{"id":"W4315647993","doi":"10.3390/s23020777","title":"GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung; Killam Trusts","keywords":"GNSS applications; Android (operating system); Computer science; Tracing; Real-time computing; Embedded system; Database; Global Positioning System; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007422922,0.00006866443,0.00007739654,0.00004460098,0.00004603745,0.00001865284,0.00002926045,0.00003484587,0.000005734571],"category_scores_gemma":[0.00001691895,0.00007319851,0.0000091026,0.0001433304,0.00001441012,0.00009522137,0.00001012471,0.00004573052,0.00001876226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002069368,"about_ca_system_score_gemma":0.000003506462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002223758,"about_ca_topic_score_gemma":0.00001599516,"domain_scores_codex":[0.9995588,0.000008504789,0.0001569693,0.0001018999,0.00008524032,0.0000885819],"domain_scores_gemma":[0.999789,0.00001582616,0.00003139834,0.00009077225,0.00005442815,0.0000185626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009746956,0.00001228276,0.008489527,0.0001489811,0.00003079031,5.350191e-7,0.001854627,0.1633486,0.7980968,0.0002444182,0.001003669,0.02675996],"study_design_scores_gemma":[0.0001046373,0.00004677351,0.05366751,0.0000713148,0.000006012948,4.084467e-7,0.0001083102,0.727852,0.2179044,0.00003605093,0.0001277191,0.00007484012],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987276,0.0001625447,0.0004137775,0.00003933471,0.000196542,0.00006531055,0.000008987402,0.0001421213,0.0002437728],"genre_scores_gemma":[0.9984235,0.0002264673,0.0007486233,0.000007941325,0.000107729,0.000008092057,0.0001685687,0.00001114287,0.0002979052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5801924,"threshold_uncertainty_score":0.2984949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04636863362837441,"score_gpt":0.2460127972907427,"score_spread":0.1996441636623683,"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."}}