{"id":"W2133493492","doi":"10.1061/(asce)0733-9488(2006)132:4(247)","title":"Transportation Activity Centers for Urban Transportation Analysis","year":2006,"lang":"en","type":"article","venue":"Journal of Urban Planning and Development","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Metropolitan area; Urban agglomeration; Transport engineering; Regional science; Strengths and weaknesses; Geography; Economic geography; Urban structure; Center (category theory); Environmental planning; Business; Urban planning; Civil engineering; Engineering; Psychology","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":[0.001706104,0.0009784376,0.0008705631,0.005729211,0.001266301,0.004086876,0.001602494,0.001106497,0.09438881],"category_scores_gemma":[0.01116279,0.0003937844,0.001143852,0.01420891,0.0005107095,0.002792065,0.002306952,0.002419812,0.02710353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002070397,"about_ca_system_score_gemma":0.006032527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02362967,"about_ca_topic_score_gemma":0.01471332,"domain_scores_codex":[0.9977999,0.0008808685,0.0001723448,0.0003445334,0.0006118732,0.0001904204],"domain_scores_gemma":[0.9962502,0.0009407551,0.0004168129,0.0005142446,0.001728573,0.0001493059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009633031,0.0001051738,0.01078921,0.0007636524,0.00009331819,0.000153993,0.0005054871,0.01499721,0.0002360902,0.3807165,0.3319897,0.2595534],"study_design_scores_gemma":[0.00005028699,0.00004303777,0.0104647,0.0005782196,0.00005264215,0.0001684912,0.0008800176,0.02652366,0.0002561546,0.05767801,0.9032543,0.0000503343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01236406,0.008670419,0.4965437,0.004578935,0.002555992,0.003200638,0.1253263,0.005855849,0.3409041],"genre_scores_gemma":[0.2203085,0.01126615,0.4815234,0.0009671913,0.001253592,0.01136379,0.1425657,0.002315953,0.1284357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09438881,"threshold_uncertainty_score":0.315762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02258349683771039,"score_gpt":0.2909280642200653,"score_spread":0.2683445673823549,"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."}}