{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002087899,0.001941783,0.000936013,0.001603851,0.0005538276,0.001621068,0.001896183,0.0008772936,0.002412734],"category_scores_gemma":[0.01243971,0.0005032125,0.0006076455,0.001026129,0.0004281731,0.001995672,0.00121192,0.001246384,0.002417895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005453083,"about_ca_system_score_gemma":0.0008830227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0123346,"about_ca_topic_score_gemma":0.008727409,"domain_scores_codex":[0.9968609,0.0004173977,0.0003212386,0.0005874681,0.001379525,0.0004335057],"domain_scores_gemma":[0.9932394,0.002076039,0.0003740274,0.00133353,0.002627338,0.0003495717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.006294997,0.001243316,0.08564897,0.002158175,0.000602162,0.001780404,0.001408707,0.02948568,0.06490207,0.001446919,0.03257715,0.7724515],"study_design_scores_gemma":[0.0006460224,0.004758094,0.1486776,0.0004740714,0.0007547257,0.002298285,0.001354378,0.614068,0.1771013,0.00120801,0.04809561,0.0005639974],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8398511,0.004990799,0.05077391,0.0008270215,0.0007353991,0.0008888374,0.002501298,0.08798943,0.01144219],"genre_scores_gemma":[0.8972877,0.001389003,0.08701342,0.0003070768,0.0001194953,0.0002628088,0.004767288,0.002603378,0.006249824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0123346,"threshold_uncertainty_score":0.02452558,"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."}}