{"id":"W2106737845","doi":"10.1109/plans.1996.509131","title":"Mitigating tropospheric propagation delay errors in precise airborne GPS navigation","year":2002,"lang":"en","type":"article","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Radiosonde; Global Positioning System; Troposphere; GNSS applications; Zenith; Computer science; Remote sensing; Differential GPS; Real Time Kinematic; Reliability (semiconductor); Kinematics; Meteorology; Environmental science; Real-time computing; Geography; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005812986,0.00009870882,0.00008533884,0.00003667405,0.0000354222,0.0000324845,0.0000665566,0.00005501018,0.0002775411],"category_scores_gemma":[0.00001775891,0.00009830871,0.0000233327,0.0002238059,0.00001628533,0.0002425739,0.000007310846,0.0001414478,0.0001671975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008738054,"about_ca_system_score_gemma":0.000002595098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003947313,"about_ca_topic_score_gemma":0.00002084708,"domain_scores_codex":[0.9993773,0.00001925414,0.0002147019,0.0001243578,0.00009910692,0.0001653547],"domain_scores_gemma":[0.9997929,0.0000170254,0.00002202294,0.0001025178,0.00002812965,0.00003736431],"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":[0.00001771863,0.0003100596,0.007076053,0.000343943,0.00005979162,0.00003320031,0.0100274,0.7213873,0.1025499,0.004141842,0.01038118,0.1436716],"study_design_scores_gemma":[0.000219422,0.00004586003,0.003171329,0.0001812595,0.000004088747,0.00001163657,0.0001248908,0.9685838,0.02720182,0.0001716632,0.0001199935,0.0001642044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9468331,0.0001256078,0.01409532,0.00007436851,0.0001444676,0.0001373845,0.000001073927,0.0003421479,0.03824647],"genre_scores_gemma":[0.9959197,0.00001267936,0.003484498,0.00001966826,0.00002615236,0.00003159107,0.00001276602,0.00001799303,0.0004749555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2471965,"threshold_uncertainty_score":0.4008913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125837886124767,"score_gpt":0.2059641876250179,"score_spread":0.1947058087637702,"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."}}