{"id":"W2899220013","doi":"10.1109/jlt.2018.2873161","title":"Integrating Fog With Long-Reach PONs From a Dynamic Bandwidth Allocation Perspective","year":2018,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Passive optical network; Dynamic bandwidth allocation; Computer science; Computer network; Access network; Cloudlet; 10G-PON; Bandwidth allocation; Bandwidth (computing); Edge computing; Cloud computing; Upstream (networking); Enhanced Data Rates for GSM Evolution; Distributed computing; Telecommunications; Wavelength-division multiplexing","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":[0.0004042789,0.0004355685,0.0003012326,0.000225018,0.0006826408,0.0009409653,0.0007025375,0.0006871946,0.001042019],"category_scores_gemma":[0.0009059729,0.0001612589,0.0002550932,0.0002517901,0.0006942059,0.001640723,0.0008604714,0.000694599,0.00009549183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007833264,"about_ca_system_score_gemma":0.000790827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004844021,"about_ca_topic_score_gemma":0.007756318,"domain_scores_codex":[0.9997795,0.00004583251,0.000003551577,0.00002615198,0.00005938701,0.0000856148],"domain_scores_gemma":[0.9996605,0.0001523069,0.00002914075,0.00004529984,0.00006665431,0.00004609776],"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":[0.0002700565,0.000286194,0.00367902,0.0001393914,0.00009580804,0.001042938,0.0002603469,0.7991859,0.04081585,0.09953584,0.001797289,0.05289133],"study_design_scores_gemma":[0.000007916183,0.0001192506,0.0006603769,0.00001096507,0.00001921452,0.0001868491,0.0001019995,0.976723,0.003187167,0.01701649,0.001951786,0.00001495327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4521557,0.001596333,0.5017562,0.0008676884,0.0002404778,0.0001078585,0.00006006151,0.0004323134,0.04278328],"genre_scores_gemma":[0.9851481,0.0002187694,0.01314433,0.00006155368,0.00002645216,0.000009376507,0.00001135735,0.00001044571,0.001369551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004844021,"threshold_uncertainty_score":0.009631634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005882708159833657,"score_gpt":0.2503409743620427,"score_spread":0.244458266202209,"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."}}