{"id":"W3193591751","doi":"10.3390/s21175709","title":"Kinematic Zenith Tropospheric Delay Estimation with GNSS PPP in Mountainous Areas","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada; University of Calgary","funders":"","keywords":"GNSS applications; Precise Point Positioning; Zenith; GLONASS; Ambiguity resolution; Global Positioning System; Remote sensing; Environmental science; Computer science; Meteorology; Geodesy; Troposphere; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002506841,0.0004458659,0.0002591296,0.0007091914,0.0002096919,0.0005155902,0.0003918251,0.0002119809,0.0003054561],"category_scores_gemma":[0.0006547653,0.0001591246,0.0002158753,0.001354905,0.0001377531,0.0004095467,0.0003932757,0.0002383214,0.0001762762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002768298,"about_ca_system_score_gemma":0.0005190769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03377404,"about_ca_topic_score_gemma":0.02812853,"domain_scores_codex":[0.9998343,0.00002761324,0.000006933053,0.0000391578,0.0000591132,0.00003292047],"domain_scores_gemma":[0.9998106,0.00003334858,0.00003793875,0.00003318871,0.00006889596,0.00001603735],"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":[0.000668412,0.0001662145,0.1639709,0.0001937284,0.0001763716,0.0009911499,0.0005495898,0.5328155,0.06184024,0.001356122,0.001615229,0.2356566],"study_design_scores_gemma":[0.00005282487,0.0002444152,0.1658033,0.00002135509,0.00006103826,0.0002353285,0.0003619627,0.8142672,0.01569734,0.0005915282,0.002620743,0.00004295549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648041,0.0001744392,0.03148407,0.00004077499,0.00001194693,0.00002582189,0.0006073605,0.0003665192,0.002484933],"genre_scores_gemma":[0.9806854,0.00009317015,0.01812483,0.000004954233,0.000006356498,0.000008329363,0.0006937007,0.00001806482,0.0003653273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03377404,"threshold_uncertainty_score":0.06715494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005156779793212379,"score_gpt":0.1939856677406841,"score_spread":0.1888288879474717,"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."}}