{"id":"W2108246181","doi":"10.1287/mnsc.1040.0236","title":"Modeling Daily Arrivals to a Telephone Call Center","year":2004,"lang":"en","type":"article","venue":"Management Science","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Call management; Computer science; Center (category theory); Focus (optics); Goodness of fit; Variance (accounting); Stochastic modelling; Statistics; Econometrics; Telecommunications; Mathematics; Call control; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001019968,0.0002014615,0.0001811557,0.0008502307,0.0005070368,0.0004895409,0.001070629,0.00002054166,0.0000750024],"category_scores_gemma":[0.0001048452,0.0001904252,0.00007488284,0.002729669,0.0001426366,0.001876328,0.0009316271,0.00008657056,0.001588204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001553077,"about_ca_system_score_gemma":0.00001118251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001770629,"about_ca_topic_score_gemma":0.00005812489,"domain_scores_codex":[0.9977095,0.000004667104,0.0002787527,0.000702201,0.0006880857,0.0006167614],"domain_scores_gemma":[0.9991491,0.000006219541,0.00008130247,0.0006056551,0.0001135288,0.00004423696],"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":[0.0000254131,0.0001007157,0.0003133765,0.00005246417,0.00002135181,0.00003815069,0.00008972819,0.8665854,0.001322277,0.1261566,0.0002278581,0.005066647],"study_design_scores_gemma":[0.004646714,0.00005944129,0.003042073,0.000707199,0.0003987281,0.000008701022,0.002554979,0.6047916,0.001481155,0.2795073,0.09981824,0.002983788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4829958,0.00001494966,0.4380943,0.002614295,0.000471568,0.0005332402,0.000001201907,0.0003929417,0.07488173],"genre_scores_gemma":[0.9847416,0.000003346764,0.008132793,0.006113554,0.000239088,0.00003130044,0.00000313416,0.00002148079,0.000713722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5017458,"threshold_uncertainty_score":0.9991892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444649350268966,"score_gpt":0.2439805742878351,"score_spread":0.2295340807851454,"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."}}