{"id":"W4361275016","doi":"10.1016/j.trip.2023.100815","title":"Understanding the travel challenges and gaps for older adults during the COVID-19 outbreak: Insights from the New York City area","year":2023,"lang":"en","type":"article","venue":"Transportation Research Interdisciplinary Perspectives","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; University Transportation Centers; Paralyzed Veterans of America; New York University; U.S. Department of Transportation","keywords":"TRIPS architecture; Pandemic; Travel behavior; Context (archaeology); Descriptive statistics; Business; Population; Gerontology; Coronavirus disease 2019 (COVID-19); Psychology; Geography; Demographic economics; Medicine; Environmental health; Transport engineering; Engineering; Economics; Disease","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000976089,0.0002328548,0.0002808751,0.0009379618,0.001345277,0.001178983,0.0003951969,0.0006404821,0.001598007],"category_scores_gemma":[0.002712856,0.0001815742,0.0003485044,0.001144701,0.0004544446,0.00195331,0.001660011,0.001018462,0.0001742507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001546612,"about_ca_system_score_gemma":0.00270737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1216204,"about_ca_topic_score_gemma":0.2966366,"domain_scores_codex":[0.9996496,0.0001086598,0.00003820471,0.0000483969,0.00004869241,0.0001062722],"domain_scores_gemma":[0.9987015,0.0003402414,0.0003977414,0.00003709424,0.0002516877,0.0002718026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00009931549,0.0002374303,0.8096038,0.0004982796,0.00005475587,0.001230099,0.1494962,0.0002678681,0.0007421944,0.0003953092,0.00543431,0.03194033],"study_design_scores_gemma":[0.000004774264,0.0001104962,0.7120072,0.0003549512,0.00002300458,0.000228516,0.2804747,0.0005498211,0.0000821143,0.0002278657,0.005913175,0.00002343092],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957914,0.0005077118,0.0001174357,0.001899864,0.00001392578,0.00003286628,0.0005094184,0.000002542226,0.001124733],"genre_scores_gemma":[0.9956412,0.001508994,0.0004470476,0.0007114999,0.00003146337,0.00008741568,0.0008003547,0.000004131838,0.0007677947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1216204,"threshold_uncertainty_score":0.241825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2493448693795864,"score_gpt":0.4214656365077563,"score_spread":0.1721207671281699,"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."}}