{"id":"W2142721553","doi":"10.1287/opre.2013.1236","title":"Using Strategic Idleness to Improve Customer Service Experience in Service Networks","year":2014,"lang":"en","type":"article","venue":"Operations Research","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Queue; Service (business); Queueing theory; Operations research; Computer network; Quality of service; Service quality; Mathematical optimization; Business; Mathematics; Marketing","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":[0.001964672,0.0005513839,0.0006143434,0.0004806045,0.0005383467,0.00137574,0.001060154,0.0004256818,0.001034052],"category_scores_gemma":[0.008360743,0.0002793845,0.0002637523,0.0006296503,0.0009150543,0.001880858,0.001318431,0.0006832523,0.0001249934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001266001,"about_ca_system_score_gemma":0.001616117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002495632,"about_ca_topic_score_gemma":0.00237441,"domain_scores_codex":[0.9990927,0.0003736365,0.00005758695,0.0001168588,0.0001741656,0.0001850641],"domain_scores_gemma":[0.9974326,0.00154812,0.0003390179,0.0002137452,0.000243031,0.0002234058],"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":[0.0005063759,0.0003686724,0.007757104,0.0001344296,0.00007199046,0.0001669311,0.0004960527,0.8227532,0.01412413,0.03984239,0.0007116617,0.1130671],"study_design_scores_gemma":[0.00002006895,0.0003024756,0.0009201296,0.000009220063,0.00003303558,0.00005521449,0.0001544647,0.9713495,0.003525341,0.02317808,0.0004304234,0.00002206549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.456033,0.0004099054,0.5387919,0.0003940417,0.00005114764,0.00005376789,0.00002646005,0.0004858824,0.003753856],"genre_scores_gemma":[0.9893755,0.00007527509,0.01028381,0.00002408214,0.000005277496,0.00000996433,0.000007057107,0.00001140006,0.0002075322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002495632,"threshold_uncertainty_score":0.01039028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1365897488044003,"score_gpt":0.3996399555342409,"score_spread":0.2630502067298407,"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."}}