{"id":"W3134366789","doi":"10.1145/3453953.3453975","title":"Frequency scaling in multilevel queues","year":2021,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Mount Royal University","funders":"","keywords":"Queue; Bulk queue; Computer science; Multilevel queue; Scaling; Fork–join queue; Scheduling (production processes); Queueing theory; Queue management system; Real-time computing; Mathematical optimization; Mathematics; Computer network; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009347043,0.0003425347,0.0005223518,0.0005130835,0.0007569492,0.001319898,0.001095719,0.0006044676,0.00277141],"category_scores_gemma":[0.004029193,0.0002970443,0.0005619245,0.0006255088,0.000725964,0.001743321,0.0009920851,0.0009408757,0.0003553665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001401409,"about_ca_system_score_gemma":0.0009959495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004417164,"about_ca_topic_score_gemma":0.002435866,"domain_scores_codex":[0.9990548,0.0001798139,0.00004869458,0.0001983474,0.0002527105,0.0002655121],"domain_scores_gemma":[0.997977,0.000903896,0.0003501274,0.0002966468,0.0003055076,0.0001668599],"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":[0.0003166102,0.0002633559,0.007935992,0.0002133997,0.000127763,0.0003868631,0.0004820908,0.6053889,0.02156111,0.2896342,0.004868017,0.0688216],"study_design_scores_gemma":[0.0000251217,0.00008840134,0.001019168,0.00001063767,0.00002216304,0.0000869932,0.00006026458,0.9627114,0.001220881,0.03281894,0.00191587,0.00002013254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3689833,0.001698449,0.6128632,0.001136172,0.000360783,0.00009435008,0.0001965761,0.0007048527,0.01396231],"genre_scores_gemma":[0.9739069,0.000345466,0.02341669,0.0001303149,0.0001886121,0.00003830189,0.00003980985,0.00004047666,0.001893307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004417164,"threshold_uncertainty_score":0.01016796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08088553930748711,"score_gpt":0.3384169216474514,"score_spread":0.2575313823399643,"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."}}