{"id":"W2130776362","doi":"10.2514/6.2008-7000","title":"Gain Scheduling Control Design for a Pitch-Axis Missile Autopilot","year":2008,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation and Control Conference and Exhibit","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Autopilot; Missile; Gain scheduling; Computer science; Scheduling (production processes); Control theory (sociology); Control (management); Engineering; Aeronautics; Aerospace engineering; Control engineering; Artificial intelligence","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":[0.0005124672,0.000299566,0.0005478064,0.00008161854,0.0003300324,0.0001178628,0.000128559,0.0001752253,0.00001903693],"category_scores_gemma":[0.0001064052,0.0002895232,0.00009310514,0.0001240632,0.0001690445,0.0002621032,0.00001016332,0.0001691901,0.000008763344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000436833,"about_ca_system_score_gemma":0.00009000776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004326172,"about_ca_topic_score_gemma":0.000007697963,"domain_scores_codex":[0.9984121,0.00009494551,0.0005238379,0.0003860888,0.0001730566,0.0004099493],"domain_scores_gemma":[0.9988536,0.0004265202,0.00009062085,0.0002305659,0.0002297726,0.000168914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006614948,0.0005733066,0.0561166,0.003640891,0.00273511,0.000129803,0.01269874,0.09383978,0.4257377,0.1176717,0.008241648,0.2719998],"study_design_scores_gemma":[0.01244175,0.0003578207,0.004670865,0.0003792413,0.0001290204,0.00004362977,0.00055336,0.9632344,0.001871011,0.004953518,0.01069893,0.0006664456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1948978,0.005430769,0.796205,0.0006999932,0.0002599033,0.001314864,0.00004586756,0.0002878633,0.0008579545],"genre_scores_gemma":[0.997393,0.0001755749,0.001141395,0.0004875926,0.0001544121,0.0004305979,0.00001238597,0.00002865034,0.0001763852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8693947,"threshold_uncertainty_score":0.9999557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098560542621262,"score_gpt":0.2340464530776119,"score_spread":0.2030608476513993,"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."}}