{"id":"W3107391282","doi":"10.1080/10095020.2020.1842811","title":"Impact of different sampling rates on precise point positioning performance using online processing service","year":2020,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"GNSS positioning and interference","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"GNSS applications; Geodetic datum; Geodesy; Precise Point Positioning; Sampling (signal processing); Kinematics; Data processing; Computer science; Context (archaeology); Position (finance); Azimuth; Global Positioning System; Remote sensing; Mathematics; Geography; Computer vision; Telecommunications; Database; Geometry; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001419118,0.0001651743,0.0001684178,0.0001925009,0.0002659568,0.000218798,0.0002758066,0.00004170371,0.00002634864],"category_scores_gemma":[0.00007574161,0.0001414351,0.00004545221,0.0007153806,0.00007196499,0.002835223,0.00006016831,0.0001831275,0.00002289618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001642138,"about_ca_system_score_gemma":0.0001100854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001476061,"about_ca_topic_score_gemma":0.000004484116,"domain_scores_codex":[0.9987568,0.000009732174,0.0004414769,0.0001296264,0.0003887916,0.0002735214],"domain_scores_gemma":[0.999171,0.00002519539,0.000155842,0.0001319262,0.0003845339,0.0001315175],"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":[0.00004264071,0.00002532163,0.001774419,0.0001962898,0.000007810245,7.600124e-8,0.005827071,0.9195561,0.04921409,0.00002494096,0.000007692056,0.02332354],"study_design_scores_gemma":[0.0001694791,0.0001893519,0.05043814,0.000336389,0.000006330475,0.000004241133,0.000164845,0.9120983,0.03643233,0.00000864477,0.000003958041,0.0001479239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9000943,0.00000963482,0.09876552,0.00005495343,0.0001024303,0.0001230879,0.00002707297,0.0001260103,0.0006970438],"genre_scores_gemma":[0.9980447,0.000008312887,0.001681961,0.0001623148,0.00005003968,0.000004294406,0.00003973978,0.000008126169,5.219684e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09795044,"threshold_uncertainty_score":0.5767557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04241626968098609,"score_gpt":0.3011956583101357,"score_spread":0.2587793886291496,"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."}}