{"id":"W2916704947","doi":"10.1109/nana.2018.8648771","title":"Joint User Scheduling for ODFMA-Based Multi-Cell Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scheduling (production processes); Computer science; Telecommunications link; Upper and lower bounds; Benchmark (surveying); Mathematical optimization; Integer programming; Job shop scheduling; Computation; Linear programming; Mathematics; Algorithm; Computer network; Routing (electronic design automation)","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":[0.00007618471,0.0001479046,0.000134365,0.00005022515,0.00007965885,0.00002614326,0.00008033836,0.00009809026,0.00007049102],"category_scores_gemma":[0.00001467675,0.0001488114,0.00005316511,0.00015152,0.00002896814,0.0001151577,0.00001386026,0.00008821589,0.00003119326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004902091,"about_ca_system_score_gemma":0.000008777661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001679825,"about_ca_topic_score_gemma":0.00001508715,"domain_scores_codex":[0.9992539,0.000007090278,0.0001959595,0.0001699772,0.00006147794,0.0003116535],"domain_scores_gemma":[0.9995658,0.00004908983,0.00002845976,0.000197881,0.00009227839,0.00006652838],"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":[0.000007389611,0.0000166246,0.00004150133,0.00002113018,0.000006685132,2.537772e-7,0.00001154693,0.996179,0.0007136118,0.00009777379,0.001343316,0.001561113],"study_design_scores_gemma":[0.0008297462,0.0000305697,0.00002402948,0.00002222605,0.000008115152,2.259058e-7,0.00001085458,0.9812983,0.01413712,0.00001198993,0.003431972,0.0001948803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00310375,0.0001562126,0.9943799,0.00002626075,0.000511504,0.0003072307,0.000001775214,0.0006538572,0.0008595068],"genre_scores_gemma":[0.5086625,0.00001197971,0.4905229,0.0001258819,0.0003289933,0.00003597462,0.00001813481,0.00005600682,0.0002376866],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5055587,"threshold_uncertainty_score":0.6068352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01797150564021036,"score_gpt":0.2281037382219872,"score_spread":0.2101322325817769,"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."}}