{"id":"W3182167228","doi":"10.1007/s11432-020-3094-6","title":"Energy-efficient URLLC service provisioning in softwarization-based networks","year":2021,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Provisioning; Energy consumption; Distributed computing; Quality of service; Flexibility (engineering); Resource allocation; Efficient energy use; Software-defined networking; Computer network; 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":[0.0006950612,0.0005212019,0.0006210358,0.0007119926,0.001120844,0.001872063,0.001417405,0.0006287424,0.004024819],"category_scores_gemma":[0.001886738,0.0002048098,0.0002560135,0.0008494346,0.0005223943,0.001874242,0.00176313,0.000663746,0.0006313103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210451,"about_ca_system_score_gemma":0.001170148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002407018,"about_ca_topic_score_gemma":0.005924604,"domain_scores_codex":[0.9993592,0.0001355392,0.00003006779,0.0001140665,0.000119997,0.0002412268],"domain_scores_gemma":[0.999008,0.0003063948,0.0001074592,0.000206879,0.0002680737,0.0001032715],"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":[0.001540186,0.0006280213,0.004104409,0.0004679517,0.0000922314,0.0008478353,0.0003822306,0.5118747,0.05887727,0.04751574,0.01529203,0.3583775],"study_design_scores_gemma":[0.00001078698,0.00008427975,0.0004663303,0.00001586119,0.00001561629,0.0001014605,0.000135671,0.9842165,0.00566755,0.007454393,0.001817459,0.00001400706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5190428,0.001402337,0.4539711,0.001159991,0.0004004436,0.0002147962,0.000412652,0.003102554,0.02029323],"genre_scores_gemma":[0.9846494,0.0001153711,0.01286663,0.00008757667,0.00002726408,0.00002366137,0.0000966157,0.00003865935,0.002094865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004024819,"threshold_uncertainty_score":0.01346439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009209955369485455,"score_gpt":0.2316726470053364,"score_spread":0.222462691635851,"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."}}