{"id":"W2440588454","doi":"10.1080/00396265.2016.1180798","title":"Tropospheric delay modelling for the EGNOS augmentation system","year":2016,"lang":"en","type":"article","venue":"Survey Review","topic":"GNSS positioning and interference","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Depth sounding; Troposphere; Limiting; Satellite; Remote sensing; Satellite system; Environmental science; Meteorology; Atmosphere (unit); Computer science; Global Positioning System; GNSS applications; Geodesy; Geography; Telecommunications; Aerospace engineering; Cartography; Engineering","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":[0.0001859993,0.0006980684,0.0002562091,0.0004131475,0.0001941878,0.0005490556,0.0005751673,0.0004147605,0.001126096],"category_scores_gemma":[0.0004099468,0.0001548265,0.0004117034,0.0007891181,0.0001083426,0.0005263737,0.0002481262,0.0003605309,0.0006304742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004781227,"about_ca_system_score_gemma":0.0006925186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02098952,"about_ca_topic_score_gemma":0.01377307,"domain_scores_codex":[0.99989,0.00002394619,0.00000791228,0.00002482858,0.00003970408,0.00001368896],"domain_scores_gemma":[0.9998829,0.00003531343,0.00001916148,0.00001330909,0.00004439958,0.000004907988],"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":[0.00006075023,0.00002208098,0.004882473,0.0002494061,0.00006267692,0.0001394847,0.00009220865,0.8756916,0.007067567,0.007446434,0.002805714,0.1014796],"study_design_scores_gemma":[0.000009559541,0.00003623294,0.002488712,0.00004472183,0.00005257472,0.00008377087,0.00004348832,0.9714637,0.00202113,0.001942578,0.02179158,0.0000219607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1285126,0.008437266,0.8365945,0.0005338808,0.0004560603,0.00008099648,0.002888803,0.001175509,0.02132029],"genre_scores_gemma":[0.900306,0.008733035,0.07839582,0.00007975567,0.0001981259,0.00007291288,0.00297355,0.0002255964,0.009015189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02098952,"threshold_uncertainty_score":0.0417347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05220790815344276,"score_gpt":0.258392922625221,"score_spread":0.2061850144717782,"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."}}