{"id":"W2313217262","doi":"10.2514/6.2007-6439","title":"Hybrid Magnetic Attitude Control Gain Selection","year":2007,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation and Control Conference and Exhibit","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Automatic gain control; Selection (genetic algorithm); Attitude control; Control theory (sociology); Control (management); Computer science; Control engineering; Artificial intelligence; Engineering; Telecommunications; Bandwidth (computing)","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":[0.0002312982,0.0004392764,0.0002748063,0.0003219785,0.0002205622,0.000601256,0.0004254912,0.0003302934,0.003349175],"category_scores_gemma":[0.0005596829,0.0001183508,0.0001434057,0.0001139951,0.0002697537,0.0002882743,0.0004039422,0.0002413876,0.0005930117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002002676,"about_ca_system_score_gemma":0.0001281497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005625421,"about_ca_topic_score_gemma":0.0007343157,"domain_scores_codex":[0.9998009,0.0000351505,0.000008630603,0.00003961352,0.00007849284,0.00003722166],"domain_scores_gemma":[0.9997575,0.00005564476,0.00004300616,0.00003100165,0.00009743439,0.00001539027],"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":[0.001479195,0.0003374278,0.002062085,0.0002773496,0.0001064915,0.0002003228,0.0002250018,0.2513418,0.2880783,0.03244403,0.004106318,0.4193417],"study_design_scores_gemma":[0.0002171613,0.0006807757,0.002136212,0.00003535192,0.00003523826,0.000161998,0.00005530623,0.9256136,0.05553478,0.006280542,0.009213039,0.00003597452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1631062,0.0002981286,0.8034002,0.0002122748,0.0001316834,0.0001107171,0.00006612295,0.001173991,0.03150081],"genre_scores_gemma":[0.9838662,0.00003117547,0.01297116,0.00004155079,0.00002853795,0.00004231837,0.00002754765,0.00001378993,0.002977836],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003349175,"threshold_uncertainty_score":0.01120412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006045726643533362,"score_gpt":0.2123052661827776,"score_spread":0.2062595395392443,"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."}}