{"id":"W4382318400","doi":"10.1609/aaai.v37i12.26667","title":"Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Walkability; Zoning; Constraint (computer-aided design); Computer science; Land use; Scale (ratio); Transport engineering; Built environment; Geography; Mathematics; Civil engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000521426,0.001148599,0.0004669949,0.0005712882,0.001569611,0.001408463,0.001143101,0.001487228,0.00521919],"category_scores_gemma":[0.001496329,0.0003141917,0.0007760951,0.002249439,0.001170482,0.0007198694,0.0008936201,0.0008774548,0.000211967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01252643,"about_ca_system_score_gemma":0.005579679,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7213129,"about_ca_topic_score_gemma":0.7985446,"domain_scores_codex":[0.9995961,0.0001247906,0.00001440079,0.00005490513,0.0000742401,0.0001356408],"domain_scores_gemma":[0.999236,0.0004499191,0.00005120329,0.00003276778,0.0001197425,0.0001103569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001069465,0.0001812114,0.003852888,0.0002201394,0.00003678088,0.001225211,0.000246172,0.9429169,0.0006181597,0.02886389,0.006930025,0.0148017],"study_design_scores_gemma":[0.00007798563,0.00009346911,0.003743155,0.00006652244,0.00003411097,0.000133371,0.001009851,0.9764292,0.0005973036,0.008179869,0.009605239,0.00002987663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7442479,0.004911314,0.1136303,0.003407142,0.000149215,0.0008181399,0.004053728,0.0004134353,0.1283687],"genre_scores_gemma":[0.9337572,0.001804573,0.04904455,0.0001233861,0.00002738147,0.0001793758,0.0014915,0.00006341816,0.01350863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2786871,"threshold_uncertainty_score":0.5606567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1125531167849433,"score_gpt":0.3673743098274609,"score_spread":0.2548211930425176,"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."}}