{"id":"W3092034864","doi":"10.1109/pimrc48278.2020.9217356","title":"Fingerprinting Localization Method Based on Clustering and Gaussian Process Regression in Distributed Massive MIMO Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"MIMO; Kriging; Cluster analysis; Computer science; RSS; Ground-penetrating radar; Computational complexity theory; Gaussian process; Mean squared error; Algorithm; Telecommunications link; Gaussian; Data mining; Artificial intelligence; Pattern recognition (psychology); Statistics; Machine learning; Mathematics; Radar","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":[0.0001087289,0.0001531613,0.0001874809,0.0001186705,0.00005484795,0.00006163015,0.00009164053,0.0001318451,0.00001154491],"category_scores_gemma":[0.0001667412,0.0001296025,0.00001726333,0.0004449909,0.00001623827,0.00009541328,0.0000304834,0.0001503703,0.000003212332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005218717,"about_ca_system_score_gemma":0.000009310657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001324762,"about_ca_topic_score_gemma":0.000007171855,"domain_scores_codex":[0.9991893,0.00003497733,0.0002480738,0.0002142126,0.000127386,0.0001860449],"domain_scores_gemma":[0.9997135,0.00005601141,0.0000415881,0.000104866,0.00003111672,0.00005293391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001268913,0.000005176816,0.002473898,0.0003982672,0.000003599947,0.000008059983,0.0002318486,0.9940816,0.0003124504,0.0003324402,0.00006659612,0.002073398],"study_design_scores_gemma":[0.0003392016,0.0000261805,0.0002923778,0.0002812135,0.000004242173,9.901293e-7,0.0008435895,0.9846096,0.01325299,0.0000306431,0.0001601133,0.000158866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006985693,0.00005504397,0.9904884,0.0003560582,0.00008214953,0.000223323,0.000006676098,0.0007767525,0.001025952],"genre_scores_gemma":[0.9974644,0.000009946416,0.002303027,0.0001164792,0.0000236634,0.00002526588,0.00002731608,0.000025084,0.000004883098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9904786,"threshold_uncertainty_score":0.5285037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327861393589627,"score_gpt":0.251295320555648,"score_spread":0.2380167066197517,"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."}}