008683926,0.001117858,0.9584265,0.001081487,0.0001412423,0.03081432,0.0004376867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09293497,0.0006807596,0.8981937,0.003003745,0.001126097,0.001195455,0.0001548934,0.0006855146,0.002024861],"genre_scores_gemma":[0.9499086,0.00002014787,0.04702641,0.0008214195,0.0004901133,0.0001817606,0.0001287196,0.00008879251,0.001334097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8569736,"threshold_uncertainty_score":0.9998808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008595142921064418,"score_gpt":0.2488070317289896,"score_spread":0.2402118888079252,"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."}}