{"id":"W2120955848","doi":"10.1145/1242572.1242692","title":"Identifying and discriminating between web and peer-to-peer traffic in the network core","year":2007,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":209,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Traffic classification; Deep packet inspection; Network packet; TRACE (psycholinguistics); Core (optical fiber); Data mining; Categorization; Web server; Computer network; The Internet; Artificial intelligence; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003004545,0.0001271548,0.0001788293,0.0000851801,0.0001713343,0.0003965577,0.0004647054,0.00004945296,0.000005250355],"category_scores_gemma":[0.00007851433,0.00008489201,0.00003977146,0.0004506877,0.00003540343,0.0001996669,0.0002080495,0.0002111041,0.000004908624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001489555,"about_ca_system_score_gemma":0.00000969889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000221723,"about_ca_topic_score_gemma":0.001489314,"domain_scores_codex":[0.9985471,0.00004886687,0.0003074195,0.0003414791,0.0003777625,0.0003773084],"domain_scores_gemma":[0.9993002,0.0003493967,0.00005826611,0.0001413496,0.00007195578,0.0000788694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007942163,0.00006282092,0.0571047,0.00005018943,0.00008050505,0.0001251366,0.07220326,0.007603513,0.00005284559,0.4730021,0.006079468,0.3836275],"study_design_scores_gemma":[0.000306908,0.00007219835,0.1293296,0.00009786175,0.00003353453,0.0000297628,0.005249595,0.8628621,0.000009054045,0.0002073456,0.001481287,0.0003207999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7437724,0.00005601056,0.2536459,0.001263969,0.00007706121,0.00008467562,2.785312e-7,0.00003918157,0.001060502],"genre_scores_gemma":[0.9884244,0.000002145771,0.01049361,0.0004486157,0.0001980408,0.000001817278,0.000001969976,0.000006113508,0.0004233501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8552586,"threshold_uncertainty_score":0.3824015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04046067350496368,"score_gpt":0.2985782239292346,"score_spread":0.2581175504242709,"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."}}