{"id":"W4321435121","doi":"10.1364/jocn.474329","title":"Merging engine implementation for intra-frame sharing in multi-tenant virtual passive optical networks","year":2023,"lang":"en","type":"article","venue":"Journal of Optical Communications and Networking","topic":"Advanced Photonic Communication Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Passive optical network; Computer network; Dynamic bandwidth allocation; Frame (networking); Bandwidth (computing); Real-time computing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009599056,0.0001539779,0.0003345981,0.0002388661,0.0001766201,0.00008008516,0.0006142897,0.00009412065,0.00000302466],"category_scores_gemma":[0.00005802809,0.0001355158,0.00008615162,0.0005304319,0.00007498058,0.0002383873,0.0003106966,0.0006024531,0.000001234988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001430927,"about_ca_system_score_gemma":0.00002263222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000383905,"about_ca_topic_score_gemma":0.00006493808,"domain_scores_codex":[0.9984366,0.00005486897,0.0008896023,0.0001217058,0.0001396141,0.0003575991],"domain_scores_gemma":[0.9978901,0.0011121,0.0001773408,0.0005748501,0.0001219586,0.0001236511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006556176,0.00009748572,0.002525523,0.00006129492,0.0002102602,0.00001143867,0.001679838,0.4313928,0.002412332,0.01737973,0.0001393709,0.5440243],"study_design_scores_gemma":[0.0009977263,0.00008824188,0.00129998,0.0002480441,0.00002984976,0.00003308772,0.001373603,0.9871234,0.0001014785,0.0003072073,0.008227211,0.0001701836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1237568,0.006898907,0.8653193,0.001807373,0.000856081,0.0008369331,0.000007853813,0.0002167859,0.0002999603],"genre_scores_gemma":[0.9539192,0.007394435,0.03838903,0.00002304826,0.0001420115,0.00006448697,0.00002126383,0.00003841618,0.000008095367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8301624,"threshold_uncertainty_score":0.5526175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05192804733328425,"score_gpt":0.3416951611749854,"score_spread":0.2897671138417012,"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."}}