{"id":"W2898886412","doi":"10.1016/j.landurbplan.2018.10.008","title":"From walking buffers to active places: An activity-based approach to measure human-scale urban form","year":2018,"lang":"en","type":"article","venue":"Landscape and Urban Planning","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Scale (ratio); Population; Geography; Multidisciplinary approach; Urban planning; Measure (data warehouse); Exploratory research; Land use; Environmental planning; Computer science; Ecology; Cartography; Sociology; Data mining; Demography; Social science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005090767,0.0002364061,0.0003200861,0.0001373651,0.001114301,0.0002485382,0.000360187,0.000178306,0.00008151255],"category_scores_gemma":[0.00003387895,0.0002142548,0.00006411278,0.0003781722,0.0001327899,0.0005854945,0.00003750919,0.0002207475,0.00001188533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006330004,"about_ca_system_score_gemma":0.00008690112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001324281,"about_ca_topic_score_gemma":0.001752854,"domain_scores_codex":[0.9980367,0.0001167316,0.0001947456,0.0006600292,0.0004379516,0.0005538722],"domain_scores_gemma":[0.9989468,0.0001007035,0.00008434254,0.0002931708,0.00009155389,0.0004834136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004019836,0.0001525105,0.8573543,0.00001620425,0.00003381722,0.000004462806,0.1341032,0.00001319561,0.001989798,0.00005585534,0.003963784,0.001910918],"study_design_scores_gemma":[0.002038422,0.0007954978,0.9102759,0.0003268505,0.0001670878,5.120819e-7,0.03771205,0.0008219723,0.007071044,0.0005510611,0.03880906,0.001430502],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589138,0.00005514886,0.002404417,0.0001620036,0.0001635692,0.0004018557,0.00006057434,0.0001674063,0.03767124],"genre_scores_gemma":[0.9967785,4.656207e-7,0.001010309,0.0003879147,0.001345109,0.00003319476,0.00007337129,0.00002427567,0.0003468286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09639113,"threshold_uncertainty_score":0.8737059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03721765994789423,"score_gpt":0.3071284011273328,"score_spread":0.2699107411794385,"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."}}