{"id":"W4255784158","doi":"10.32920/ryerson.14653059.v1","title":"Reliability analysis of pedestrian crossing sight distance","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sight; Reliability (semiconductor); Pedestrian; Braking distance; Standard deviation; Margin (machine learning); Statistics; Probabilistic logic; Computer science; Mathematics; Simulation; Engineering; Transport engineering; Automotive engineering","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":[0.001844114,0.0007199901,0.0006748324,0.001982573,0.0001873621,0.0004729692,0.0006191546,0.0004616323,0.0009140758],"category_scores_gemma":[0.009002914,0.0002910779,0.001024536,0.0008763106,0.0003111775,0.0005812889,0.0004391967,0.0004497414,0.0002443735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004633924,"about_ca_system_score_gemma":0.000351697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00107421,"about_ca_topic_score_gemma":0.0006193518,"domain_scores_codex":[0.997588,0.0006682115,0.000116161,0.0004566347,0.001063305,0.0001076568],"domain_scores_gemma":[0.9921257,0.004465323,0.001072238,0.0006652649,0.001576593,0.00009482135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006175463,0.00008333791,0.02635676,0.0006045811,0.0002682034,0.0003468289,0.0003754853,0.7959672,0.04017509,0.00766382,0.0005220592,0.1270189],"study_design_scores_gemma":[0.00001525781,0.0007545448,0.0317168,0.00004450649,0.000134207,0.0005042047,0.0000740515,0.9395619,0.02117993,0.004630345,0.001304442,0.00007977759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2105275,0.0007785721,0.7862974,0.00004230452,0.00003573633,0.00005904092,0.000211398,0.000513067,0.00153503],"genre_scores_gemma":[0.964209,0.000234224,0.03471374,0.000007076319,0.00002089464,0.00005367808,0.0001927626,0.0000469675,0.0005215818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001982573,"threshold_uncertainty_score":0.009752691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007921824945017357,"score_gpt":0.2361858141802039,"score_spread":0.2282639892351865,"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."}}