{"id":"W4236296460","doi":"10.32920/ryerson.14650077.v1","title":"A system level implementation of wavelet based filtering for GNSS signals","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"GNSS applications; Wavelet; Computer science; Global Positioning System; Multipath propagation; SystemC; SIGNAL (programming language); Position (finance); GLONASS; Cluster analysis; Real-time computing; Filter (signal processing); Discrete wavelet transform; Wavelet transform; Embedded system; Artificial intelligence; Computer vision; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005813944,0.0008278918,0.0004772543,0.0007531536,0.0004328464,0.001205758,0.001114163,0.0008254825,0.01326517],"category_scores_gemma":[0.00136682,0.000378791,0.0005087512,0.0005976138,0.0003277036,0.0007099609,0.0005075599,0.001110671,0.007155234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003666417,"about_ca_system_score_gemma":0.000459298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00123912,"about_ca_topic_score_gemma":0.001103461,"domain_scores_codex":[0.9995623,0.00005516559,0.00003086171,0.00006283667,0.000225162,0.00006364325],"domain_scores_gemma":[0.9996012,0.000094458,0.00002032568,0.0001217702,0.0001451795,0.00001703975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006837371,0.0004381087,0.002491123,0.0006344895,0.0001917968,0.0008907974,0.0004214255,0.06070705,0.3025682,0.06697006,0.02290301,0.5411002],"study_design_scores_gemma":[0.0001534729,0.0004431598,0.001445356,0.0001169749,0.00009136228,0.0008764178,0.00005352943,0.6602569,0.2316595,0.01430421,0.09051729,0.00008196827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00486444,0.000039936,0.9791692,0.00004144173,0.00004378997,0.00005203433,0.0001228868,0.0127193,0.002947019],"genre_scores_gemma":[0.1284034,0.0001734166,0.857087,0.0001427038,0.00004563189,0.0001959225,0.0009222134,0.002178051,0.0108517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01326517,"threshold_uncertainty_score":0.04437637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1188241741460449,"score_gpt":0.3195803747398972,"score_spread":0.2007562005938524,"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."}}