{"id":"W4251013720","doi":"10.32920/ryerson.14640207.v1","title":"Performance Analysis of Several GPS/Galileo Precise Point Positioning Models","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":"Toronto Metropolitan University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Galileo (satellite navigation); Global Positioning System; Precise Point Positioning; Geodesy; Satellite; Computer science; Convergence (economics); GNSS applications; Geography; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003276726,0.00113679,0.001016086,0.001100823,0.0006929167,0.001395829,0.001467617,0.001044093,0.001359242],"category_scores_gemma":[0.007593563,0.0007140573,0.0009385179,0.001889658,0.0006464916,0.001573603,0.001400223,0.00105371,0.0003933609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001699894,"about_ca_system_score_gemma":0.001638998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05731258,"about_ca_topic_score_gemma":0.02298486,"domain_scores_codex":[0.9982356,0.0006057378,0.0001030137,0.0002786398,0.0005662798,0.0002106333],"domain_scores_gemma":[0.9967318,0.001687,0.0002685122,0.0004023484,0.0007761943,0.0001341308],"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":[0.0002263217,0.00003852109,0.005137227,0.00004722922,0.00008405069,0.00003410397,0.0000410162,0.9750871,0.0005945252,0.0008095084,0.0003963574,0.01750404],"study_design_scores_gemma":[0.00002606953,0.00006650826,0.001512387,0.000006524853,0.00002542642,0.00001345521,0.00002819189,0.9970981,0.0006636981,0.0002629627,0.0002835406,0.00001316405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.725314,0.001362906,0.2524205,0.000792928,0.0001952384,0.0001933047,0.001016329,0.003692113,0.01501281],"genre_scores_gemma":[0.9743689,0.0002587165,0.02333563,0.0000725016,0.00001943376,0.00005394138,0.0007478985,0.0001009599,0.001042041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05731258,"threshold_uncertainty_score":0.1139579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01638492198580584,"score_gpt":0.2177727703357167,"score_spread":0.2013878483499109,"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."}}