{"id":"W2086396347","doi":"10.1007/s10479-006-5293-9","title":"Discrete-time analysis of the GI/G/1 system with Bernoulli retrials: An algorithmic approach","year":2006,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bernoulli's principle; Markov chain; Computer science; Independence (probability theory); Theory of computation; Mathematical optimization; Algorithm; Bernoulli process; Markov chain Monte Carlo; Matrix (chemical analysis); Continuous-time Markov chain; Exploit; Set (abstract data type); Markov process; Discrete time and continuous time; Applied mathematics; Mathematics; Markov property; Markov model; Bayesian probability; Artificial intelligence","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":[0.003287459,0.001091952,0.001551883,0.001848498,0.001136156,0.002874097,0.003150819,0.001910402,0.0057676],"category_scores_gemma":[0.009218656,0.0009114575,0.001352838,0.001264546,0.002772173,0.003404855,0.002213108,0.003237123,0.0004301134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003575101,"about_ca_system_score_gemma":0.002990953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007884804,"about_ca_topic_score_gemma":0.005459699,"domain_scores_codex":[0.9988285,0.000417113,0.00003704539,0.0001491367,0.0002870442,0.0002811585],"domain_scores_gemma":[0.9943675,0.00399388,0.0005562763,0.0001920865,0.00054918,0.0003409626],"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":[0.0000695607,0.00007038496,0.0007693073,0.00008255892,0.00004173943,0.0001608492,0.00013572,0.6154962,0.0009612436,0.3745033,0.002097452,0.005611589],"study_design_scores_gemma":[0.000004913127,0.000006759965,0.0001092118,0.000005730967,0.000005672664,0.0000162346,0.00001571693,0.9626427,0.00007096165,0.03691429,0.0001999308,0.000007891751],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1199523,0.001436819,0.8560448,0.004114836,0.0002565315,0.0001473401,0.0003209039,0.0002379978,0.01748844],"genre_scores_gemma":[0.9197538,0.001126547,0.06398734,0.0004850444,0.0004177876,0.0001558913,0.000269764,0.000127042,0.01367687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007884804,"threshold_uncertainty_score":0.02593929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.087525396369387,"score_gpt":0.3652298565749086,"score_spread":0.2777044602055216,"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."}}