{"id":"W4220715374","doi":"10.3390/machines10030218","title":"Improved Extreme Learning Machine Based UWB Positioning for Mobile Robots with Signal Interference","year":2022,"lang":"en","type":"article","venue":"Machines","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Interference (communication); Extreme learning machine; Computer science; Mean squared error; SIGNAL (programming language); Genetic algorithm; Ultra-wideband; Artificial intelligence; Positioning system; Compensation (psychology); Algorithm; Mathematics; Telecommunications; Acoustics; Statistics; Machine learning; Artificial neural network; Physics","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":[0.0001060022,0.000159877,0.0001503079,0.0001329102,0.0003472221,0.000045942,0.0001851677,0.00003508319,0.0003381861],"category_scores_gemma":[0.00001709338,0.0001430097,0.00004861967,0.0002107893,0.00002661902,0.00007380478,0.00005882335,0.0002649408,0.000001897973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006589184,"about_ca_system_score_gemma":0.00001422751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003333271,"about_ca_topic_score_gemma":0.00002625066,"domain_scores_codex":[0.9993171,0.00002530772,0.0001555931,0.0001761254,0.0001059822,0.0002198638],"domain_scores_gemma":[0.9997062,0.00006500156,0.00003834723,0.000126541,0.00003733781,0.0000265345],"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":[0.00006551699,0.00002008358,0.002569917,0.00005673441,0.00002209721,0.000003351882,0.000138792,0.9673699,0.01379746,0.0001599022,0.00009886163,0.0156974],"study_design_scores_gemma":[0.0005824325,0.000397471,0.0001257036,0.00001505322,0.00001544639,0.000008903531,0.00015561,0.9855022,0.01169153,0.0001048784,0.001188703,0.0002120873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06801436,0.0004060123,0.928766,0.0000705136,0.0001758836,0.0004282692,0.00005071707,0.001486053,0.0006022544],"genre_scores_gemma":[0.9935256,0.000002529544,0.005482591,0.000058184,0.00003145891,0.0004933835,0.0001392663,0.00004642341,0.0002205572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9255112,"threshold_uncertainty_score":0.5831766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00924588718433074,"score_gpt":0.2024613005218944,"score_spread":0.1932154133375636,"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."}}