{"id":"W4414076565","doi":"10.1080/17538947.2025.2548377","title":"Correlation and causality between traffic congestion and the built environment: a case study in New York city","year":2025,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Causality (physics); Correlation; Traffic congestion; Spatial correlation","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":[0.001822717,0.0003079608,0.0003098811,0.001271197,0.002466978,0.001307586,0.0009063762,0.0008161993,0.001824846],"category_scores_gemma":[0.006786704,0.0003043039,0.000415415,0.002885866,0.001691485,0.0009540661,0.001650734,0.001397688,0.00007907837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00580932,"about_ca_system_score_gemma":0.005151301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4473566,"about_ca_topic_score_gemma":0.6130134,"domain_scores_codex":[0.9988249,0.0006690241,0.00004792239,0.0001172051,0.000154897,0.000186145],"domain_scores_gemma":[0.9951233,0.003204783,0.0005507111,0.0002323017,0.0005200525,0.0003688255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001779425,0.001620681,0.9003701,0.0001875147,0.0002334825,0.02463293,0.02354795,0.01163086,0.0005671037,0.009242188,0.004496416,0.02329277],"study_design_scores_gemma":[0.0001058022,0.000447981,0.8122584,0.0002161799,0.0002275833,0.002779444,0.1114817,0.0602832,0.000630923,0.00410586,0.007347501,0.0001154124],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977527,0.00006460308,0.0004844136,0.0004064284,0.000005249236,0.00004688048,0.0001267762,0.000004558233,0.001108337],"genre_scores_gemma":[0.9984817,0.0001622352,0.0007488683,0.00004523836,0.000006627447,0.00004788487,0.0001061189,0.000003552464,0.0003978038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4473566,"threshold_uncertainty_score":0.8895053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03659308788780348,"score_gpt":0.3157279137259507,"score_spread":0.2791348258381472,"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."}}