{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002898782,0.0001699925,0.000160097,0.0001096951,0.0001646109,0.0000636523,0.0001493775,0.00003140408,0.0004996046],"category_scores_gemma":[0.00001674932,0.0001181058,0.00004039533,0.0001432118,0.00004438447,0.0001499055,0.00006356198,0.000202726,6.791094e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00014382,"about_ca_system_score_gemma":0.00002911305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000374318,"about_ca_topic_score_gemma":0.0008076997,"domain_scores_codex":[0.9989443,0.00006843794,0.0002267199,0.0001922025,0.0004044582,0.0001638839],"domain_scores_gemma":[0.9994502,0.000219672,0.00009768317,0.0001247574,0.00005282478,0.00005488294],"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":[0.001648404,0.000226166,0.481071,0.0001136768,0.002367595,0.00006121709,0.005511058,0.3589942,0.004088312,0.005084265,0.133166,0.00766806],"study_design_scores_gemma":[0.002570427,0.001064475,0.2000647,0.000158267,0.0004428234,0.00006978428,0.003257366,0.7744844,0.002668107,0.0005158978,0.01372181,0.0009819409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4617738,0.0004448117,0.5353297,0.0006333346,0.000495765,0.0005063092,0.0001281804,0.0001129203,0.0005751983],"genre_scores_gemma":[0.9973731,0.00003197981,0.000958717,0.00008242735,0.000246618,0.0000770172,0.00005709917,0.00002348997,0.001149533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5355993,"threshold_uncertainty_score":0.5470319,"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."}}