{"id":"W4237833174","doi":"10.32920/ryerson.14662089","title":"An improved precise point positioning model using GPS and Galileo observations","year":2021,"lang":"en","type":"preprint","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada; Government of Ontario","keywords":"Galileo (satellite navigation); Global Positioning System; Precise Point Positioning; GNSS applications; Constellation; Satellite; UTC offset; Computer science; Geodesy; Offset (computer science); Convergence (economics); Remote sensing; Real-time computing; Geography; Telecommunications; Engineering; Aerospace engineering; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0006928906,0.0009748371,0.0009208364,0.0009405433,0.0004277399,0.001613309,0.003077973,0.001313676,0.002653991],"category_scores_gemma":[0.001568322,0.0008267995,0.00132897,0.002408593,0.0006542061,0.002922342,0.001416597,0.001685062,0.001317551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009927072,"about_ca_system_score_gemma":0.001353793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01941224,"about_ca_topic_score_gemma":0.009892761,"domain_scores_codex":[0.9991118,0.000127347,0.00004780641,0.0003525316,0.0002848227,0.0000757255],"domain_scores_gemma":[0.9995527,0.0001028688,0.00008785381,0.00007241704,0.0001649638,0.00001928407],"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":[0.00003701478,0.00002441747,0.001581012,0.00008704593,0.0000409649,0.00009151326,0.00008389739,0.9412047,0.001307656,0.02958629,0.001522291,0.0244332],"study_design_scores_gemma":[0.00001400636,0.0000309717,0.0006651052,0.00001074357,0.0000224642,0.00004451328,0.00001173519,0.9903517,0.0002127163,0.005545071,0.003071475,0.00001942667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01479113,0.0003999018,0.9745374,0.0002896284,0.0001651082,0.00007207474,0.0008596128,0.0005117852,0.008373444],"genre_scores_gemma":[0.7655222,0.003659812,0.1817242,0.0003661605,0.0003895268,0.000561054,0.00490725,0.0003281728,0.04254174],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01941224,"threshold_uncertainty_score":0.03859854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04621511283622197,"score_gpt":0.2614037525555353,"score_spread":0.2151886397193133,"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."}}