{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002157733,0.0008158556,0.0007488764,0.0009316301,0.0006145405,0.001873213,0.002277542,0.002306976,0.002181358],"category_scores_gemma":[0.01101778,0.0007450319,0.0006765424,0.001576202,0.0007118728,0.00200299,0.0009017285,0.001989832,0.0004942149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001959431,"about_ca_system_score_gemma":0.001391159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0212085,"about_ca_topic_score_gemma":0.01493515,"domain_scores_codex":[0.998744,0.000493692,0.00006101584,0.0002362738,0.000242113,0.0002228204],"domain_scores_gemma":[0.9946985,0.003074185,0.001024878,0.0003217933,0.0006145872,0.0002660722],"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":[0.00004740696,0.00007144461,0.004466515,0.00001956278,0.00002802211,0.00006893949,0.0001665467,0.9766216,0.000587152,0.0139392,0.0005202662,0.003463396],"study_design_scores_gemma":[0.000006246666,0.0000146882,0.000517972,0.000001934044,0.000004391509,0.00001117698,0.00002268346,0.9970202,0.00006945925,0.002133633,0.0001900618,0.000007575664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5873548,0.0002899527,0.4055454,0.000977117,0.00009051539,0.0001400262,0.0008363301,0.000513231,0.004252641],"genre_scores_gemma":[0.9730592,0.0002882155,0.02293857,0.00008792934,0.00008252828,0.0001357438,0.0005241768,0.00005282268,0.002830899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0212085,"threshold_uncertainty_score":0.04217011,"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."}}