{"id":"W1994418842","doi":"10.1111/j.1937-5956.2012.01362.x","title":"A Markov Chain Model for an EMS System with Repositioning","year":2012,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Table (database); Markov chain; Computer science; Markov model; Compliance (psychology); Point (geometry); Markov process; Operations research; Mathematical optimization; Mathematics; Data mining; Statistics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00182107,0.001158341,0.001309352,0.000955092,0.000860755,0.002220407,0.00224109,0.002118418,0.00758927],"category_scores_gemma":[0.003809165,0.00074139,0.001019674,0.001094903,0.001642026,0.002237626,0.001083909,0.00199076,0.0008819788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002798581,"about_ca_system_score_gemma":0.002945866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03213227,"about_ca_topic_score_gemma":0.01307788,"domain_scores_codex":[0.9989179,0.0003232588,0.00004559174,0.0002229022,0.0002233944,0.0002669464],"domain_scores_gemma":[0.9973943,0.001549654,0.0004117609,0.00009717525,0.0003515179,0.0001956598],"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":[0.00003496217,0.00002410008,0.0004666288,0.00001909904,0.00001478708,0.00009431614,0.00004908438,0.962945,0.0003497808,0.03461735,0.0004408876,0.0009440223],"study_design_scores_gemma":[0.00001322333,0.0000103791,0.00009081676,0.000003542315,0.000005176263,0.000008460142,0.000007985564,0.9953604,0.00004801905,0.00425905,0.0001855339,0.000007317622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08661919,0.0005046903,0.8981745,0.00185368,0.000137411,0.0002049917,0.001085649,0.0005050504,0.01091468],"genre_scores_gemma":[0.944008,0.0008668025,0.03861098,0.0002540274,0.0001388888,0.0006180209,0.0008312103,0.00007921251,0.01459285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03213227,"threshold_uncertainty_score":0.06389046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01811477911844871,"score_gpt":0.2381812913626622,"score_spread":0.2200665122442135,"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."}}