{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001236342,0.0005612462,0.000621132,0.0009952219,0.000588022,0.001070973,0.0008324955,0.0007557886,0.001238692],"category_scores_gemma":[0.006537531,0.0002598148,0.0003604713,0.00108253,0.000308588,0.001061714,0.0006578708,0.0004783764,0.0006327496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005064167,"about_ca_system_score_gemma":0.0007260111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002358288,"about_ca_topic_score_gemma":0.002410567,"domain_scores_codex":[0.9992998,0.00008212045,0.00005545986,0.0001568493,0.0003076237,0.00009814902],"domain_scores_gemma":[0.9973735,0.0008410676,0.0003049658,0.0002884468,0.001076181,0.0001159393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001483332,0.0004630428,0.01881041,0.0003623925,0.0001425966,0.000264671,0.0003645608,0.1098912,0.2665657,0.002052198,0.002828831,0.5967712],"study_design_scores_gemma":[0.00009677748,0.00137479,0.02429618,0.00004237401,0.000130169,0.0007706592,0.0001318072,0.6374606,0.3279336,0.001160039,0.006503969,0.00009907797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4671838,0.00127102,0.5216313,0.0001935491,0.0001214916,0.0001321747,0.0001805904,0.003925419,0.005360745],"genre_scores_gemma":[0.6621563,0.0005315284,0.3349158,0.00007847402,0.00003755212,0.00003877007,0.0003613994,0.000309159,0.001570997],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002358288,"threshold_uncertainty_score":0.00653851,"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."}}