{"id":"W4320729441","doi":"10.3390/su15043429","title":"Geographies of Frontline Workers: Gender, Race, and Commuting in New York City","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Microdata (statistics); Workforce; Ethnic group; Census; Spatial mismatch; Demographic economics; Geography; Public use; American Community Survey; Inequality; Journey to work; Pandemic; Socioeconomics; Public transport; Business; Political science; Economic growth; Sociology; Coronavirus disease 2019 (COVID-19); Demography; Medicine; Population; 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":[0.0002698913,0.0001756129,0.0001771609,0.001113131,0.0009737724,0.000793911,0.0004194499,0.0002912084,0.004651316],"category_scores_gemma":[0.0009972431,0.0001408585,0.0002516619,0.001746838,0.0003795293,0.0006859726,0.001013868,0.0003960211,0.0003909158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259042,"about_ca_system_score_gemma":0.001084728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4010345,"about_ca_topic_score_gemma":0.6240068,"domain_scores_codex":[0.999813,0.00002661839,0.00001575084,0.00004159017,0.00004316737,0.00005986917],"domain_scores_gemma":[0.9992588,0.00007756582,0.000316629,0.00004533358,0.000139518,0.0001621712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003299107,0.00004068418,0.9911271,0.00002172011,0.00002006842,0.00006286716,0.002622527,0.00007242642,0.0002017723,0.0001229725,0.001959719,0.003715231],"study_design_scores_gemma":[0.000001314601,0.00001054466,0.9938823,0.00003128863,0.000005735953,0.00002571423,0.004902172,0.000134242,0.00003182624,0.0000281699,0.0009411551,0.000005491301],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959165,0.0002299642,0.00006128989,0.0002413933,0.00001042831,0.00001330704,0.002064248,0.00000433079,0.001458479],"genre_scores_gemma":[0.9969278,0.0002804043,0.0001060081,0.00005399317,0.00001210318,0.00003650776,0.001391953,0.000004528538,0.001186706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4010345,"threshold_uncertainty_score":0.7974004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03967650901054461,"score_gpt":0.3252443442068508,"score_spread":0.2855678351963062,"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."}}