{"id":"W4414598543","doi":"10.1139/cjce-2025-0138","title":"Relationship between built environment and metro ridership: machine learning analysis","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Fujian Province","keywords":"Nonlinear system; Gradient boosting; Built environment; Boosting (machine learning); Regression analysis; Flow (mathematics); Extreme value theory; Key (lock); Nonlinear programming","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001680514,0.0004692092,0.0004993791,0.001464887,0.0002499438,0.0007749401,0.0005282404,0.000671029,0.001380293],"category_scores_gemma":[0.004705091,0.000162431,0.0005498074,0.001035831,0.0002932151,0.0005185475,0.0004020891,0.0006499747,0.000280079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004200107,"about_ca_system_score_gemma":0.0004524448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009550825,"about_ca_topic_score_gemma":0.007941934,"domain_scores_codex":[0.9996207,0.0001665703,0.00002563822,0.00007758895,0.00005618293,0.0000533751],"domain_scores_gemma":[0.9976299,0.001805073,0.0001853031,0.0001095218,0.0001930392,0.00007722094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002260976,0.0005818332,0.5500343,0.00009235256,0.0004006577,0.0002011938,0.0001122644,0.3225843,0.0008173675,0.0009772639,0.001578952,0.1223935],"study_design_scores_gemma":[0.000004135448,0.00004514039,0.05732165,0.00001155495,0.00002714452,0.00003141363,0.00005794704,0.9413775,0.0002288255,0.0006541896,0.0002313678,0.000009107602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547452,0.0004640992,0.04229898,0.0004281534,0.00003281729,0.00003459493,0.0005472834,0.0002033596,0.001245566],"genre_scores_gemma":[0.995415,0.0001118808,0.003733306,0.00001572112,0.00001882402,0.00001151116,0.0003443417,0.000004375666,0.0003450232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009550825,"threshold_uncertainty_score":0.01899046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01089985087281805,"score_gpt":0.1920019110021678,"score_spread":0.1811020601293498,"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."}}