{"id":"W2604599742","doi":"10.1017/s0373463317000121","title":"Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers","year":2017,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; Computer science; GNSS applications; Artificial intelligence; Pattern recognition (psychology); Multipath mitigation; Support vector machine; Context (archaeology); Real-time computing; Computer vision; Channel (broadcasting); Global Positioning System; Telecommunications; Geography","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":[0.0001796153,0.0003654461,0.0003449097,0.0003954247,0.0001486321,0.0002632807,0.0004167918,0.0004064187,0.0003216272],"category_scores_gemma":[0.0006804509,0.000143502,0.0002233026,0.0003295317,0.0001399936,0.0004166885,0.0002758111,0.0003411529,0.0002721928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000183956,"about_ca_system_score_gemma":0.0002603212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001155579,"about_ca_topic_score_gemma":0.002085985,"domain_scores_codex":[0.9998142,0.00003033251,0.000008887218,0.00004578181,0.00007427012,0.00002649471],"domain_scores_gemma":[0.9998043,0.00004210069,0.00003828836,0.00003343053,0.00007245628,0.000009499156],"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":[0.0003883847,0.0001149697,0.005293975,0.0001113607,0.00006655061,0.0002344981,0.00008422096,0.143915,0.1841653,0.001677164,0.001208148,0.6627403],"study_design_scores_gemma":[0.00001259885,0.000181557,0.004990488,0.00001052104,0.00003288625,0.0002648268,0.00001903677,0.9470812,0.04550403,0.0006005323,0.001280042,0.0000221778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1414298,0.0005701748,0.8549159,0.00008099245,0.00009782718,0.00004358881,0.0000492815,0.00138018,0.001432189],"genre_scores_gemma":[0.8667676,0.00028363,0.1317421,0.00004843326,0.00003982948,0.00003684924,0.00008386109,0.00002431363,0.0009733922],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001155579,"threshold_uncertainty_score":0.00229764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04319673025275576,"score_gpt":0.2609022523661571,"score_spread":0.2177055221134014,"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."}}