{"id":"W2188979760","doi":"10.1109/wsc.2015.7408410","title":"Waiting time predictors for multi-skill call centers","year":2015,"lang":"en","type":"article","venue":"2015 Winter Simulation Conference (WSC)","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Queue; Queueing theory; Heuristic; Artificial neural network; Customer service; Service (business); Recurrent neural network; Regression; Machine learning; Artificial intelligence; Real-time computing; Computer network; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005343794,0.0002518654,0.0002896327,0.0002453443,0.0001283144,0.0002548504,0.0003168324,0.00009401349,0.0002999162],"category_scores_gemma":[0.0008646562,0.0002416759,0.0001398384,0.000220518,0.00006895319,0.001333583,0.0001530091,0.000117599,0.0006659371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008586558,"about_ca_system_score_gemma":0.00003442305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005598365,"about_ca_topic_score_gemma":0.00004952148,"domain_scores_codex":[0.9985237,0.00002811592,0.0004118033,0.0004172389,0.0002617887,0.0003574067],"domain_scores_gemma":[0.9984096,0.0001376761,0.0003386376,0.0003112293,0.0007516363,0.00005125557],"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":[0.0007906642,0.0003923268,0.02340467,0.0002245327,0.0004034774,0.0000121534,0.001867655,0.9205806,0.001045109,0.01643648,0.0237359,0.01110649],"study_design_scores_gemma":[0.001209601,0.00001719686,0.0002725706,0.00007388032,0.00009263633,3.152043e-7,0.0002211946,0.9587352,0.00004150593,0.002000788,0.03705351,0.000281655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2457078,0.00003708142,0.7397038,0.001460575,0.001280123,0.00113064,0.00004133876,0.0008248838,0.009813776],"genre_scores_gemma":[0.9928538,3.719783e-7,0.001535597,0.0007621995,0.0007661869,0.00002576066,0.0001862865,0.00004460012,0.003825186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.747146,"threshold_uncertainty_score":0.9855257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07507864866832731,"score_gpt":0.3111709493835819,"score_spread":0.2360923007152546,"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."}}