{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004141511,0.0005147348,0.0006734648,0.0005092674,0.0002606137,0.0005097555,0.0008732175,0.0007700822,0.0006494824],"category_scores_gemma":[0.001289742,0.000220227,0.0004886313,0.0005497477,0.0003459631,0.0006370883,0.0005187495,0.00054954,0.0002883518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002953916,"about_ca_system_score_gemma":0.0003200486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001594347,"about_ca_topic_score_gemma":0.001164613,"domain_scores_codex":[0.9995673,0.00009854865,0.00002711171,0.0001033346,0.0001631491,0.00004044321],"domain_scores_gemma":[0.9996843,0.0001024504,0.00005340757,0.00004611254,0.0001028072,0.00001089728],"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":[0.0001498695,0.00004657251,0.002264483,0.0001553387,0.00007330169,0.0002021347,0.0001383145,0.6138129,0.02914666,0.005027069,0.0009725409,0.3480108],"study_design_scores_gemma":[0.000006370367,0.00004858595,0.0005324238,0.000005989305,0.000009704807,0.00007935376,0.00001157239,0.9926604,0.005045355,0.0009693624,0.0006179883,0.00001286556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01491325,0.0001141445,0.983775,0.000042979,0.00002561181,0.000008006423,0.000009659837,0.0004173804,0.0006940532],"genre_scores_gemma":[0.6947948,0.0001931335,0.3019553,0.0001067174,0.00003421215,0.00008076709,0.0000923566,0.00005213786,0.00269049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001594347,"threshold_uncertainty_score":0.003170192,"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."}}