{"id":"W2896435355","doi":"10.1002/cpe.5021","title":"Real‐time multiuser scheduling based on end‐user requirement using big data analytics","year":2018,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"National Natural Science Foundation of China","keywords":"Computer science; Maximum throughput scheduling; Scheduling (production processes); Quality of service; Computer network; Wireless network; Wireless; Throughput; Fading; Distributed computing; Channel (broadcasting); Round-robin scheduling; Fair-share scheduling; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002501874,0.0005920821,0.0007539357,0.0006601262,0.0004824803,0.0008222022,0.0006857305,0.0003624689,0.0007283127],"category_scores_gemma":[0.005173652,0.0002676356,0.0002569146,0.0006236864,0.0003378274,0.0009286418,0.0004863335,0.0006286818,0.0001082592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007287216,"about_ca_system_score_gemma":0.000859339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003185803,"about_ca_topic_score_gemma":0.00310235,"domain_scores_codex":[0.9992398,0.0003203699,0.00005244723,0.00009713433,0.000204312,0.00008603148],"domain_scores_gemma":[0.9935018,0.00439367,0.0003747695,0.0004286239,0.0009742442,0.0003268445],"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":[0.0007108895,0.0002612933,0.006507867,0.00007830064,0.00005579614,0.0001982109,0.0000929515,0.9307287,0.01121598,0.003431916,0.001373332,0.04534479],"study_design_scores_gemma":[0.000002401555,0.00001455845,0.000301648,7.288083e-7,0.000002022324,0.000005762229,0.000007262097,0.9981725,0.0009305187,0.0005148515,0.00004494814,0.000002763878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4809318,0.000235453,0.5130355,0.0005852011,0.0000894921,0.00012696,0.0002234546,0.001278654,0.00349358],"genre_scores_gemma":[0.9813412,0.00003178782,0.01827457,0.00002161443,0.00001007077,0.00002088352,0.00008880241,0.00002033183,0.0001906358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003185803,"threshold_uncertainty_score":0.01323134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08366151756304095,"score_gpt":0.3472004218482463,"score_spread":0.2635389042852053,"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."}}