{"id":"W2052677528","doi":"10.1007/s11134-011-9256-8","title":"Heavy traffic approximation for the stationary distribution of stochastic fluid networks","year":2011,"lang":"en","type":"article","venue":"Queueing Systems","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stationary distribution; Reflected Brownian motion; Queue; Limit (mathematics); Monotonic function; Mathematics; Brownian motion; Statistical physics; Heavy traffic approximation; Path (computing); Stationary state; Distribution (mathematics); Stationary process; Applied mathematics; Fluid queue; Mathematical optimization; Queueing theory; Computer science; Markov chain; Mathematical analysis; Geometric Brownian motion; Physics; Diffusion process; Statistics","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":[0.0038647,0.000995854,0.001546486,0.002125534,0.001118657,0.002421955,0.002836724,0.002043489,0.002636801],"category_scores_gemma":[0.0198383,0.0008752158,0.001046987,0.001321609,0.003470994,0.004147666,0.00203678,0.003933499,0.0006998688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003581044,"about_ca_system_score_gemma":0.00251949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01504859,"about_ca_topic_score_gemma":0.006790746,"domain_scores_codex":[0.9988416,0.0004715707,0.00002998219,0.0001418249,0.0003100548,0.000204929],"domain_scores_gemma":[0.9948068,0.003301287,0.0002806427,0.0003739337,0.0008538617,0.0003834831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006623562,0.00004277341,0.0005654122,0.00004838688,0.00002711898,0.00008958184,0.0001252525,0.4621442,0.0007259659,0.5276919,0.002818804,0.005654341],"study_design_scores_gemma":[0.000004029715,0.000002560339,0.00003235357,0.000003872993,0.0000024688,0.000007136607,0.000006911352,0.9569233,0.00004369888,0.04271105,0.0002587546,0.00000381354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02610605,0.0009870958,0.9659055,0.001160875,0.0002478501,0.00002991219,0.0001026819,0.0002744362,0.005185532],"genre_scores_gemma":[0.8917693,0.00236081,0.08089573,0.000608082,0.000982466,0.0001747903,0.0004134682,0.0003737323,0.02242164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01504859,"threshold_uncertainty_score":0.02992201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350138444895103,"score_gpt":0.2234189640682639,"score_spread":0.1999175796193129,"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."}}