{"id":"W1482216715","doi":"10.1109/aero.2015.7119226","title":"Improving star tracker centroiding performance in dynamic imaging conditions","year":2015,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Star tracker; Star (game theory); Artificial intelligence; Computer science; Slew rate; Thresholding; Algorithm; Computer vision; Pixel; Physics; Image (mathematics); 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":[0.00008034973,0.00006652777,0.00006126075,0.00006844876,0.00002639626,0.00001953012,0.00003799696,0.0000205573,0.00002536141],"category_scores_gemma":[0.00001028941,0.00006751146,0.00001299307,0.0001131086,0.000008544153,0.0002973358,0.000006573571,0.00009559028,0.00004458293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001523129,"about_ca_system_score_gemma":0.00000806348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006523528,"about_ca_topic_score_gemma":0.00005318637,"domain_scores_codex":[0.9995476,0.000006567177,0.0001248448,0.00007037663,0.00007299695,0.000177615],"domain_scores_gemma":[0.9998515,0.000008503646,0.000009642011,0.00006292529,0.00002188437,0.00004553308],"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":[0.00003329169,0.00007091921,0.06154481,0.0002076311,0.00002374171,0.00004047159,0.003253981,0.4640707,0.4133517,0.00118846,0.001054309,0.05516001],"study_design_scores_gemma":[0.000310738,0.00000540421,0.01210936,0.00001500362,0.000002977987,0.00000471581,0.0002450516,0.9782146,0.008816041,0.0000334996,0.0001370002,0.0001055963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993322,0.00007336838,0.001758118,0.00003272469,0.0002102617,0.00007098191,0.000002782317,0.0001914083,0.004338397],"genre_scores_gemma":[0.9993078,0.000007685589,0.000526296,0.00001973987,0.00003191817,0.000004510772,0.00002280879,0.0000145721,0.00006467484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5141439,"threshold_uncertainty_score":0.2753038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006729452420516413,"score_gpt":0.2099226750743275,"score_spread":0.2031932226538111,"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."}}