{"id":"W4319027861","doi":"10.3390/s23031631","title":"A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Pseudorange; Computer science; Dilution of precision; Android (operating system); Real-time computing; Multipath propagation; Range (aeronautics); Remote sensing; Global Positioning System; Engineering; Telecommunications; Geography","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":[0.00004028197,0.00007857423,0.0001989287,0.0003715106,0.00001262403,0.000004199089,0.00006001018,0.0000386179,0.00003670075],"category_scores_gemma":[0.00001222632,0.0000840632,0.00006021819,0.00081916,0.00003161226,0.00001939534,0.00001382596,0.00007313871,0.00009764705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003391258,"about_ca_system_score_gemma":0.000001706317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001949992,"about_ca_topic_score_gemma":0.0000643715,"domain_scores_codex":[0.9994547,0.00002494403,0.0001706001,0.0001046141,0.00009881672,0.0001463075],"domain_scores_gemma":[0.9997394,0.00005419899,0.00001993038,0.0001497469,0.000007215982,0.00002951389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000008685385,0.00001635864,0.009651322,0.00003560186,0.0002716736,0.00001702984,0.001540345,0.978884,0.008890787,0.00003642426,0.0003594324,0.0002882819],"study_design_scores_gemma":[0.0001898063,0.00001731184,0.5649599,0.00003572707,0.0001117087,7.339505e-7,0.0003647123,0.4320707,0.001868752,0.00002932964,0.000242169,0.000109187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978388,0.00003371925,0.00008066271,0.00001464865,0.00008352836,0.00004195972,0.00003673433,0.00007845333,0.001791489],"genre_scores_gemma":[0.9995539,0.00006231578,0.00004730833,0.00000853526,0.000006601525,0.000004905596,0.00005572538,0.00001132854,0.0002493651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5553086,"threshold_uncertainty_score":0.3427998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02054062809756173,"score_gpt":0.2381851805010246,"score_spread":0.2176445524034628,"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."}}