{"id":"W3199407374","doi":"10.32866/001c.28107","title":"Hiking with Tobler: Tracking Movement and Calibrating a Cost Function for Personalized 3D Accessibility","year":2021,"lang":"en","type":"article","venue":"Findings","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Bespoke; Terrain; Function (biology); Walkability; Computer science; Preferred walking speed; Tracking (education); Simulation; Transport engineering; Environmental science; Physical medicine and rehabilitation; Engineering; Geography; Medicine; Business; Psychology; Physical activity; Cartography","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":[0.0006167011,0.0005433544,0.0003048646,0.001392926,0.0002481539,0.0006977122,0.0005545118,0.0004399027,0.0008908275],"category_scores_gemma":[0.004295672,0.000208014,0.0003996756,0.001170551,0.0003166017,0.0008704765,0.0004816274,0.0003173793,0.0002265223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016109,"about_ca_system_score_gemma":0.0005954525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07265969,"about_ca_topic_score_gemma":0.05973271,"domain_scores_codex":[0.999713,0.00009346311,0.00001588748,0.00008479533,0.00005590798,0.00003691843],"domain_scores_gemma":[0.9989703,0.0003707529,0.0001738709,0.0001746011,0.0002530661,0.00005751766],"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":[0.0002460865,0.000213345,0.2854192,0.0001784421,0.000219062,0.0002214767,0.0006581673,0.6000921,0.003690078,0.004229594,0.00188367,0.1029488],"study_design_scores_gemma":[0.00001059705,0.0001279433,0.1345432,0.00002800754,0.00005175741,0.00014973,0.0003870101,0.8593561,0.002208562,0.001479374,0.001614071,0.00004369619],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8873327,0.000107642,0.1089648,0.0000750378,0.00002399281,0.00008119482,0.0008435082,0.0003093436,0.002261713],"genre_scores_gemma":[0.9774454,0.0000440648,0.02139297,0.000009281642,0.000002982488,0.000034422,0.0006105427,0.00002210032,0.0004381848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07265969,"threshold_uncertainty_score":0.1444735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03893309837049915,"score_gpt":0.3073854085700848,"score_spread":0.2684523101995857,"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."}}