{"id":"W3124703186","doi":"10.2478/arsa-2020-0012","title":"Assessment of GNSS PPP-Based Zenith Tropospheric Delay","year":2020,"lang":"en","type":"article","venue":"Artificial Satellites","topic":"GNSS positioning and interference","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Galileo (satellite navigation); GNSS applications; Zenith; Global Positioning System; Precise Point Positioning; Geodesy; Troposphere; Environmental science; Satellite; Remote sensing; Meteorology; Constellation; Computer science; Geography; Telecommunications; Physics","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":[0.00004603771,0.0001066094,0.000149654,0.00001828151,0.00003482424,0.00003379352,0.0001103776,0.00004142767,0.0002042412],"category_scores_gemma":[0.00001853772,0.0001042723,0.00005672867,0.000187859,0.0000390107,0.00006726813,0.00001034961,0.0001030672,0.00007844794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002147915,"about_ca_system_score_gemma":0.00002085014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001587622,"about_ca_topic_score_gemma":0.000003883453,"domain_scores_codex":[0.9993315,0.00002204058,0.0002391336,0.0001255982,0.0001228245,0.00015892],"domain_scores_gemma":[0.9996848,0.00005039635,0.00003014723,0.0001098342,0.00004788363,0.00007701337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004291204,0.0001557842,0.009912587,0.0002425061,0.00009912916,0.00001303525,0.001044883,0.234619,0.7107463,0.01234223,0.0007921702,0.02998944],"study_design_scores_gemma":[0.0001343701,0.000266834,0.008857302,0.00004835498,0.0000282236,9.683941e-7,0.0001050365,0.4986359,0.4895329,0.0004910465,0.001658501,0.0002405204],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9292151,0.0002856674,0.05319046,0.0002083196,0.0001807386,0.000105222,0.0000151365,0.0003122182,0.01648718],"genre_scores_gemma":[0.9945233,0.00001477363,0.005221244,0.000118907,0.00007040709,0.000006234759,0.00001521207,0.00001964932,0.00001020895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2640169,"threshold_uncertainty_score":0.42521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02115689147401876,"score_gpt":0.2396618532742678,"score_spread":0.218504961800249,"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."}}