{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001609608,0.0002435843,0.0006509341,0.0001719998,0.00005342142,0.0001733499,0.0002192041,0.0002394328,0.0001496764],"category_scores_gemma":[0.00006026552,0.0002248687,0.0003946486,0.0005076939,0.00007957719,0.00007097943,0.0001796431,0.0004556098,6.660399e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001821366,"about_ca_system_score_gemma":0.00008971098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002438055,"about_ca_topic_score_gemma":0.0002349146,"domain_scores_codex":[0.9987111,0.00002006294,0.0004608402,0.0003421327,0.0001903987,0.0002754789],"domain_scores_gemma":[0.9988901,0.00004634428,0.00008737777,0.0007589897,0.0001564236,0.000060784],"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":[0.000005950155,0.00001320873,0.01650464,0.0008301149,0.001136459,0.00001346545,0.000928612,0.9732457,0.004079945,0.00006400504,0.00008964796,0.003088259],"study_design_scores_gemma":[0.0004734002,0.00002069587,0.4445281,0.001015438,0.004512665,0.000002454467,0.001229982,0.426535,0.1108457,0.001822365,0.00702144,0.001992746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8307448,0.000571634,0.1597635,0.000008939401,0.001520844,0.0001016219,0.00003882437,0.0001946288,0.007055165],"genre_scores_gemma":[0.9912198,0.00007452586,0.00832165,0.000005097986,0.0001491238,0.00000989342,0.00008091253,0.00002534331,0.0001136837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5467107,"threshold_uncertainty_score":0.9169877,"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."}}