{"id":"W4417132194","doi":"10.1109/wf-iot64238.2025.11270703","title":"Enhancing LOS/NLOS Classification in UWB with Robust Feature Engineering","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mahalanobis distance; Feature extraction; Pattern recognition (psychology); Principal component analysis; Robustness (evolution); Dimensionality reduction; Outlier; Feature (linguistics); Curse of dimensionality","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":[0.001218289,0.001166343,0.001108439,0.001713003,0.0004436596,0.0009835478,0.0008371115,0.0008721132,0.0006333846],"category_scores_gemma":[0.00428731,0.0001904676,0.0008198028,0.001324247,0.000493656,0.001284338,0.0009336588,0.001136201,0.0008928265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002811814,"about_ca_system_score_gemma":0.0004328833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0013465,"about_ca_topic_score_gemma":0.00109934,"domain_scores_codex":[0.9989222,0.0001861795,0.00007617988,0.0002788304,0.0004109724,0.000125677],"domain_scores_gemma":[0.9982623,0.0007230549,0.0002439482,0.0002512748,0.0004736154,0.00004580417],"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":[0.000432453,0.0003767889,0.007614593,0.0001996469,0.0001068021,0.0003290437,0.0002183234,0.08572196,0.06576697,0.001984956,0.003323867,0.8339247],"study_design_scores_gemma":[0.00002425074,0.0003655918,0.008526201,0.00003412865,0.0000810811,0.0004761629,0.0001704146,0.9339893,0.04692631,0.004362483,0.004976045,0.00006803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0869468,0.0005216639,0.9093167,0.0001806833,0.00009492746,0.00006598022,0.000192134,0.001704603,0.0009766498],"genre_scores_gemma":[0.7016557,0.0004766298,0.2942336,0.0001841472,0.0001416564,0.0001492808,0.001254083,0.0001688519,0.001736001],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001713003,"threshold_uncertainty_score":0.006443024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007426909686252835,"score_gpt":0.2030694935333069,"score_spread":0.195642583847054,"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."}}