{"id":"W2088801414","doi":"10.3182/20140824-6-za-1003.00604","title":"Four Reaction Wheels Management: Algorithms Trade-Off and Tuning Drivers for the PROBA-3 Mission","year":2014,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Magnetic Bearings and Levitation Dynamics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"NGC Aerospace (Canada)","funders":"Canadian Space Agency","keywords":"Reaction wheel; Spacecraft; Torque; Fidelity; Algorithm; Attitude control; Field (mathematics); Angular momentum; Actuator; Constraint (computer-aided design); Control (management); Computer science; Engineering; Control theory (sociology); Control engineering; Aerospace engineering; Mathematics; Artificial intelligence; Mechanical engineering; Electrical engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002386122,0.0001345268,0.0001100325,0.0000655882,0.0001858604,0.0001250766,0.0001072155,0.00006443201,0.000007405448],"category_scores_gemma":[0.00003513338,0.0001111135,0.0000376965,0.00009991598,0.00003321794,0.0002074773,0.00002488702,0.0001113236,0.000002833766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002731067,"about_ca_system_score_gemma":0.000002334406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005441859,"about_ca_topic_score_gemma":0.000001712231,"domain_scores_codex":[0.9993292,0.000002640249,0.00014881,0.0001826146,0.0001315626,0.0002051541],"domain_scores_gemma":[0.9997463,0.00005049572,0.0000467661,0.0000625432,0.00003476176,0.000059122],"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":[0.00001204897,0.00001220765,0.0009112572,0.0006360499,0.00007649204,4.328132e-7,0.001941927,0.0008262685,0.006154086,0.006710613,0.004826218,0.9778924],"study_design_scores_gemma":[0.0003918285,0.00005105944,0.008447868,0.00006634837,0.00005570762,0.000007220274,0.0006640545,0.8444927,0.0001245808,0.001011182,0.1445194,0.000167973],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7641281,0.00124905,0.178088,0.005353814,0.001629994,0.003575015,0.0000167307,0.001800669,0.04415857],"genre_scores_gemma":[0.9752923,0.0004367446,0.02196872,0.00008541093,0.0001776664,0.00007521515,0.00000493044,0.00004830112,0.001910726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9777244,"threshold_uncertainty_score":0.4531077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075716680890251,"score_gpt":0.2050401372792114,"score_spread":0.1942829704703088,"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."}}