{"id":"W2090656321","doi":"10.5539/cis.v1n1p12","title":"Speedy Algorithm of Public Traffic Route Selection Based on Adaptive Backbone Network","year":2008,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Backbone network; Selection (genetic algorithm); Computation; Core (optical fiber); Algorithm; Service (business); Selection algorithm; Computer network; Telecommunications; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0007109247,0.0004611003,0.0005796261,0.001122391,0.0007185562,0.0008259487,0.001146031,0.0004830122,0.0024335],"category_scores_gemma":[0.002381244,0.0002385351,0.0002591004,0.001000725,0.000460039,0.001355819,0.0008765152,0.0004306384,0.000385626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008404424,"about_ca_system_score_gemma":0.00137086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006564159,"about_ca_topic_score_gemma":0.004312838,"domain_scores_codex":[0.9993931,0.00009735653,0.00003918907,0.0001612112,0.0002241429,0.00008508116],"domain_scores_gemma":[0.99925,0.0002008984,0.00008838066,0.0001049871,0.000313516,0.00004214614],"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":[0.0005129549,0.0001110078,0.005682701,0.000118831,0.00008241789,0.0001181184,0.0003133631,0.3628463,0.01153092,0.02351252,0.01055701,0.5846139],"study_design_scores_gemma":[0.0001073179,0.00006903097,0.0008705382,0.000007273778,0.00002365853,0.0001280222,0.00006347973,0.9838697,0.004035039,0.006751501,0.004053902,0.00002051645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05236438,0.0002147749,0.9427149,0.0001676601,0.00008765556,0.0001481549,0.00007199659,0.0008951652,0.003335249],"genre_scores_gemma":[0.5754119,0.0002846706,0.4171158,0.00009395592,0.00009911494,0.0003140717,0.0004221113,0.0001026832,0.006155742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006564159,"threshold_uncertainty_score":0.01305193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675363555684376,"score_gpt":0.2227268167047123,"score_spread":0.1959731811478685,"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."}}