{"id":"W4250774560","doi":"10.32920/ryerson.14645982.v1","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); Slew rate; Star tracker; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000807696,0.0006048688,0.0006323966,0.001058148,0.0005502329,0.001019715,0.001060911,0.000755545,0.001709063],"category_scores_gemma":[0.002905773,0.000372597,0.000409504,0.0006710566,0.0004035027,0.001268656,0.0008302144,0.0007092478,0.001441921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004369777,"about_ca_system_score_gemma":0.0005702664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008767031,"about_ca_topic_score_gemma":0.001696108,"domain_scores_codex":[0.9994647,0.00007246386,0.00003820347,0.0001252155,0.0002486722,0.00005068233],"domain_scores_gemma":[0.9986002,0.0004093541,0.0002035825,0.0001614806,0.0005623517,0.00006309285],"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":[0.0005428498,0.0002606502,0.00523652,0.0003178826,0.00006565311,0.0002838651,0.0004830353,0.03944124,0.2604696,0.007926032,0.001870738,0.683102],"study_design_scores_gemma":[0.00006278534,0.0006722897,0.007682086,0.00004803096,0.00008713864,0.001518714,0.0001873171,0.7091569,0.263513,0.006218983,0.01077456,0.00007815103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03495797,0.0003605525,0.962535,0.00006005596,0.00002976214,0.00004830614,0.00002050332,0.0006279437,0.001359901],"genre_scores_gemma":[0.2523887,0.0004234566,0.7437366,0.00009772949,0.00004479822,0.00005512813,0.000113882,0.0001665931,0.00297316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001709063,"threshold_uncertainty_score":0.005717397,"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."}}