{"id":"W2039974433","doi":"10.2478/arsa-2013-0010","title":"Analysis of Current Position Determination Accuracy in Natural Resources Canada Precise Point Positioning Service","year":2013,"lang":"en","type":"article","venue":"Artificial Satellites","topic":"GNSS positioning and interference","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Precise Point Positioning; GNSS applications; GLONASS; Global Positioning System; Computer science; Satellite; Position (finance); Real Time Kinematic; Geodesy; Satellite navigation; Real-time computing; Galileo (satellite navigation); Orbit (dynamics); Remote sensing; Service (business); Telecommunications; Geography; Aerospace engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007052852,0.0001892098,0.0002475421,0.0008405726,0.000769102,0.0008196966,0.0006684723,0.0002936641,0.00152205],"category_scores_gemma":[0.004618146,0.0001070272,0.0001579311,0.001769418,0.0003684902,0.0003785467,0.0003236614,0.0002459124,0.0006040665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005440813,"about_ca_system_score_gemma":0.004263135,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8443174,"about_ca_topic_score_gemma":0.8157313,"domain_scores_codex":[0.9983676,0.00006093685,0.00005523266,0.0002067338,0.001161524,0.0001479114],"domain_scores_gemma":[0.9955217,0.0005243133,0.000234258,0.0002128664,0.003398314,0.0001085955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001421484,0.0002040399,0.7066297,0.0002467824,0.0001579163,0.0002937208,0.002027783,0.03387701,0.03666656,0.001246201,0.004616235,0.2126126],"study_design_scores_gemma":[0.00002562122,0.0002062123,0.8914332,0.0000340865,0.00007530754,0.0001466292,0.001524019,0.07582889,0.02448986,0.0001342238,0.006045914,0.00005607093],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902648,0.0002122563,0.002343944,0.0000744885,0.00001492099,0.000027278,0.001369595,0.0001571842,0.005535526],"genre_scores_gemma":[0.9951487,0.00007981044,0.002005373,0.00001260391,0.000002762214,0.000006840746,0.00117043,0.00001090368,0.001562645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1556826,"threshold_uncertainty_score":0.3131989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01166132995001157,"score_gpt":0.2344417486623914,"score_spread":0.2227804187123798,"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."}}