{"id":"W2972862630","doi":"10.1049/cp.2019.1115","title":"Static Resource Allocation for Dynamic Traffic","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nunavut General Monitoring Plan; National Science Foundation","keywords":"Computer science; Bandwidth (computing); Probabilistic logic; Dynamic bandwidth allocation; Throughput; Bandwidth allocation; Resource allocation; Computer network; Resource management (computing); Distributed computing; Telecommunications; Wireless; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007000623,0.0006739785,0.0006082029,0.0005634153,0.0009995474,0.0009776698,0.001577755,0.000473701,0.003617108],"category_scores_gemma":[0.00260682,0.0002900579,0.0003340289,0.0009312824,0.0005812453,0.001324221,0.001090271,0.0008221694,0.0007622917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007674086,"about_ca_system_score_gemma":0.001764209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001735976,"about_ca_topic_score_gemma":0.00308366,"domain_scores_codex":[0.9992915,0.0001372444,0.0000280412,0.0001626812,0.0002099445,0.000170608],"domain_scores_gemma":[0.9991633,0.0003652515,0.00006168753,0.0001687395,0.0001726142,0.00006849089],"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":[0.0003376214,0.0002190626,0.001187638,0.0001251181,0.00004168859,0.0001674233,0.00009296178,0.5639542,0.01821452,0.05040481,0.008104878,0.3571501],"study_design_scores_gemma":[0.00001271195,0.00005719066,0.000193307,0.000005335778,0.000008374072,0.00009464246,0.00001702879,0.9819042,0.002919942,0.01235574,0.002418628,0.00001285041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01826623,0.0002066635,0.9745815,0.0001126497,0.00007231575,0.00005014906,0.00006393752,0.0007963945,0.005850162],"genre_scores_gemma":[0.8323702,0.0002130073,0.1622261,0.0001652953,0.0001194236,0.0001366959,0.0001800048,0.0001795515,0.004409581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003617108,"threshold_uncertainty_score":0.01210046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005010908860438817,"score_gpt":0.2149226236003591,"score_spread":0.2099117147399203,"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."}}