{"id":"W2742926947","doi":"10.1139/cjce-2017-0145","title":"Macro-spatial approach for evaluating the impact of socio-economics, land use, built environment, and road facility on pedestrian safety","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Traffic and Road Safety","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Transport engineering; Macro; Built environment; Poison control; Recreation; Land use; Bayes' theorem; Geography; Computer science; Engineering; Bayesian probability; Statistics; Civil engineering; Mathematics; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000503668,0.0001717462,0.0002834914,0.00008517921,0.0002298115,0.00008874921,0.0002526662,0.00008825637,0.0000244644],"category_scores_gemma":[0.0001610941,0.0001337736,0.0001651869,0.00001506673,0.0000682579,0.0001762747,0.00001280306,0.0002504185,5.158863e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002403227,"about_ca_system_score_gemma":0.0001751295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003524345,"about_ca_topic_score_gemma":0.009135695,"domain_scores_codex":[0.9991751,0.00001485331,0.0003481917,0.0001045768,0.00007212247,0.0002851085],"domain_scores_gemma":[0.9991807,0.00009193922,0.0001562325,0.0002648011,0.0000271952,0.0002791019],"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":[0.00002879587,0.000004012381,0.02026102,0.00004486338,0.0001395116,0.000001847714,0.0002767665,0.9707184,0.0001110521,0.00001794787,0.00007387319,0.00832189],"study_design_scores_gemma":[0.0009433714,0.0001848109,0.5560141,0.0000396752,0.00004389001,0.00003248408,0.00004255522,0.4418369,0.00003724737,0.00002247372,0.0006096326,0.0001928914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9539013,0.0003946451,0.04453368,0.0000533476,0.0002555019,0.0002219038,0.0003030145,0.00001113311,0.0003254983],"genre_scores_gemma":[0.9992726,0.0001098098,0.0004237325,0.000002600961,0.0001429807,0.000003138833,0.000006937472,0.00002322418,0.00001490184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.535753,"threshold_uncertainty_score":0.5455127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03155648931249893,"score_gpt":0.2402695829581191,"score_spread":0.2087130936456202,"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."}}