{"id":"W4210577251","doi":"10.1057/s41289-022-00178-w","title":"Street network or functional attractors? Capturing pedestrian movement patterns and urban form with the integration of space syntax and MCDA","year":2022,"lang":"en","type":"article","venue":"URBAN DESIGN International","topic":"Urban Design and Spatial Analysis","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Space syntax; Pedestrian; Computer science; Space (punctuation); Syntax; Urban design; Street network; Representation (politics); Urban planning; Geography; Artificial intelligence; Transport engineering; Civil engineering; Engineering","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":[0.0003998069,0.0003352641,0.0005454061,0.001656059,0.0002723797,0.001593022,0.0005390492,0.0003998997,0.002071694],"category_scores_gemma":[0.003493971,0.0003692227,0.000442808,0.001723854,0.0006443844,0.001908708,0.001002705,0.0003881971,0.0002112723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005208054,"about_ca_system_score_gemma":0.0004586457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01279668,"about_ca_topic_score_gemma":0.02371564,"domain_scores_codex":[0.99985,0.00006542596,0.000008064952,0.00003795483,0.00001928268,0.00001909659],"domain_scores_gemma":[0.999366,0.0003232342,0.000108226,0.00007100256,0.00007794835,0.00005373322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002235689,0.0001075458,0.1570482,0.0002346025,0.0002288456,0.0002042083,0.001143678,0.6467717,0.004478847,0.08671517,0.002385244,0.1004585],"study_design_scores_gemma":[0.000005501789,0.00001319259,0.01374486,0.00002073725,0.00001676298,0.00003345679,0.000403519,0.9643174,0.0002357782,0.0200539,0.001141436,0.00001335669],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6093078,0.0007052387,0.3827398,0.0005874938,0.00004997332,0.00005304865,0.001181205,0.0004067703,0.004968777],"genre_scores_gemma":[0.9635982,0.0001337749,0.03556287,0.00001869902,0.00001122031,0.00002430087,0.0002210959,0.00002937984,0.0004004376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01279668,"threshold_uncertainty_score":0.02544439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0219171189640498,"score_gpt":0.1913805091439119,"score_spread":0.1694633901798621,"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."}}