{"id":"W4312587974","doi":"10.1109/tim.2022.3218556","title":"Neural Network Calibration of Star Trackers","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Rocket","keywords":"Calibration; Star tracker; Computer science; Artificial intelligence; Star (game theory); Computer vision; Artificial neural network; Parametric statistics; Starlight; Support vector machine; Basis (linear algebra); Radial basis function; Physics; Mathematics; Spacecraft; Stars","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":[0.001101389,0.0004703397,0.0004328466,0.0004644631,0.0002903305,0.0006551291,0.0007817399,0.0006801646,0.001091462],"category_scores_gemma":[0.003710913,0.0002787178,0.0002825578,0.0005630224,0.0003291646,0.00079536,0.0005200504,0.0007604093,0.0006228692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009960913,"about_ca_system_score_gemma":0.0005374951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006471042,"about_ca_topic_score_gemma":0.00442797,"domain_scores_codex":[0.999428,0.0001379496,0.00002458845,0.0001628629,0.0002025953,0.00004415028],"domain_scores_gemma":[0.9991356,0.0001995666,0.0001178397,0.00009298517,0.0004362827,0.00001784963],"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":[0.0001077848,0.00003243353,0.002576261,0.00005345921,0.00003002825,0.00003107356,0.00005072376,0.8361213,0.009082576,0.002365003,0.0009887093,0.1485607],"study_design_scores_gemma":[0.000001852708,0.00001231489,0.0005585265,0.000005197459,0.000002502741,0.000009668357,0.000003976913,0.9960243,0.002489645,0.0005307806,0.0003568893,0.000004389822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1474231,0.0006903407,0.8402452,0.0002474781,0.000117477,0.00004187683,0.0001165974,0.00195109,0.009166673],"genre_scores_gemma":[0.9392995,0.0002081883,0.05732042,0.00005539663,0.00002311483,0.00002646519,0.0001484038,0.0000793931,0.002839198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006471042,"threshold_uncertainty_score":0.01286674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0241206958604985,"score_gpt":0.2168255429895064,"score_spread":0.1927048471290079,"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."}}