{"id":"W2123272317","doi":"10.1109/isssta.2008.16","title":"New L5/E5a Acquisition Algorithms: Analysis and Comparison","year":2008,"lang":"en","type":"article","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Robustness (evolution); GNSS applications; Ranging; Algorithm; Satellite navigation; Monte Carlo method; False alarm; Real-time computing; Data mining; Global Positioning System; Artificial intelligence; Telecommunications; Mathematics","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":[0.00001819823,0.00006444807,0.0001149505,0.00009192222,0.00004672362,0.00002015939,0.00003655306,0.00003261024,0.0002489249],"category_scores_gemma":[0.000001505678,0.00006174704,0.00003286491,0.0002000739,0.0000121295,0.00008141416,0.000008118261,0.00005390748,0.00005052882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001440105,"about_ca_system_score_gemma":0.000003516904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001655095,"about_ca_topic_score_gemma":0.00001875487,"domain_scores_codex":[0.9996572,0.000004951401,0.0001003554,0.00008553178,0.00006123422,0.00009076022],"domain_scores_gemma":[0.999824,0.00001014561,0.000008261252,0.00008200745,0.00001447236,0.00006114938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003882905,0.0002070899,0.3349199,0.0001180975,0.003596858,0.00004050584,0.01320949,0.1644242,0.01662604,0.003640147,0.3030521,0.1601267],"study_design_scores_gemma":[0.0003152421,0.00007551305,0.2748405,0.00001952751,0.0002151411,0.00003212107,0.0001464008,0.7020312,0.02048036,0.0001457593,0.001395008,0.0003032349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3475798,0.0002579254,0.6325475,0.00003203109,0.00006584435,0.00002316965,0.000001853274,0.0002574855,0.01923443],"genre_scores_gemma":[0.9849014,0.00004787136,0.01414281,0.00002827889,0.00004161895,0.000001448073,0.0000137035,0.000005456134,0.0008173862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6373217,"threshold_uncertainty_score":0.2725553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470047002319944,"score_gpt":0.2312796573808105,"score_spread":0.216579187357611,"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."}}