{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003462301,0.000489616,0.0003934947,0.0005613878,0.0001774227,0.0003379625,0.000298046,0.0004623164,0.0004558933],"category_scores_gemma":[0.002189405,0.0001502963,0.0002985001,0.0007329884,0.0002897023,0.0004955815,0.0003570904,0.0002425108,0.0001563775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002407514,"about_ca_system_score_gemma":0.000271091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006212289,"about_ca_topic_score_gemma":0.005269256,"domain_scores_codex":[0.99958,0.00006134223,0.00002393557,0.00007297086,0.0002084758,0.00005322273],"domain_scores_gemma":[0.9991657,0.0003123239,0.0001258449,0.0001358465,0.000233683,0.00002655551],"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":[0.0001516776,0.00006253837,0.02489883,0.0002500895,0.00007279874,0.0005950305,0.0001260182,0.9139948,0.02418155,0.001423714,0.0006068491,0.03363606],"study_design_scores_gemma":[0.00001486038,0.0003906088,0.07899489,0.00003652394,0.00004476398,0.0006713122,0.0002242122,0.8990757,0.01805794,0.0007462806,0.001684691,0.00005811848],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401883,0.0004860967,0.0550565,0.00008250231,0.00002746231,0.00003154279,0.0006944936,0.000390405,0.003042708],"genre_scores_gemma":[0.9938233,0.000182342,0.005208446,0.000009219766,0.000005548944,0.000009214105,0.0004212197,0.00001933937,0.0003213201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006212289,"threshold_uncertainty_score":0.01235229,"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."}}