{"id":"W4382049198","doi":"10.32920/23582316.v1","title":"Improving Performance Of Star Trackers: Brightness Prediction And Star Centroid Accuracy","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Centroid; Brightness; Star tracker; Star (game theory); Calibration; Computer science; Pixel; Artificial intelligence; Set (abstract data type); BitTorrent tracker; Physics; Computer vision; Algorithm; Astrophysics; Mathematics; Statistics; Eye tracking; Optics; Astronomy","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":[0.0001254766,0.0001877934,0.0002258684,0.00009994434,0.00004056993,0.0000346576,0.00009559902,0.0001985624,0.00002698374],"category_scores_gemma":[0.00002481675,0.0001802095,0.00005004341,0.00008692606,0.00002586921,0.0001914727,0.00008563549,0.000329432,0.000008092133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007277768,"about_ca_system_score_gemma":0.00001909745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003886272,"about_ca_topic_score_gemma":0.00003052376,"domain_scores_codex":[0.9990437,0.0000135379,0.0003485954,0.0002186825,0.0001799383,0.0001955191],"domain_scores_gemma":[0.9995487,0.00004208712,0.00008021446,0.0002086159,0.00007037585,0.00004995514],"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":[0.0003988721,0.0001348028,0.01290178,0.02279904,0.0006543942,0.00001916109,0.005158268,0.4519039,0.4080267,0.0003411164,0.002233757,0.09542819],"study_design_scores_gemma":[0.000303479,0.00004419824,0.02443752,0.0002367707,0.00005301212,0.000002113522,0.00008918509,0.8909388,0.08342238,0.00006505865,0.0001677469,0.0002397031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996046,0.0001716956,0.001736067,0.00001777756,0.0009003633,0.0003144978,0.0001133844,0.0005077578,0.0001924392],"genre_scores_gemma":[0.997968,0.000718651,0.0007282371,0.000003282961,0.0002151047,0.00001437741,0.0001854446,0.00004474997,0.0001221499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4390349,"threshold_uncertainty_score":0.734873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356866923219473,"score_gpt":0.2151282018582925,"score_spread":0.2015595326260978,"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."}}