{"id":"W3213493478","doi":"10.32920/ryerson.14664759.v1","title":"Assessing pavement conditions and their effect on road safety: Ontario experience","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":"Ministère des Transports","keywords":"Transport engineering; International Roughness Index; Bayes' theorem; Pavement management; Highway maintenance; Computer science; Environmental science; Engineering; Surface finish; Bayesian probability; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001260553,0.000262812,0.0002603177,0.0003729424,0.001694749,0.0008214837,0.0004930813,0.0003559074,0.002023046],"category_scores_gemma":[0.005343684,0.0002394064,0.0002109392,0.0009264475,0.0009815464,0.0006432689,0.0006129856,0.0002695588,0.0002624647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0144463,"about_ca_system_score_gemma":0.01121156,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9525825,"about_ca_topic_score_gemma":0.9860073,"domain_scores_codex":[0.9988568,0.0001870211,0.0000402033,0.0001061888,0.0006728764,0.0001368888],"domain_scores_gemma":[0.9966211,0.0009873834,0.0002485796,0.0001769668,0.001654666,0.0003113094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009830932,0.0008219007,0.7075292,0.0003272602,0.0000977071,0.001557292,0.04574387,0.01263569,0.01142126,0.002000392,0.005908776,0.2109736],"study_design_scores_gemma":[0.00004461632,0.000847373,0.932543,0.00005469572,0.00005969993,0.0002438375,0.01722399,0.008885895,0.004517741,0.0005456894,0.03494817,0.00008524847],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905906,0.000173441,0.0004901307,0.0002419806,0.00000440229,0.00004393405,0.0001965678,0.00001451466,0.008244464],"genre_scores_gemma":[0.9930461,0.0004550614,0.0009357739,0.00003281405,0.00000285128,0.00001240125,0.0001343891,0.00001132552,0.005369311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04741752,"threshold_uncertainty_score":0.1048158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137365983153588,"score_gpt":0.2556312322488149,"score_spread":0.244257572417279,"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."}}