{"id":"W647723064","doi":"","title":"Evaluating the Potential of Transportation-Related Social Exclusion of Elderly People: An Application of a Joint Mode Choice and Travel Distance Demand Model in the National Capital Region (NCR) of Canada","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social exclusion; Travel survey; Empirical research; Travel behavior; Geography; Social capital; Demographic economics; Mode choice; Population; Mode (computer interface); Distance decay; Elderly people; Transport engineering; Public transport; Economics; Economic geography; Economic growth; Demography; Computer science; Gerontology; Engineering; Sociology; Medicine; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002756709,0.0009177944,0.00103039,0.001280749,0.001343983,0.002584566,0.002612861,0.001230355,0.00226513],"category_scores_gemma":[0.005766307,0.0005966662,0.001602803,0.00178166,0.001335988,0.001007354,0.002051252,0.001228553,0.0001842854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01505868,"about_ca_system_score_gemma":0.0121848,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9142944,"about_ca_topic_score_gemma":0.7972361,"domain_scores_codex":[0.9984192,0.0006575864,0.00003616427,0.0001966775,0.000150216,0.0005402196],"domain_scores_gemma":[0.9954822,0.002823425,0.0004377377,0.0001288736,0.0007039714,0.0004237877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003434523,0.0003982864,0.1424215,0.0001162921,0.0002901735,0.0008564095,0.0009070086,0.8130769,0.0005229728,0.02912534,0.001974912,0.009966762],"study_design_scores_gemma":[0.0000268691,0.00006492095,0.01400723,0.00001423759,0.00007466108,0.00004932137,0.0007785497,0.9821497,0.00006817452,0.002053368,0.0006804309,0.00003260995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677253,0.0005476391,0.02256828,0.0009830134,0.00002591376,0.0001755597,0.001110715,0.000064358,0.006799227],"genre_scores_gemma":[0.9916518,0.0003229436,0.003970883,0.00004520053,0.00001315572,0.00005189215,0.0004089939,0.00001572544,0.003519521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08570564,"threshold_uncertainty_score":0.1724207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06884186823983326,"score_gpt":0.4057073908580298,"score_spread":0.3368655226181966,"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."}}