{"id":"W2044884901","doi":"10.1016/j.comcom.2004.07.003","title":"Proportional loss rate differentiation in a FIFO queue","year":2004,"lang":"en","type":"article","venue":"Computer Communications","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Computer science; FIFO (computing and electronics); Queue; Real-time computing; Computer network; 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":[0.0002416349,0.0001201725,0.0001398691,0.0001290527,0.0001922301,0.0001383625,0.001771452,0.00005434985,0.000007291552],"category_scores_gemma":[0.00001189101,0.0001210788,0.00005868792,0.0004463117,0.00008120309,0.0003812569,0.0005285479,0.0002292625,0.00008494262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009996758,"about_ca_system_score_gemma":0.0001485491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002362396,"about_ca_topic_score_gemma":0.0001893652,"domain_scores_codex":[0.998904,0.0001668151,0.0003258989,0.0002588072,0.0001385132,0.0002059074],"domain_scores_gemma":[0.9981263,0.000128996,0.0001064415,0.001460408,0.0001087746,0.00006911002],"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":[0.000008034507,0.0005561448,0.002586725,0.00000729054,0.00002731487,0.00001018786,0.0007288816,0.02933658,0.00005487409,0.7948271,0.0003865568,0.1714703],"study_design_scores_gemma":[0.001565085,0.00004653433,0.08366165,0.00008121456,0.000007081452,0.00002398298,0.000007041489,0.8768483,0.00003297121,0.03253331,0.004908413,0.0002844596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0217023,0.0002408818,0.962068,0.0149094,0.0002452412,0.0002451038,0.000001709504,0.0002205388,0.0003667811],"genre_scores_gemma":[0.9185224,0.00007828861,0.08055104,0.0006124089,0.00007428689,0.00008207117,0.00002946637,0.000006438896,0.00004359981],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8968201,"threshold_uncertainty_score":0.4937449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864538382352708,"score_gpt":0.2462081777786052,"score_spread":0.2275627939550781,"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."}}