{"id":"W1729220625","doi":"10.1109/ccece.2000.849552","title":"Traffic identification using Bayes' classifier","year":2002,"lang":"en","type":"article","venue":"","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Naive Bayes classifier; Byte; Classifier (UML); Traffic classification; Bayes' theorem; Data mining; Quality of service; Bayes classifier; The Internet; Network packet; Server; Bayes error rate; Artificial intelligence; Machine learning; Computer network; Bayesian probability; World Wide Web; Support vector machine; Operating system","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000116816,0.00006126827,0.00005406146,0.00005995286,0.0001378932,0.0002666161,0.0002624621,0.00003887653,0.00008409099],"category_scores_gemma":[0.000009743186,0.0000560098,0.00002289541,0.0004101843,0.00001678627,0.0005823708,0.00002902401,0.00004755202,0.0001141449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002272232,"about_ca_system_score_gemma":0.000008494136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001049536,"about_ca_topic_score_gemma":0.000001221474,"domain_scores_codex":[0.9993432,0.00002513953,0.0001422914,0.0002185303,0.0001333585,0.0001375084],"domain_scores_gemma":[0.9995821,0.00001562738,0.0000552587,0.0002540796,0.00005240039,0.00004049753],"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":[0.000001413447,0.0001627355,0.0001614657,0.00001938449,0.00001328969,0.000005369591,0.001347421,0.2312059,0.001429174,0.02216021,0.01469901,0.7287946],"study_design_scores_gemma":[0.00006889817,0.000005231251,0.00005993578,0.000005950418,0.000002456194,0.000006975328,0.000007763703,0.9982424,0.0003311024,0.0002562869,0.0009335601,0.00007938059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01080963,0.0001222605,0.9821497,0.0003769713,0.000279014,0.00004004766,7.595937e-8,0.0002480805,0.005974235],"genre_scores_gemma":[0.8920048,0.00001734546,0.1040807,0.0001647897,0.00007104376,0.000001931513,8.849934e-7,0.000005449593,0.00365309],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8811952,"threshold_uncertainty_score":0.2570985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04397368960835139,"score_gpt":0.2437218741185943,"score_spread":0.1997481845102428,"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."}}