{"id":"W2143324015","doi":"10.1080/0740817x.2014.905735","title":"Maximizing throughput in zero-buffer tandem lines with dedicated and flexible servers","year":2014,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Server; Heuristics; Throughput; Computer science; Queue; Distributed computing; Tandem; Buffer (optical fiber); Zero (linguistics); Queueing theory; Computer network; Mathematical optimization; Operating system; Mathematics; Engineering; Wireless","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":[0.0002237143,0.0001457129,0.0001914053,0.0002439964,0.0002195772,0.00008952083,0.00009824904,0.0000557164,0.0001413083],"category_scores_gemma":[0.00002252065,0.0001263873,0.00004247361,0.0007058185,0.00008462872,0.0009434277,0.000008502599,0.0001601364,0.00003359618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002334521,"about_ca_system_score_gemma":0.000007903016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003520443,"about_ca_topic_score_gemma":0.001019306,"domain_scores_codex":[0.9992292,0.0000154847,0.0001739414,0.0002526408,0.0001154068,0.0002133538],"domain_scores_gemma":[0.9996194,0.00006192527,0.00007512281,0.0001725902,0.00005623334,0.00001474469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001335349,0.001132595,0.1104249,0.001078142,0.001048318,0.00008500467,0.001559023,0.6773558,0.007401431,0.0761708,0.000714821,0.1216938],"study_design_scores_gemma":[0.01356511,0.0001782201,0.05997659,0.001225938,0.002686911,0.00005422243,0.003100286,0.5736057,0.003921691,0.187096,0.1509602,0.003629151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3532787,0.00004707737,0.6407763,0.001642774,0.00009314634,0.000142449,0.000001182448,0.0002986318,0.003719739],"genre_scores_gemma":[0.9974161,0.00001710629,0.001444864,0.0005934162,0.0001159164,0.00001772507,0.000007457104,0.00002617027,0.0003612626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6441374,"threshold_uncertainty_score":0.5153925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392728282281946,"score_gpt":0.2212751497083812,"score_spread":0.2073478668855617,"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."}}