{"id":"W3010541414","doi":"10.23919/cnsm46954.2019.9012688","title":"Predicting Distributions of Waiting Times in Customer Service Systems using Mixture Density Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Queueing theory; Variance (accounting); Queue; Service (business); Percentile; Data mining; Statistics; Computer network; Mathematics","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":[0.002571085,0.0006487991,0.0005762588,0.001567373,0.000383152,0.001006089,0.001081371,0.00104105,0.000748657],"category_scores_gemma":[0.009682901,0.0005783681,0.0007288574,0.00117386,0.0005104912,0.001715054,0.0005859782,0.001533919,0.0002515657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001928938,"about_ca_system_score_gemma":0.0006280063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02503244,"about_ca_topic_score_gemma":0.01926923,"domain_scores_codex":[0.9994462,0.000187963,0.00002659254,0.0001388268,0.0001220867,0.00007824195],"domain_scores_gemma":[0.9938904,0.004634424,0.0003999337,0.000329767,0.000550046,0.0001954584],"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":[0.0001673589,0.0001033452,0.01629228,0.00002456524,0.00003962295,0.0000326187,0.00006540743,0.9672201,0.0005613843,0.002254988,0.0008258975,0.01241229],"study_design_scores_gemma":[0.000001971785,0.000006408151,0.001110392,0.00000117773,0.000001748378,0.000003290204,0.000005777969,0.9980454,0.0001046798,0.0006754642,0.00003994879,0.000003648393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8763575,0.0003805445,0.1204757,0.0004485274,0.00003702607,0.00006433476,0.0009747897,0.0006078828,0.0006536406],"genre_scores_gemma":[0.9855675,0.0001377777,0.01254128,0.00003161312,0.0000166284,0.00003011681,0.001269626,0.00002310792,0.0003822981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02503244,"threshold_uncertainty_score":0.04977345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009490698506952867,"score_gpt":0.2176143606612597,"score_spread":0.2081236621543069,"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."}}