{"id":"W3098483725","doi":"10.3390/healthcare8040469","title":"A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"Healthcare","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Durham; Response Biomedical (Canada); York University","funders":"Canadian Institutes of Health Research; Public Health Agency; Public Health Agency of Canada","keywords":"Vaccination; Pandemic; Immunization; Preparedness; Coronavirus disease 2019 (COVID-19); Mass vaccination; Computer science; Medical emergency; Medicine; Virology; Immunology; Political science; Disease","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0004758875,0.0001818734,0.0004127508,0.00002737431,0.0004236214,0.00001569688,0.0001329341,0.0001662748,0.00007010317],"category_scores_gemma":[0.02067974,0.0001607033,0.0001225734,0.0002011571,0.00001740485,0.0001201296,0.00007906352,0.000185776,0.0000183067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006007832,"about_ca_system_score_gemma":0.0001034574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009599925,"about_ca_topic_score_gemma":0.00009406995,"domain_scores_codex":[0.9982219,0.0002146052,0.000547737,0.0004503907,0.0001965715,0.0003688202],"domain_scores_gemma":[0.9948551,0.004352447,0.0002434348,0.0002011351,0.0001540362,0.0001938219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009176612,0.000108109,0.8652143,0.01769393,0.0001364973,0.00001986158,0.01647034,0.01848914,0.0008864546,0.0664921,0.006613085,0.006958538],"study_design_scores_gemma":[0.004222822,0.0006302315,0.1009911,0.0001181964,0.00009719442,0.000004787542,0.001331687,0.06186873,0.0001596014,0.7751303,0.05458074,0.000864576],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3276754,0.0004893851,0.5642791,0.1039627,0.0001434458,0.002462841,0.0001046214,0.0008276097,0.00005484191],"genre_scores_gemma":[0.9707416,0.0001078657,0.01352434,0.01500508,0.0003033257,0.0002220166,0.00002536095,0.00002673607,0.00004361789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7642232,"threshold_uncertainty_score":0.9875695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5080193349386872,"score_gpt":0.5206454082899811,"score_spread":0.01262607335129384,"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."}}