{"id":"W2894171144","doi":"10.1007/s42243-018-0151-y","title":"MMPP/M/C queue with congestion-based staffing policy and applications in operations of steel industry","year":2018,"lang":"en","type":"article","venue":"Journal of Iron and Steel Research International","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"National Key Research and Development Program of China; Higher Education Discipline Innovation Project","keywords":"Slab; Staffing; Queue; Computer science; Poisson distribution; Service (business); Queueing theory; Operations research; Production (economics); Real-time computing; Simulation; Computer network; Mathematics; Statistics; Engineering; Business; Structural engineering; Economics; Microeconomics","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":[0.00112695,0.00007406249,0.0001411238,0.001067056,0.0001385301,0.0001394664,0.0001900345,0.00006008522,0.00008655301],"category_scores_gemma":[0.0006893025,0.00005852095,0.00002296402,0.0005073072,0.0003105231,0.0006305898,0.00007608246,0.0003863341,0.000002149134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007628267,"about_ca_system_score_gemma":0.0001540426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002288882,"about_ca_topic_score_gemma":0.000511887,"domain_scores_codex":[0.9989423,0.00004145352,0.0003076164,0.0001190576,0.0004521779,0.0001374018],"domain_scores_gemma":[0.9983799,0.0002211568,0.0001743718,0.00009078535,0.001105899,0.00002792558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0009536537,0.0006101863,0.2119161,0.0001843605,0.0001839901,0.00002677101,0.0004052013,0.03189123,0.007193844,0.7301068,0.0003563626,0.01617144],"study_design_scores_gemma":[0.01292608,0.001165611,0.5715579,0.002325755,0.0002167311,0.0001009704,0.01440996,0.2595023,0.003054307,0.03858907,0.09504989,0.001101449],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893121,0.00004825338,0.002761725,0.002964074,0.00003516574,0.0001322673,0.000005945893,0.000005340141,0.004735195],"genre_scores_gemma":[0.9980804,0.00001942977,0.0006418456,0.00014258,0.0007249407,0.000007575327,0.000005053174,0.000008455559,0.0003697322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6915178,"threshold_uncertainty_score":0.2386415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03745110774266113,"score_gpt":0.3618498516012329,"score_spread":0.3243987438585718,"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."}}