{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004228055,0.0001158096,0.0001260148,0.0001102191,0.0008326893,0.0001150735,0.001446443,0.00005008415,0.00003417002],"category_scores_gemma":[0.0002676542,0.00008924547,0.00009306856,0.0002605859,0.00006776313,0.0002880458,0.0005780815,0.0001729075,0.00009406067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004309367,"about_ca_system_score_gemma":0.00003299685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007599916,"about_ca_topic_score_gemma":0.0000416161,"domain_scores_codex":[0.9989696,0.0002092561,0.0002106606,0.0002363539,0.0001275421,0.0002466004],"domain_scores_gemma":[0.9971752,0.0008123645,0.0001145825,0.001702498,0.0001164381,0.00007896472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001225751,0.000227807,0.003157011,0.00001945952,0.00008347759,0.00000284199,0.00197664,0.000478643,0.006460652,0.1273388,0.004413984,0.8558284],"study_design_scores_gemma":[0.001236313,0.0002244612,0.006955483,0.0001746273,0.00003040704,0.00004235815,0.00005416958,0.4877765,0.0006429519,0.01451369,0.4878511,0.0004979633],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002259288,0.0008735301,0.948806,0.03391559,0.0001147835,0.000178253,0.00001159653,0.0004468928,0.01339404],"genre_scores_gemma":[0.8019311,0.0001432715,0.1936202,0.0002594713,0.0000400746,0.00005886165,0.00002939866,0.00001496239,0.003902667],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8553304,"threshold_uncertainty_score":0.6404457,"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."}}