{"id":"W3122402508","doi":"10.1016/j.amepre.2020.11.016","title":"Subway Ridership, Crowding, or Population Density: Determinants of COVID-19 Infection Rates in New York City","year":2021,"lang":"en","type":"article","venue":"American Journal of Preventive Medicine","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"New York Institute of Technology","funders":"Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University","keywords":"Crowding; Socioeconomic status; Demography; Per capita; Geography; Pandemic; Coronavirus disease 2019 (COVID-19); Population; Population density; Variables; Demographic economics; Medicine; Economics; Biology; Statistics; Disease; Sociology; Infectious disease (medical specialty); Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00257061,0.0001812282,0.00121708,0.000219042,0.00006504482,0.000005473005,0.0001256179,0.00005022416,0.000359724],"category_scores_gemma":[0.05019666,0.0001198886,0.0001551531,0.0008095095,0.0003356313,0.00009964997,0.00007744548,0.0002898742,9.031907e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003308274,"about_ca_system_score_gemma":0.000368005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004781864,"about_ca_topic_score_gemma":0.006225615,"domain_scores_codex":[0.9971071,0.0009128194,0.001185671,0.0002131931,0.0003560589,0.0002250834],"domain_scores_gemma":[0.9928828,0.00460078,0.00186753,0.0001687035,0.0002598698,0.0002203545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003114788,0.0001313958,0.9805308,0.0001447311,0.0001032829,0.0001067771,0.001677273,0.00002191551,0.0003179898,0.0002003259,0.002282946,0.01417112],"study_design_scores_gemma":[0.001771102,0.001740352,0.9316264,0.001016335,0.0002510803,0.0001380243,0.002571895,0.00005122897,0.001376861,0.05855765,0.0007216697,0.000177375],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797543,0.0004381086,0.01668553,0.002728062,0.0001867271,0.0001513654,0.000001566818,0.000012026,0.0000423093],"genre_scores_gemma":[0.9957268,0.000325444,0.002831531,0.0005597209,0.0002157715,0.000001879988,0.000001647715,0.0000114206,0.0003258137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05835733,"threshold_uncertainty_score":0.957804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2970790765962777,"score_gpt":0.4784752647071673,"score_spread":0.1813961881108896,"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."}}