{"id":"W2968611636","doi":"10.3390/rs11161851","title":"Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"GNSS positioning and interference","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Hong Kong Polytechnic University","keywords":"Non-line-of-sight propagation; Computer science; Multipath propagation; Pseudorange; Artificial intelligence; Global Positioning System; Support vector machine; Pattern recognition (psychology); Telecommunications; GNSS applications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001608438,0.0002140957,0.0001965825,0.0001141889,0.000072584,0.00006297946,0.00006875551,0.0001106404,0.00001775158],"category_scores_gemma":[0.00005198738,0.0002172594,0.00005612806,0.00009949272,0.00003214142,0.00006755036,0.00002051237,0.0002944563,0.0002038051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001698838,"about_ca_system_score_gemma":0.00001776117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003207788,"about_ca_topic_score_gemma":0.000006173004,"domain_scores_codex":[0.9989284,0.00004011092,0.0002194119,0.0002649033,0.0002600345,0.0002871514],"domain_scores_gemma":[0.999431,0.00006751409,0.0000388291,0.0002863561,0.00006385544,0.0001124797],"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":[0.0002007176,0.00008519703,0.002973477,0.0005583969,0.0002476423,0.0000445753,0.00141701,0.3557001,0.2529415,0.0001453549,0.002156995,0.383529],"study_design_scores_gemma":[0.0003632291,0.00006677076,0.001406182,0.0007111263,0.00001779887,0.00001508449,0.00005577246,0.9651999,0.03147978,0.00004321065,0.0003924844,0.0002485998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8702784,0.00009892597,0.1012581,0.00005424828,0.001165683,0.0002345322,0.000006907838,0.0002817367,0.02662151],"genre_scores_gemma":[0.9941811,0.00001152685,0.005327854,0.0001370076,0.00005344285,3.147677e-8,0.000007990172,0.00004409567,0.0002369625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6094999,"threshold_uncertainty_score":0.885958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04992607480052427,"score_gpt":0.2380082315872729,"score_spread":0.1880821567867486,"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."}}