{"id":"W3119037122","doi":"10.1109/tnsm.2021.3049718","title":"Delay-Sensitive Multi-Source Multicast Resource Optimization in NFV-Enabled Networks: A Column Generation Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Multicast; Computer network; Unicast; Distributed computing; Bandwidth (computing)","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":[0.0007475979,0.0007493754,0.0006147185,0.0005207473,0.0004607445,0.001074703,0.001398462,0.0009379922,0.002043871],"category_scores_gemma":[0.001227626,0.0003215067,0.0005570711,0.0006027533,0.000523746,0.0008818686,0.0008639946,0.000945683,0.0001876331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014261,"about_ca_system_score_gemma":0.0008369062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004330433,"about_ca_topic_score_gemma":0.00389269,"domain_scores_codex":[0.9996533,0.0001411027,0.000009053754,0.00004485023,0.00008179101,0.000069899],"domain_scores_gemma":[0.9995621,0.0002495717,0.0000494928,0.00002372818,0.00008118743,0.00003398147],"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":[0.00003011793,0.00004849863,0.0002782887,0.00006813966,0.0000254331,0.00009693525,0.00004781886,0.9459445,0.001520537,0.02429159,0.001585235,0.02606289],"study_design_scores_gemma":[0.000002311588,0.00001163963,0.00002511581,0.000003476849,0.000002686711,0.00001229665,0.00001113741,0.9953279,0.0001754866,0.003941092,0.0004843324,0.000002477738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01703009,0.0005219324,0.974502,0.0004305764,0.00007102278,0.00005825118,0.00006152463,0.000110039,0.007214489],"genre_scores_gemma":[0.7481973,0.0009264885,0.2431493,0.0003369359,0.0001314668,0.0001628051,0.0001817954,0.0001089715,0.006804926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004330433,"threshold_uncertainty_score":0.008610427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0193014765577517,"score_gpt":0.213621842302695,"score_spread":0.1943203657449433,"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."}}