{"id":"W4407590082","doi":"10.36227/techrxiv.173950936.68133395/v1","title":"Smartphone PPP-RTK positioning with Galileo high accuracy service and atmospheric correction from single reference station","year":2025,"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 Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Galileo (satellite navigation); GNSS applications; Remote sensing; Geodesy; Service (business); Computer science; Precise Point Positioning; Global Positioning System; Environmental science; Telecommunications; Geology; Business","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":[0.0002972505,0.0008532087,0.0006213093,0.0008497823,0.000376211,0.0005070157,0.0009676707,0.0005905477,0.002235675],"category_scores_gemma":[0.001340757,0.0003161369,0.0004803702,0.0008555888,0.0002383351,0.0006673822,0.001104348,0.0005713266,0.001970687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002348387,"about_ca_system_score_gemma":0.0007154741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006700451,"about_ca_topic_score_gemma":0.006076327,"domain_scores_codex":[0.9993663,0.00006232618,0.00003798495,0.0001326326,0.0003433161,0.00005735391],"domain_scores_gemma":[0.9994674,0.00003470645,0.00005077735,0.0001204796,0.0002998166,0.0000268238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004693896,0.00007913112,0.01943123,0.0009045054,0.0001127511,0.0011162,0.0006720599,0.03298518,0.0904045,0.003671481,0.01787532,0.8322781],"study_design_scores_gemma":[0.000320357,0.001191621,0.04617856,0.0001948737,0.0002891291,0.003837493,0.0006320944,0.7663754,0.09167387,0.003141623,0.08586603,0.0002991009],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0897537,0.001160212,0.8860279,0.0003011536,0.0005865569,0.0002307136,0.0008768814,0.00912742,0.01193547],"genre_scores_gemma":[0.7536598,0.0007163489,0.2351202,0.0002019527,0.0001952383,0.0002731336,0.002089377,0.00018731,0.00755654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006700451,"threshold_uncertainty_score":0.01332289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189373519617475,"score_gpt":0.2102762916942544,"score_spread":0.1983825564980796,"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."}}