{"id":"W4243519581","doi":"10.32920/ryerson.14645982","title":"Detection Strategies for High Slew Rate, Low SNR Star Tracking","year":2021,"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":"Star (game theory); Star tracker; Slew rate; Thresholding; Computer science; Artificial intelligence; Tracking (education); Stars; Pixel; Computer vision; Algorithm; Image (mathematics); Physics; Astrophysics; 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":[0.0001124618,0.0002201911,0.0002331806,0.00006571856,0.00005793341,0.0002666412,0.0000854074,0.0003092391,0.0001064244],"category_scores_gemma":[0.00001754923,0.0002289385,0.0001144564,0.00008098232,0.000009217651,0.0001888457,0.00003250376,0.0003246179,0.000006820052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001123009,"about_ca_system_score_gemma":0.00003743194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003464337,"about_ca_topic_score_gemma":0.0005113586,"domain_scores_codex":[0.9991576,0.00002235592,0.0002702007,0.0002456272,0.00009542159,0.0002087721],"domain_scores_gemma":[0.999542,0.00004673665,0.00004073547,0.0002000196,0.0001344449,0.00003604701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003233927,0.00001751532,0.000004165581,0.001709297,0.0001549065,0.000008152567,0.0004749733,0.6261052,0.3341281,0.001030507,0.0001882265,0.03614663],"study_design_scores_gemma":[0.0003587021,0.0000393285,0.000749584,0.0002260029,0.00007272274,0.000002684738,0.0006676414,0.1834421,0.8095515,0.003853697,0.0004662446,0.0005697912],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7057384,0.0001640608,0.2896644,0.00002286529,0.002137617,0.0003856842,0.00002059422,0.0005427393,0.001323565],"genre_scores_gemma":[0.9966469,0.00007245534,0.002110277,0.00001631499,0.0006360357,0.0000743717,0.0002651257,0.00005500354,0.000123545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4754235,"threshold_uncertainty_score":0.9335843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399607248526045,"score_gpt":0.2295183073894796,"score_spread":0.2155222349042192,"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."}}