{"id":"W4360594836","doi":"10.1109/tgrs.2023.3260243","title":"Development and Validation of Comprehensive Closed Formulas for Atmospheric Delay and Altimetry Correction in Ground-Based GNSS-R","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"GNSS positioning and interference","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Mitacs; Natural Environment Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Sight Research UK","keywords":"GNSS applications; Altimeter; Remote sensing; Interferometry; Geodesy; Atmospheric refraction; Reflectometry; Atmospheric model; Geology; Refraction; Atmospheric correction; Satellite; Computer science; Global Positioning System; Physics; Optics; Meteorology; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009950605,0.00009005208,0.0001101217,0.0000902387,0.0001617493,0.00003068935,0.00001928881,0.00005043025,4.09888e-7],"category_scores_gemma":[0.000003373139,0.00009056745,0.00001559429,0.0003296616,0.00006503348,0.0001133332,8.168581e-7,0.00007969574,7.215907e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000332849,"about_ca_system_score_gemma":0.00001754519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001211907,"about_ca_topic_score_gemma":0.00006445645,"domain_scores_codex":[0.9994485,0.00001199934,0.0001592462,0.000160325,0.00007924549,0.0001406358],"domain_scores_gemma":[0.9997409,0.0001056326,0.00002507364,0.00005328349,0.00003940108,0.00003573638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004006015,0.00001537731,0.0000119067,0.0001449838,0.00001284089,0.00000179281,0.001498265,0.07156952,0.04646714,0.000003071114,0.000009338342,0.8802257],"study_design_scores_gemma":[0.0002828105,0.0000626652,0.002291764,0.0001750666,0.000008259101,0.00001414856,0.000348629,0.8870907,0.1095583,0.0000262961,0.00004715719,0.00009422481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5515304,0.0000134335,0.448094,0.00001065779,0.000216859,0.00007662382,0.000001341465,0.00004067703,0.00001612818],"genre_scores_gemma":[0.9528486,0.00004970676,0.0470319,0.00001614264,0.000005069401,5.093576e-7,0.000002544595,0.000008299676,0.00003722366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8801315,"threshold_uncertainty_score":0.3693233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01998650573825114,"score_gpt":0.2424290932590786,"score_spread":0.2224425875208274,"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."}}