{"id":"W2788428941","doi":"10.1109/tsp.2018.2812748","title":"Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":538,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Telecommunications link; Computer science; Scheduling (production processes); Mathematical optimization; Optimization problem; Convex optimization; Distributed computing; Algorithm; Regular polygon; Mathematics; Computer network","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007350601,0.0002566723,0.0002625579,0.0003164879,0.001660178,0.0003103933,0.0002885858,0.0001837291,0.00004676753],"category_scores_gemma":[0.000007567814,0.0002792325,0.0001090781,0.000443402,0.0001092619,0.0005592228,0.000003097035,0.000546031,0.00006596711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000270339,"about_ca_system_score_gemma":0.00008728895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000434323,"about_ca_topic_score_gemma":0.00002159877,"domain_scores_codex":[0.9981638,0.0000672868,0.0005151497,0.0003260509,0.000426018,0.0005017081],"domain_scores_gemma":[0.998849,0.0002325673,0.0001280807,0.0003080569,0.0003589491,0.0001233283],"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":[0.0001137322,0.0001555689,0.00001504477,0.0008538085,0.0001672114,0.000001623187,0.001420855,0.6869254,0.01997533,0.00006868089,0.0001723709,0.2901304],"study_design_scores_gemma":[0.0004909725,0.0001465712,0.000005895866,0.0009706055,0.00004361217,0.00004500963,0.0003525543,0.9672015,0.0198539,0.0001260772,0.01039068,0.0003726361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02507663,0.0004362385,0.9718286,0.00006065239,0.0007382685,0.0006929002,0.00001121633,0.0007341025,0.000421343],"genre_scores_gemma":[0.9785184,0.000009634648,0.01998988,0.00001952958,0.0005802047,0.0005377744,0.00001190716,0.0001048469,0.0002277764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9534418,"threshold_uncertainty_score":0.999966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03411177826444568,"score_gpt":0.2986854867013103,"score_spread":0.2645737084368646,"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."}}