{"id":"W1904764308","doi":"10.1109/asspcc.2000.882500","title":"Semiblind multiuser detection based on subspace tracking","year":2002,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multiuser detection; Detector; Subspace topology; Computer science; Interference (communication); Single antenna interference cancellation; Code division multiple access; Base station; Tracking (education); Convergence (economics); Algorithm; Electronic engineering; Telecommunications; Artificial intelligence; Engineering","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":[0.0003154453,0.00008996925,0.00007705289,0.0002147544,0.0001784728,0.0002252463,0.001011768,0.00006355883,0.0002103997],"category_scores_gemma":[0.0000655227,0.00008109536,0.00004317665,0.0008451363,0.00002646472,0.0003593848,0.0001361964,0.0002718142,0.0003769144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000599858,"about_ca_system_score_gemma":0.000009820219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002072792,"about_ca_topic_score_gemma":0.00007601354,"domain_scores_codex":[0.9988068,0.0001620203,0.0001292404,0.0002645612,0.0003849992,0.0002523684],"domain_scores_gemma":[0.9982855,0.0003581539,0.00003899505,0.001128933,0.000100636,0.0000877269],"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":[0.00001295338,0.0003887995,0.001059728,0.00001074612,0.00001075091,0.00001009457,0.0005037914,0.06081083,0.004441395,0.004145918,0.004563015,0.924042],"study_design_scores_gemma":[0.0002754418,0.00004350649,0.001563861,0.00001117721,5.027542e-7,0.000001581247,0.00000658628,0.9816598,0.01233592,0.00003039903,0.00397299,0.00009819515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01069906,0.00006040663,0.9641843,0.003669462,0.0001112137,0.0001655642,1.688608e-7,0.0003723063,0.02073752],"genre_scores_gemma":[0.9807499,0.00002497176,0.01737248,0.0004284591,0.00003730113,0.00001813708,4.160685e-7,0.000009521,0.001358793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9700509,"threshold_uncertainty_score":0.4844598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06504657275621904,"score_gpt":0.2837719194929433,"score_spread":0.2187253467367242,"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."}}