{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003329986,0.0001420406,0.0004541443,0.001166911,0.0005337563,0.0002189181,0.0006665051,0.00006520985,0.0001125249],"category_scores_gemma":[0.0002941681,0.00009131507,0.0002124585,0.006518924,0.0003531671,0.0009702739,0.0001843671,0.0001994033,0.00002248095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002507289,"about_ca_system_score_gemma":0.00005530792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004462814,"about_ca_topic_score_gemma":0.0007156865,"domain_scores_codex":[0.9975974,0.0002933789,0.000510798,0.0003494397,0.000927778,0.0003212336],"domain_scores_gemma":[0.9971586,0.0001332301,0.0001636185,0.0008888459,0.001639726,0.00001594244],"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":[0.0001192672,0.0003162573,0.002147515,0.0001337526,0.0008881875,0.000002227784,0.00008459415,0.8577474,0.003302704,0.1343406,0.0005062072,0.0004112789],"study_design_scores_gemma":[0.0002774211,0.00004163666,0.004020509,0.0000718466,0.0006018741,7.796562e-7,0.001104147,0.9908235,0.001683619,0.0006445198,0.000535098,0.0001950702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9638751,0.0001580891,0.01374961,0.001237097,0.00003184581,0.0008586931,0.000068859,0.0000741448,0.01994659],"genre_scores_gemma":[0.9972384,0.000004345688,0.0008794915,0.00003599134,0.0002787408,0.00004669483,0.0001651291,0.00002285426,0.001328344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1336961,"threshold_uncertainty_score":0.674647,"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."}}