{"id":"W3085408771","doi":"10.5267/j.dsl.2020.8.002","title":"Optimization of multi-channel queuing systems with a single retail attempt: Economic approach","year":2020,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Labor Market and Education","field":"Economics, Econometrics and Finance","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Queueing theory; Channel (broadcasting); Operations research; Computer science; Multi-objective optimization; Business; Mathematical optimization; Environmental economics; Economics; Industrial organization; Engineering; Telecommunications; Mathematics; Computer network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003104835,0.001280103,0.002037715,0.001298632,0.0007381017,0.002755766,0.001674153,0.002747007,0.002650104],"category_scores_gemma":[0.005334601,0.001129886,0.001419971,0.001149789,0.002240626,0.002494869,0.001765627,0.002116431,0.0001984096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004277098,"about_ca_system_score_gemma":0.002546093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01011898,"about_ca_topic_score_gemma":0.006519923,"domain_scores_codex":[0.9986504,0.0007266714,0.00004267483,0.0001590826,0.0001868276,0.0002345104],"domain_scores_gemma":[0.9959708,0.003020579,0.0003456355,0.00006900162,0.0003211919,0.0002727896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003611434,0.00004190428,0.0003688197,0.00005020799,0.00003392043,0.00007042157,0.00002462451,0.9502042,0.0001845378,0.04707526,0.0004050974,0.001504907],"study_design_scores_gemma":[0.000006348393,0.00001367106,0.0001037726,0.000006177191,0.000005307398,0.000004581463,0.00001751547,0.9907006,0.00002282419,0.008964328,0.000149309,0.000005555229],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1583885,0.004105352,0.8061872,0.004552193,0.000333244,0.0001930014,0.0003044808,0.0001295278,0.0258065],"genre_scores_gemma":[0.9540936,0.001605943,0.02926897,0.0002216202,0.0002181687,0.0002010127,0.0001123361,0.00007010678,0.01420833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01011898,"threshold_uncertainty_score":0.03103262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07549971743082352,"score_gpt":0.2331618364987974,"score_spread":0.1576621190679739,"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."}}