{"id":"W2032028146","doi":"10.1097/aln.0b013e318182a955","title":"Observations on Surgical Demand Time Series","year":2008,"lang":"en","type":"article","venue":"Anesthesiology","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; Queen's University","funders":"","keywords":"Variance (accounting); Statistics; Autoregressive model; Moving average; Series (stratigraphy); Time series; Econometrics; Linear model; Medicine; Nonlinear system; Scheduling (production processes); Mathematics; Mathematical optimization; Accounting; Economics","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":[0.0009048786,0.0002689566,0.0001934725,0.001098212,0.0001618934,0.0003588765,0.0002823857,0.0002806417,0.002931711],"category_scores_gemma":[0.009221382,0.0001807175,0.0002425321,0.001527332,0.0001947647,0.0004345365,0.0003627558,0.0003882034,0.0004395841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007969437,"about_ca_system_score_gemma":0.0004113057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008327812,"about_ca_topic_score_gemma":0.006233323,"domain_scores_codex":[0.9994515,0.0001079096,0.0000554996,0.0000950574,0.000233294,0.0000568101],"domain_scores_gemma":[0.9926363,0.004368585,0.001493749,0.000383549,0.0009538431,0.0001640039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004601658,0.0001811409,0.8874885,0.0002270227,0.0001417783,0.0002952553,0.0005238085,0.0493961,0.004305504,0.001718041,0.002115821,0.05314676],"study_design_scores_gemma":[0.00001333223,0.0001408111,0.947511,0.00001790792,0.00002724208,0.0001089086,0.0002667575,0.04634176,0.001490434,0.001143768,0.002917608,0.00002059342],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869328,0.0001106126,0.006520285,0.0002245425,0.00002033219,0.00002979265,0.003158364,0.00004467645,0.002958639],"genre_scores_gemma":[0.9950478,0.000122696,0.001237194,0.00001876427,0.00003345514,0.0000194209,0.002990473,0.000008292905,0.0005219407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008327812,"threshold_uncertainty_score":0.01655871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.111871493787008,"score_gpt":0.3809931513304992,"score_spread":0.2691216575434912,"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."}}