{"id":"W4412902429","doi":"10.1016/j.compenvurbsys.2025.102331","title":"Wheelchair accessibility to public facilities via transits and analysis of delay factors—A case study of Shanghai, China","year":2025,"lang":"en","type":"article","venue":"Computers Environment and Urban Systems","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Tongji University; National Natural Science Foundation of China","keywords":"China; Geography; Wheelchair; Transport engineering; Public transport; Engineering; Political science; Archaeology","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":[0.000505617,0.0006254677,0.0004043221,0.002493898,0.001124291,0.0009727933,0.0006494398,0.000400617,0.001160569],"category_scores_gemma":[0.0007930067,0.0003099683,0.0007912574,0.00381616,0.0009114702,0.0004493418,0.0006213774,0.0002573829,0.000100366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005536743,"about_ca_system_score_gemma":0.003231182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4991373,"about_ca_topic_score_gemma":0.5007091,"domain_scores_codex":[0.9995852,0.00008396152,0.00002716541,0.00005902424,0.00006625283,0.0001784235],"domain_scores_gemma":[0.9992821,0.0002046543,0.0001465866,0.00004294944,0.0001622296,0.0001614784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001307829,0.0002527235,0.9748687,0.0001095711,0.000144612,0.007414316,0.005824516,0.003566565,0.001227275,0.0008020002,0.0003497443,0.005309102],"study_design_scores_gemma":[0.00001394583,0.000206586,0.9735466,0.00002468375,0.0001784945,0.0006612139,0.01722682,0.006703197,0.0005025915,0.0001203395,0.0007827806,0.00003270209],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995722,0.00004676222,0.0000623653,0.00001420772,9.190118e-7,0.000005697984,0.00007371577,0.000002147862,0.0002219035],"genre_scores_gemma":[0.999472,0.00006202099,0.00006355404,0.000004151878,0.000001500893,0.00000373006,0.0001014775,0.000001016922,0.0002906326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4991373,"threshold_uncertainty_score":0.9924641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0234727439796589,"score_gpt":0.2617289956638156,"score_spread":0.2382562516841567,"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."}}