{"id":"W6898719805","doi":"10.57702/blxhlecl","title":"Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto","year":2025,"lang":"en","type":"dataset","venue":"TIB Data Manager","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Walkability; Metropolitan area; Urban planning; Perspective (graphical); Physical activity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001098791,0.0004798774,0.000748204,0.0001861808,0.0002008749,0.0001545894,0.001474942,0.0002370646,0.0009725492],"category_scores_gemma":[0.0003967282,0.0004828943,0.00003913831,0.0003430617,0.0001058459,0.001518379,0.004348135,0.0002670493,0.00002270311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002298924,"about_ca_system_score_gemma":0.0001178488,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.114807,"about_ca_topic_score_gemma":0.079468,"domain_scores_codex":[0.996658,0.0003084134,0.0008682677,0.001332834,0.0005314545,0.0003010244],"domain_scores_gemma":[0.9912269,0.0002241191,0.0004525093,0.007793555,0.0001893634,0.0001135652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003674822,0.001045018,0.00004343315,0.0005572622,0.0003745782,0.000823838,0.00009136979,0.0004270147,6.493777e-8,0.000004661117,0.994888,0.001708014],"study_design_scores_gemma":[0.001295266,0.0001288106,0.0001378796,0.00007538206,0.001528086,0.0001701934,0.001282359,0.0198781,8.439613e-8,0.000006219421,0.9750145,0.0004830897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001094228,0.000938718,0.0005071766,0.0000147619,0.000194962,0.002163697,0.9958438,0.00008692146,0.0001405106],"genre_scores_gemma":[0.00009045017,0.0002429749,0.004780188,0.00002740435,0.00009294959,0.00006650374,0.9942802,0.00003800468,0.0003813689],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03533898,"threshold_uncertainty_score":0.9999407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04813225520020427,"score_gpt":0.3506551061840515,"score_spread":0.3025228509838472,"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."}}