{"id":"W4401508084","doi":"10.1109/infocom52122.2024.10621139","title":"Routing-Oblivious Network Tomography with Flow-Based Generative Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Network tomography; Routing (electronic design automation); Flow (mathematics); Tomography; Generative model; Generative grammar; Artificial intelligence; Computer network; Network topology; Medicine; Mechanics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002123213,0.001022111,0.00112016,0.0008466089,0.0005340806,0.001451012,0.002235992,0.001658657,0.001829466],"category_scores_gemma":[0.008642389,0.0009121895,0.001109807,0.0009881557,0.002108121,0.002984496,0.001977946,0.002750689,0.0005241538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00205991,"about_ca_system_score_gemma":0.001550563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008694477,"about_ca_topic_score_gemma":0.00837772,"domain_scores_codex":[0.9991829,0.0003442377,0.00002612322,0.0002035232,0.0001560775,0.00008716572],"domain_scores_gemma":[0.9955435,0.003069108,0.0004339129,0.0005222511,0.0002872073,0.000144037],"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":[0.00005765298,0.00001812006,0.0009779299,0.00002901837,0.00001925763,0.00005346735,0.00006197449,0.9632123,0.0007282104,0.02612115,0.001014181,0.007706762],"study_design_scores_gemma":[0.000004777977,0.000004194218,0.00006259045,0.000003398396,0.000002765233,0.00001396481,0.000003579908,0.9900932,0.0001628649,0.009473003,0.0001709256,0.000004705786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02531948,0.0003175318,0.9705393,0.0008227057,0.00004814791,0.00004778343,0.0003158023,0.0006039923,0.00198524],"genre_scores_gemma":[0.8654568,0.0006724014,0.1248908,0.0006772089,0.0001523507,0.0002567469,0.0011608,0.0003288744,0.006403888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008694477,"threshold_uncertainty_score":0.01728773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02203391758253553,"score_gpt":0.2479176587584923,"score_spread":0.2258837411759567,"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."}}