{"id":"W4403093595","doi":"10.1016/j.compenvurbsys.2024.102199","title":"A specialized inclusive road dataset with elevation profiles for realistic pedestrian navigation using open geospatial data and deep learning","year":2024,"lang":"en","type":"article","venue":"Computers Environment and Urban Systems","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Geospatial analysis; Pedestrian; Elevation (ballistics); Geography; Cartography; Open data; Computer science; Remote sensing; World Wide Web; Engineering; Archaeology","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":[0.0002572241,0.001580947,0.0007788542,0.001481027,0.0005432177,0.0006567236,0.001996513,0.001987997,0.009856537],"category_scores_gemma":[0.001181974,0.0004459031,0.001355616,0.001906549,0.0003742499,0.000729431,0.001188923,0.001245044,0.01122622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005778609,"about_ca_system_score_gemma":0.001453273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05485051,"about_ca_topic_score_gemma":0.1408228,"domain_scores_codex":[0.9996343,0.00005148036,0.00002152436,0.0001168668,0.0001005977,0.00007520447],"domain_scores_gemma":[0.9994645,0.00006344481,0.0000292339,0.0001564952,0.0002050924,0.00008137995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007394592,0.00137215,0.02253858,0.001273012,0.0004474956,0.001221656,0.0002382901,0.09694379,0.005524916,0.002056572,0.7478545,0.1197896],"study_design_scores_gemma":[0.0006141437,0.0004717392,0.08210517,0.0007248835,0.0004163152,0.0019314,0.001670569,0.4316179,0.01624563,0.008686969,0.4550914,0.0004237703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1039607,0.000551616,0.02926076,0.0005865846,0.0006058593,0.0002916077,0.8380533,0.01519728,0.01149224],"genre_scores_gemma":[0.08900321,0.0002265305,0.02088367,0.0002058325,0.00005143929,0.0002164589,0.8843441,0.0004853429,0.004583473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05485051,"threshold_uncertainty_score":0.1090625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843175439878623,"score_gpt":0.3216218714072888,"score_spread":0.2731901170085026,"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."}}