{"id":"W2524783010","doi":"10.1109/mmar.2016.7575083","title":"SoftPOSIT enhancements for monocular camera spacecraft pose estimation","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Centroid; Initialization; Artificial intelligence; Computer vision; Weighting; Computer science; Pose; Matching (statistics); Spacecraft; Monocular; Image (mathematics); Algorithm; Mathematics; Engineering; Statistics","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":[0.0000391357,0.00007957809,0.00007206837,0.00003746482,0.00003130615,0.00001905295,0.00003969106,0.00004048722,0.00008074897],"category_scores_gemma":[0.0000182007,0.00005831738,0.00003007963,0.0000433209,0.000007050545,0.00009757146,0.000004756857,0.00001382736,0.00006410683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004569033,"about_ca_system_score_gemma":0.000004841811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005633055,"about_ca_topic_score_gemma":0.000004002892,"domain_scores_codex":[0.9995806,0.000004133706,0.0001148514,0.00009224604,0.00007219391,0.0001360099],"domain_scores_gemma":[0.99977,0.00002743498,0.00001297566,0.0001123093,0.00003737063,0.00003985594],"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":[0.00001802787,0.00003712798,0.0003428946,0.0000917233,0.00007763906,0.000001488244,0.0000904418,0.6035346,0.2770399,0.004384737,0.00900179,0.1053796],"study_design_scores_gemma":[0.0003956656,0.00003220052,0.0001073192,0.00002302715,0.00001175395,4.255003e-7,0.000004380696,0.8690099,0.1273867,0.0005144405,0.002397226,0.000116913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01940987,0.00003325458,0.9791253,0.0002052938,0.0001898715,0.0001911552,0.000004640284,0.0001523874,0.0006882438],"genre_scores_gemma":[0.945116,0.00002946472,0.05329444,0.00006732179,0.00005501772,0.00002574187,0.00002119057,0.00002806996,0.00136276],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9258308,"threshold_uncertainty_score":0.2378114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007351138391300468,"score_gpt":0.2108287085505407,"score_spread":0.2034775701592403,"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."}}