{"id":"W4242198529","doi":"10.32920/ryerson.14663670.v1","title":"Image Processing Techniques For Improved Sun Sensor Performance","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adaptive optics and wavefront sensing","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Measure (data warehouse); Image sensor; Kalman filter; Computer vision; Calibration; Computer science; Artificial intelligence; Noise (video); Filter (signal processing); Algorithm; Image (mathematics); Mathematics; Statistics; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012925,0.0003274749,0.0003987176,0.00005135006,0.000181988,0.0003319121,0.0001421639,0.000123883,0.00008320972],"category_scores_gemma":[0.00000492161,0.0003035259,0.0002184044,0.0000472481,0.00005260534,0.0001413075,0.0003523012,0.0003788667,0.000002512103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003839562,"about_ca_system_score_gemma":0.0002122513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006579288,"about_ca_topic_score_gemma":0.000002355618,"domain_scores_codex":[0.9987339,0.00001402446,0.0003039429,0.0005223442,0.00008517112,0.000340666],"domain_scores_gemma":[0.9989225,0.00002703017,0.0002227738,0.0003291824,0.0004334349,0.00006510586],"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":[0.00008097649,0.0003532052,0.00137256,0.001284595,0.0003738496,0.000003922216,0.0006749027,0.00007856427,0.1026598,0.002106808,0.001249369,0.8897614],"study_design_scores_gemma":[0.000706112,0.000138316,0.0004407525,0.001089555,0.0002615242,0.000003166303,0.001812224,0.6710643,0.307626,0.004975787,0.009889767,0.001992553],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07079671,0.00008923114,0.8909194,0.0002605079,0.0002313314,0.001066091,0.00009517823,0.0001611222,0.03638043],"genre_scores_gemma":[0.5521803,0.000006167658,0.4445407,0.00004874014,0.000607559,0.00006474277,0.000278278,0.00005373844,0.002219738],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8877689,"threshold_uncertainty_score":0.9999417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622600368690152,"score_gpt":0.2649544094422247,"score_spread":0.2487284057553232,"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."}}