{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001370682,0.00007476194,0.000074257,0.00006013933,0.0001714027,0.000009666692,0.00002778387,0.00001754967,0.0001384256],"category_scores_gemma":[4.579965e-7,0.00008396025,0.00003303528,0.0001444895,0.00001419091,0.0001002965,3.391083e-7,0.0001047528,6.600347e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001222317,"about_ca_system_score_gemma":0.000009954283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000301258,"about_ca_topic_score_gemma":0.00003012304,"domain_scores_codex":[0.9992529,0.00004322975,0.0001914594,0.00008305207,0.0003327582,0.00009663546],"domain_scores_gemma":[0.9998447,0.00000701911,0.00002940255,0.00005788483,0.00002622634,0.00003473181],"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":[0.00006603468,0.00004219594,0.00002797032,0.00002287019,0.00003394595,3.250664e-7,0.0004893104,0.9298136,0.03132803,0.00005228971,0.0001672438,0.03795623],"study_design_scores_gemma":[0.002121546,0.0005685235,0.001306604,0.00002877946,0.0001042951,0.00001031512,0.001452828,0.6645548,0.328171,0.0001367251,0.001210767,0.0003337647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8825316,0.00005931078,0.1154052,0.0001024127,0.001213585,0.0003039305,0.00003154068,0.000107327,0.0002450695],"genre_scores_gemma":[0.9995897,0.00002569359,0.0002112274,0.00006671857,0.00002180599,0.00005178111,0.000009068468,0.00001125636,0.00001271257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.296843,"threshold_uncertainty_score":0.34238,"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."}}