{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007739091,0.0001147477,0.0001291723,0.0005059428,0.0003825313,0.0003169842,0.0007400066,0.00002572136,0.000005626337],"category_scores_gemma":[0.00001368159,0.0001011119,0.00003060198,0.002340109,0.000248371,0.011686,0.0002562828,0.00008352569,0.00003133157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003675344,"about_ca_system_score_gemma":0.0001374861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003848329,"about_ca_topic_score_gemma":3.354788e-7,"domain_scores_codex":[0.9985749,0.00002929146,0.0002946093,0.000228435,0.0005837168,0.0002890559],"domain_scores_gemma":[0.9991507,0.00004222889,0.0001641302,0.0002828581,0.0002479436,0.0001121184],"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":[0.000003094774,0.00003879182,0.0001896643,0.000006884625,0.000003669719,0.000001011013,0.0003362968,0.01586294,0.000002667347,0.02090958,0.002210153,0.9604353],"study_design_scores_gemma":[0.000295562,0.0002275489,0.01239908,0.00001308789,0.000001354995,0.00001068917,0.000007685275,0.9812255,0.00005491641,0.00006936411,0.005575803,0.0001194223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002125769,0.000009065677,0.9948608,0.0001890625,0.0004163421,0.0001449151,0.000003925193,0.00009678173,0.002153322],"genre_scores_gemma":[0.4101612,0.00004079792,0.5885941,0.001035941,0.0001231994,0.000005234727,0.00001573852,0.000002753011,0.0000210535],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9653625,"threshold_uncertainty_score":0.8472066,"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."}}