{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00172961,0.0005297645,0.0007639745,0.0005538136,0.0004036699,0.0009516335,0.00106754,0.000740873,0.00181586],"category_scores_gemma":[0.006493879,0.0003041647,0.0003758904,0.0006683382,0.0002241668,0.00125011,0.0009209044,0.000505431,0.001889836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007385602,"about_ca_system_score_gemma":0.0006900523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004957875,"about_ca_topic_score_gemma":0.0038313,"domain_scores_codex":[0.9992328,0.0001135185,0.00002894834,0.0002428243,0.0003117132,0.00007026518],"domain_scores_gemma":[0.9982738,0.0005331209,0.0001858801,0.000437549,0.0005029091,0.00006671744],"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":[0.001029787,0.0001512239,0.03112383,0.0001895635,0.0001235688,0.0001246562,0.0003720365,0.2809131,0.1069203,0.002135654,0.003845077,0.5730711],"study_design_scores_gemma":[0.00002566796,0.0001140754,0.01206507,0.00001322206,0.00002589961,0.0001259704,0.00003442633,0.9100908,0.07398062,0.0006643768,0.002822558,0.00003733893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1869386,0.0004689658,0.8008682,0.0001821084,0.00008445455,0.0000327471,0.0002600054,0.008437353,0.002727605],"genre_scores_gemma":[0.8058257,0.0001641892,0.1905969,0.00004883944,0.00002734547,0.00002274401,0.0006064549,0.0006894277,0.002018474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004957875,"threshold_uncertainty_score":0.009858072,"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."}}