{"id":"W2027902893","doi":"10.1109/pimrc.2010.5671951","title":"Cognitive uplink interference management in 4G cellular femtocells","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Femtocell; Macrocell; Telecommunications link; Computer network; Computer science; Interference (communication); Base station; Channel (broadcasting); Cognitive radio; Scheduling (production processes); Telecommunications; Wireless; 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":[0.00006151949,0.00009946545,0.00009039615,0.0000954267,0.00001139818,0.00001614708,0.00008543501,0.00005404726,0.000206613],"category_scores_gemma":[0.000006934209,0.0001019973,0.00001516713,0.0001350494,0.00001290032,0.0001130134,0.00003040582,0.0001637816,0.0002131505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002276501,"about_ca_system_score_gemma":0.000002048954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000665561,"about_ca_topic_score_gemma":0.00005316878,"domain_scores_codex":[0.9995026,0.00000513983,0.0001632995,0.0001337097,0.00004298289,0.0001522943],"domain_scores_gemma":[0.9997826,0.00002132953,0.00001419708,0.0001256912,0.00002224675,0.0000339286],"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":[0.00006445838,0.0002732249,0.00566102,0.001022666,0.0002494682,0.0002634368,0.002881254,0.5197456,0.2733636,0.0299348,0.00155494,0.1649855],"study_design_scores_gemma":[0.001426052,0.00003490593,0.0007077867,0.0001928613,0.0000228633,0.000005316344,0.000906881,0.8181078,0.1759663,0.001008989,0.0009492188,0.0006709617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04299489,0.00002721975,0.8600116,0.000007089962,0.0004013451,0.000314714,0.00000113125,0.0002137779,0.09602818],"genre_scores_gemma":[0.9833747,0.00001878032,0.01522386,0.00001513007,0.00002811373,0.00004079037,0.000008468138,0.00002210927,0.001268064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9403798,"threshold_uncertainty_score":0.415933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008920536410175975,"score_gpt":0.2217266315358623,"score_spread":0.2128060951256863,"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."}}