{"id":"W4380204711","doi":"10.1016/j.jth.2023.101623","title":"Getting groceries during the pandemic: How transit remained important despite the rise of e-delivery","year":2023,"lang":"en","type":"article","venue":"Journal of Transport & Health","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"University of Toronto","keywords":"Pandemic; Respondent; Tobit model; Public transport; Transit (satellite); Negative binomial distribution; Demography; Demographic economics; Coronavirus disease 2019 (COVID-19); Business; Advertising; Medicine; Economics; Political science; Econometrics; Statistics; Engineering; Transport engineering; Sociology; Mathematics","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":[0.00150272,0.0002271444,0.0005370801,0.0001580601,0.000232845,0.00001759636,0.0003802844,0.00008938013,0.00001775327],"category_scores_gemma":[0.00001401462,0.0001343495,0.000317402,0.0004952662,0.0001719863,0.0001699909,0.000004126534,0.0006600879,0.000001276539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000851149,"about_ca_system_score_gemma":0.0001734626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003994314,"about_ca_topic_score_gemma":0.0002968201,"domain_scores_codex":[0.9977065,0.00004909948,0.001161274,0.0001226867,0.0004808901,0.0004795948],"domain_scores_gemma":[0.998932,0.000121552,0.0004035832,0.000262325,0.0001165669,0.0001639598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009341738,0.0002037795,0.827902,0.006717819,0.001546848,0.001589837,0.03699043,0.07962964,0.0323323,0.001168932,0.003410635,0.007573617],"study_design_scores_gemma":[0.001633859,0.0003248153,0.9734825,0.0005915867,0.0002877087,0.0003582749,0.002273801,0.003511445,0.001548602,0.0004155674,0.01514066,0.0004311393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853934,0.005785594,0.004907109,0.002885167,0.0005048705,0.0002341213,0.00006966369,0.0001513183,0.00006874679],"genre_scores_gemma":[0.9878225,0.01134891,0.000335112,0.00009753175,0.0002638387,0.000003096493,0.000006299685,0.00005197295,0.00007079795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1455806,"threshold_uncertainty_score":0.5478612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02484230320437411,"score_gpt":0.221132136561247,"score_spread":0.1962898333568729,"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."}}