{"id":"W4390050685","doi":"10.1287/ijoc.2022.0263","title":"Supervised ML for Solving the <i>GI</i>/<i>GI</i>/1 Queue","year":2023,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Queue; Queueing theory; Computer science; Range (aeronautics); Stationary distribution; Artificial neural network; Distribution (mathematics); Service (business); Sample (material); Operations research; Artificial intelligence; Algorithm; Mathematics; Machine learning; Engineering; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0018569,0.0009977225,0.001096555,0.0006727005,0.0006243751,0.001007472,0.002777111,0.00171608,0.002004128],"category_scores_gemma":[0.007068918,0.0006412055,0.0008856855,0.0007062326,0.0008735708,0.001466066,0.001188255,0.003199819,0.0004875335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002020633,"about_ca_system_score_gemma":0.002934507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01224728,"about_ca_topic_score_gemma":0.0159213,"domain_scores_codex":[0.9992799,0.0002117013,0.00004258635,0.0002528437,0.0001133609,0.00009967024],"domain_scores_gemma":[0.9947307,0.003643883,0.0004592154,0.0004109302,0.0005716117,0.0001836461],"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":[0.0001579592,0.0002329541,0.003264183,0.0001660084,0.00008521966,0.00008066958,0.00008300203,0.8711274,0.001050014,0.007660647,0.006237504,0.1098543],"study_design_scores_gemma":[0.000005345804,0.000007945559,0.00007591137,0.000002417543,0.000001708545,0.000003072904,0.000003782015,0.9973739,0.0001593764,0.002282418,0.00008244857,0.000001566431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08152233,0.0006078706,0.909385,0.001509555,0.0001321874,0.0001295898,0.0005760472,0.003377571,0.002759794],"genre_scores_gemma":[0.6348358,0.0002278315,0.3564083,0.0007607639,0.0003449112,0.0003242963,0.001656126,0.0003155577,0.005126559],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01224728,"threshold_uncertainty_score":0.02435201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02410599635534466,"score_gpt":0.2610686845859325,"score_spread":0.2369626882305879,"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."}}