{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006592953,0.0001070074,0.0001695532,0.00002697143,0.0008027861,0.0002952958,0.00008877148,0.00007434798,0.0003054253],"category_scores_gemma":[0.0001194685,0.00009549638,0.00005229709,0.0002419777,0.0001407445,0.0006387711,0.00002006098,0.0001021121,6.387878e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009666556,"about_ca_system_score_gemma":0.0001819421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004069132,"about_ca_topic_score_gemma":0.003869496,"domain_scores_codex":[0.9987968,0.0000514308,0.0001985029,0.0003961409,0.0002617163,0.0002954717],"domain_scores_gemma":[0.999459,0.0001316182,0.00008273289,0.0001056617,0.0001317722,0.00008924782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001321431,0.00006450748,0.9666853,0.0001045461,0.00003046042,0.000004831915,0.01349142,0.00000277494,0.001322666,0.002927543,0.00008103152,0.01515281],"study_design_scores_gemma":[0.00272624,0.0001318111,0.93599,0.0002826568,0.0001780306,8.319312e-7,0.02005721,0.0005890345,0.004027924,0.005446708,0.02998083,0.0005887024],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849363,0.0001640579,0.01168079,0.0004822527,0.0001310456,0.000453328,0.0000186964,0.00007187961,0.00206163],"genre_scores_gemma":[0.9964783,0.000007312967,0.00183356,0.0003559302,0.000158391,0.00005797121,0.00002909187,0.00001114528,0.001068321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03069524,"threshold_uncertainty_score":0.6174463,"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."}}