{"id":"W3202133905","doi":"10.1007/s12553-021-00594-y","title":"Optimizing planning and design of COVID-19 drive-through mass vaccination clinics by simulation","year":2021,"lang":"en","type":"article","venue":"Health and Technology","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Canadian Institutes of Health Research; Public Health Agency; Public Health Agency of Canada","keywords":"Vaccination; Computer science; Throughput; Set (abstract data type); Process (computing); Discrete event simulation; Queueing theory; Simulation; Operations research; Engineering; Medicine; Operating system; Computer network","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":[0.0007886719,0.0001047757,0.0002975723,0.0001559745,0.000939327,0.000008461284,0.00004518767,0.0004159226,0.00003668888],"category_scores_gemma":[0.001549452,0.000108697,0.00001059098,0.0004495757,0.00004409341,0.0001200941,0.00004752466,0.0004128128,0.000001919113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001200897,"about_ca_system_score_gemma":0.001525711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005894928,"about_ca_topic_score_gemma":0.00001471476,"domain_scores_codex":[0.9981113,0.0004988632,0.0006745717,0.0003002029,0.00009487267,0.0003201582],"domain_scores_gemma":[0.9982761,0.0007110546,0.0003287546,0.0001815429,0.0003005663,0.0002019706],"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":[0.0005166827,0.0004139293,0.2627792,0.008951283,0.000132345,0.00004440913,0.05922062,0.4325302,0.002465594,0.1092497,0.009098294,0.1145977],"study_design_scores_gemma":[0.004495638,0.00102283,0.0006722012,0.0005122618,0.00003016023,0.00001995624,0.02640974,0.8995752,0.0002663372,0.01711021,0.04943201,0.0004534172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0262082,0.007662283,0.9395607,0.02549007,0.0001519367,0.0006949946,0.00001889068,0.0001369265,0.00007605901],"genre_scores_gemma":[0.7236638,0.005100078,0.2639768,0.006752321,0.0000486775,0.0000917337,0.000193193,0.00002674764,0.0001466234],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6974556,"threshold_uncertainty_score":0.7224638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1296932314467449,"score_gpt":0.4868824351770952,"score_spread":0.3571892037303502,"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."}}