{"id":"W4226040218","doi":"10.2139/ssrn.4055715","title":"Asymptotic Analysis of Multi-Class Advance Patient Scheduling","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scheduling (production processes); Computer science; Class (philosophy); Mathematical optimization; Mathematics; Artificial intelligence","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":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002746595,0.0001180667,0.0003283585,0.0004766631,0.002085532,0.000008321745,0.0001970749,0.00006789278,0.0004164714],"category_scores_gemma":[0.0002394621,0.000113054,0.000161137,0.00134655,0.00002054081,0.0001237386,0.00008779949,0.003094642,0.00001009078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001926536,"about_ca_system_score_gemma":0.004646627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001765076,"about_ca_topic_score_gemma":0.001296341,"domain_scores_codex":[0.9961078,0.000929812,0.0008241219,0.0002069493,0.0003936252,0.001537723],"domain_scores_gemma":[0.9986455,0.0001456914,0.0005283405,0.0002276782,0.0003513097,0.0001014597],"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":[0.00006042591,0.0001834488,0.02583844,0.00001845386,0.0006414549,0.000001403171,0.002668479,0.9237471,0.0002089857,0.03821887,0.00001453202,0.008398453],"study_design_scores_gemma":[0.00192683,0.000986468,0.0036097,0.00005891699,0.0007425935,0.00003312829,0.06235782,0.9222869,0.00001992426,0.003614761,0.003968521,0.0003944257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8559904,0.002243276,0.1389694,0.001466957,0.0005622684,0.0004063242,0.00002634134,0.00003679986,0.0002982662],"genre_scores_gemma":[0.993341,0.0009556018,0.004415122,0.0003766535,0.00006928626,0.0000595637,0.00004323121,0.00002267968,0.0007168692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1373506,"threshold_uncertainty_score":0.9992136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02872826269066971,"score_gpt":0.372200390925367,"score_spread":0.3434721282346973,"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."}}