{"id":"W4385325613","doi":"10.32920/23796129.v1","title":"Autonomous optical navigation using nanosatellite-class instruments: a Mars approach case study","year":2023,"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":"Calibration; Star tracker; Computer science; Mars Exploration Program; BitTorrent tracker; Remote sensing; Orbit (dynamics); Interplanetary spaceflight; Field of view; Aerospace engineering; Artificial intelligence; Spacecraft; Physics; Engineering; Astronomy; Geography; 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":[0.0005615834,0.0003601067,0.000244804,0.0004210082,0.0006628149,0.0006928757,0.0007575697,0.0008116613,0.0005533514],"category_scores_gemma":[0.001171307,0.0001123315,0.000318786,0.0007345648,0.0004069849,0.0006446096,0.0005849364,0.0003912812,0.0002294964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004882616,"about_ca_system_score_gemma":0.0003638541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009125163,"about_ca_topic_score_gemma":0.01708183,"domain_scores_codex":[0.9995391,0.0001243644,0.00001903247,0.0001105919,0.0001484163,0.000058411],"domain_scores_gemma":[0.999332,0.0001782939,0.00008842518,0.0001996207,0.0001489145,0.00005270962],"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":[0.001190582,0.0009714519,0.1221372,0.0004822336,0.0003972511,0.003448303,0.001696019,0.5383619,0.0340367,0.007329521,0.0123917,0.2775571],"study_design_scores_gemma":[0.000171009,0.001150617,0.09351627,0.00005358473,0.0001277519,0.001382759,0.002514625,0.8251738,0.0418366,0.005066353,0.02891839,0.00008818551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678931,0.0002230792,0.02285712,0.0003043242,0.00003390949,0.00008239097,0.0006242687,0.0005507923,0.007431081],"genre_scores_gemma":[0.9791064,0.00006919919,0.01911476,0.0000268808,0.00001358275,0.00002320957,0.0005283134,0.000032055,0.001085632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009125163,"threshold_uncertainty_score":0.01814413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04973823373688039,"score_gpt":0.2816146839236222,"score_spread":0.2318764501867418,"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."}}