{"id":"W2940662702","doi":"10.2139/ssrn.3345302","title":"On the Optimal Design of a Bipartite Matching Queueing System","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bipartite graph; Queueing theory; Matching (statistics); Layered queueing network; Computer science; Mathematics; Mathematical optimization; Statistics; Theoretical computer science","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":[0.003306258,0.0008446166,0.001700552,0.001100496,0.0009202642,0.002180474,0.002014793,0.002433325,0.005417514],"category_scores_gemma":[0.01017406,0.001147823,0.000649726,0.001175541,0.001450264,0.001770643,0.00246363,0.001272431,0.000647239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002872427,"about_ca_system_score_gemma":0.003351479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005113504,"about_ca_topic_score_gemma":0.004153215,"domain_scores_codex":[0.9976967,0.001161055,0.00007708719,0.0003554875,0.0003081253,0.0004015345],"domain_scores_gemma":[0.995411,0.003310821,0.0002817869,0.0001640441,0.0004653441,0.0003669786],"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":[0.000267389,0.00009645458,0.00036795,0.0001190459,0.00004124499,0.0000480606,0.00006935871,0.9302508,0.003259031,0.04911007,0.001252456,0.01511817],"study_design_scores_gemma":[0.00002960783,0.00003946617,0.00005070148,0.000007471939,0.000009618757,0.000007633521,0.00001117117,0.9850257,0.0002253387,0.01436566,0.0002203042,0.00000729835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05071013,0.0002771836,0.9407783,0.0006785198,0.00007821969,0.000184738,0.0001323182,0.0002921735,0.006868362],"genre_scores_gemma":[0.8691204,0.0003627589,0.1252786,0.0002998718,0.00009428068,0.0002105219,0.0001357734,0.0001099558,0.004387718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005417514,"threshold_uncertainty_score":0.020841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008238001044069283,"score_gpt":0.2078086886748367,"score_spread":0.1995706876307674,"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."}}