{"id":"W2626981433","doi":"10.1109/aero.2017.7943755","title":"Enabling technologies for high slew rate star trackers","year":2017,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Slew rate; Agile software development; Star (game theory); Star tracker; BitTorrent tracker; Spacecraft; Computer science; Satellite; Engineering; Aerospace engineering; Physics; Artificial intelligence; Electrical engineering; Astrophysics; Eye tracking","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":[0.0010065,0.0003972027,0.0003397473,0.000479981,0.0003859786,0.001228953,0.0008118876,0.000793484,0.002990441],"category_scores_gemma":[0.001196183,0.0002820225,0.0002847559,0.0004079986,0.0004244909,0.002575056,0.001360221,0.001157457,0.002376025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003847435,"about_ca_system_score_gemma":0.0005035856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000226524,"about_ca_topic_score_gemma":0.0002846702,"domain_scores_codex":[0.9992751,0.00008391314,0.00003488186,0.00009455334,0.0004478422,0.0000636867],"domain_scores_gemma":[0.9992913,0.0001399927,0.0001137223,0.000118952,0.0002627288,0.00007325053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003193713,0.0002082915,0.003240933,0.0006917227,0.00004222897,0.0007208167,0.001003983,0.009303106,0.5187001,0.1410097,0.008320698,0.3164391],"study_design_scores_gemma":[0.00009635439,0.002167305,0.003316805,0.0002500821,0.00006821423,0.002466231,0.0004932143,0.05665393,0.409973,0.0371265,0.4872352,0.000153164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07281178,0.005085048,0.8640499,0.00174131,0.001129606,0.0002286638,0.0002470201,0.00191379,0.05279277],"genre_scores_gemma":[0.5545933,0.007160786,0.4046608,0.0007311821,0.000524542,0.0003344886,0.0005242179,0.0002411562,0.03122958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002990441,"threshold_uncertainty_score":0.01000404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512351022202808,"score_gpt":0.2365342521570259,"score_spread":0.2214107419349979,"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."}}