{"id":"W2159592056","doi":"10.1109/icccn.2011.6005799","title":"DYNABYTE: A Dynamic Sampling Algorithm for Redundant Content Detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Byte; Redundancy (engineering); Network packet; Overhead (engineering); Algorithm; Data redundancy; Traverse; Sampling (signal processing); Throughput; Path (computing); Real-time computing; Computer network; Detector; Computer hardware","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":[0.0003117659,0.0001199479,0.0001590035,0.0000939646,0.0001306226,0.00009399602,0.0004164428,0.00005273762,0.00002978],"category_scores_gemma":[0.00002187238,0.00009708819,0.0001665871,0.0001490283,0.00001996445,0.0002151035,0.00008488642,0.00008406608,0.00002397145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006070132,"about_ca_system_score_gemma":0.00001941705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007458835,"about_ca_topic_score_gemma":0.000235944,"domain_scores_codex":[0.9989462,0.00002073405,0.0002713669,0.00035399,0.0001352743,0.0002723684],"domain_scores_gemma":[0.9994888,0.00004162585,0.00009015464,0.0001753917,0.0001411113,0.00006293815],"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":[0.000006549185,0.00007099615,0.000003855495,0.000007569892,0.00007904314,0.00000382592,0.001315628,0.0001036857,0.0007651045,0.1044161,0.0000262758,0.8932014],"study_design_scores_gemma":[0.0001918733,0.0001190785,0.0001112301,0.00001110872,0.00001504732,0.00001556688,0.0001885628,0.9971417,0.001570403,0.0001509741,0.0003433828,0.0001411205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005879817,0.00003684571,0.9926299,0.00004168318,0.0004551593,0.0001622166,0.000001191192,0.0001975063,0.0005956119],"genre_scores_gemma":[0.6976485,0.000002143489,0.3016981,0.00008624623,0.00002837657,0.00001832247,0.000001565535,0.000006606252,0.0005101598],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9970379,"threshold_uncertainty_score":0.3959141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05526519222612278,"score_gpt":0.2592141074113535,"score_spread":0.2039489151852308,"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."}}