{"id":"W3197164486","doi":"10.1002/9781119675525.ch6","title":"Machine Learning for Resource Allocation in Mobile Broadband Networks","year":2021,"lang":"en","type":"other","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Wireless network; Radio resource management; Wireless; Resource allocation; Scalability; Computer network; Distributed computing; Wireless broadband; Multi-frequency network; Context (archaeology); Wireless WAN; Scheduling (production processes); Key distribution in wireless sensor networks; Telecommunications; 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":[0.00006966715,0.0001629512,0.0002215516,0.0001356745,0.000013578,0.00001590292,0.00005975985,0.0002412038,0.0005261065],"category_scores_gemma":[0.00001716653,0.0001800088,0.00003290674,0.0001507061,0.00000531478,0.00002123144,0.00001278657,0.000174302,0.000003729304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007120385,"about_ca_system_score_gemma":0.000005803624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004981622,"about_ca_topic_score_gemma":0.000575638,"domain_scores_codex":[0.9994059,0.00002025576,0.0001822153,0.0001848367,0.00004561235,0.0001611686],"domain_scores_gemma":[0.9997249,0.00003762926,0.00004839664,0.0001512891,0.00001311789,0.00002468166],"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":[0.000001610251,0.000004036577,0.0000340768,0.0001693725,0.0000153207,7.599272e-7,0.00002454378,0.970118,0.00002170118,0.00003361394,0.02534524,0.004231742],"study_design_scores_gemma":[0.0001705822,0.000006152906,9.27733e-7,0.0001536375,0.000003616571,8.349757e-7,0.00001890498,0.5228666,0.00001257405,0.000001579517,0.4766485,0.0001160707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[6.991285e-7,0.01206481,0.7306334,0.000002211619,0.000147827,0.0005129037,0.000002418665,0.0003587988,0.2562769],"genre_scores_gemma":[0.002259622,0.001207563,0.01643268,0.0000154156,0.000419212,0.0004531043,0.001390029,0.000862407,0.9769599],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7206831,"threshold_uncertainty_score":0.7340544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006545977345138091,"score_gpt":0.220262644043446,"score_spread":0.2137166666983079,"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."}}