{"id":"W2525649253","doi":"10.1049/iet-com.2016.0080","title":"Extreme learning machine for 60 GHz millimetre wave positioning","year":2016,"lang":"en","type":"article","venue":"IET Communications","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Millimetre wave; Millimeter; Computer science; Extremely high frequency; Telecommunications; Astronomy; Physics; Optics","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":[0.001338023,0.0004904685,0.0008110591,0.0005618615,0.0003038496,0.0007516419,0.0005878113,0.0008819901,0.0008648901],"category_scores_gemma":[0.005175135,0.0002103118,0.0004094408,0.0007224827,0.0005065674,0.0006032366,0.0007861044,0.001279736,0.0002783598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006008441,"about_ca_system_score_gemma":0.0004438042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00124278,"about_ca_topic_score_gemma":0.0008049518,"domain_scores_codex":[0.9994144,0.0002342703,0.00003826186,0.000101914,0.0001548146,0.0000564098],"domain_scores_gemma":[0.9981811,0.001257733,0.0001725594,0.0001217725,0.000233516,0.00003346441],"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":[0.00008262219,0.00004120815,0.001677891,0.00005446368,0.00004273029,0.00005309023,0.00004669477,0.8496362,0.001749918,0.01400208,0.0009692369,0.1316439],"study_design_scores_gemma":[0.000001528852,0.00001048918,0.000152279,0.000002362276,0.000001295481,0.000006285245,0.000002366697,0.9959663,0.0002779923,0.003444413,0.000131671,0.000002924609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02923487,0.0005515768,0.9684894,0.0002923597,0.00003903944,0.00001863118,0.00003623483,0.0003205494,0.001017229],"genre_scores_gemma":[0.821837,0.0005482223,0.1743905,0.0001400145,0.00009311264,0.0001213177,0.0001950509,0.00004145398,0.002633452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001338023,"threshold_uncertainty_score":0.007076204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05770384722302277,"score_gpt":0.2872472422198286,"score_spread":0.2295433949968058,"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."}}