{"id":"W2876319397","doi":"10.1109/jlt.2018.2855148","title":"BackHauling-as-a-Service (BHaaS) for 5G Optical Sliced Networks: An Optimized TCO Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Advanced Photonic Communication Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Backhaul (telecommunications); Computer science; Scalability; Computer network; Provisioning; Cellular network; Network planning and design; Base station; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005802396,0.0002422705,0.0005993813,0.0004032097,0.0001139064,0.00003707461,0.00112279,0.0004746605,0.00004363078],"category_scores_gemma":[0.000157148,0.0002199853,0.0001297294,0.0006393678,0.0001414235,0.0002670688,0.00005798409,0.0006718098,0.0000320564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001523602,"about_ca_system_score_gemma":0.00005934721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002089734,"about_ca_topic_score_gemma":0.000008714944,"domain_scores_codex":[0.998245,0.00004351091,0.0008970101,0.000201594,0.0001943519,0.00041856],"domain_scores_gemma":[0.9977431,0.0001922505,0.000344979,0.0008773971,0.0006883927,0.0001539032],"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":[0.002740563,0.001518891,0.0003340537,0.0007338679,0.002656301,0.00009706878,0.004703503,0.6582135,0.1670277,0.1081812,0.008866144,0.04492729],"study_design_scores_gemma":[0.005119665,0.001447773,0.00004283429,0.0002003889,0.0001430177,0.00187817,0.001685137,0.8503644,0.05183419,0.004810276,0.08173286,0.0007412674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07718983,0.001591716,0.9054105,0.001547282,0.001165985,0.0007427924,0.000004427377,0.0007687185,0.01157869],"genre_scores_gemma":[0.7390699,0.0001038271,0.2600616,0.0001407014,0.0004643078,0.00004359717,0.000004922319,0.00006825722,0.00004289056],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6618801,"threshold_uncertainty_score":0.8970742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829514866187083,"score_gpt":0.2779924159945333,"score_spread":0.2596972673326625,"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."}}