{"id":"W4378803027","doi":"10.1139/cjce-2023-0015","title":"Developing a machine learning-based approach for predicting road surface friction using dash camera images—a City of Edmonton, Canada, case study","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Road surface; Dash; Mean squared error; Computer science; Data collection; Machine learning; Measure (data warehouse); Artificial intelligence; Simulation; Data mining; Engineering; Statistics; Mathematics; Civil engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008236145,0.001313718,0.0006305901,0.002091025,0.001023706,0.001438524,0.001542137,0.0008020122,0.0008306073],"category_scores_gemma":[0.001904156,0.0004008288,0.0006723849,0.002422156,0.0004369614,0.0006095663,0.0005557267,0.0007382492,0.0003074674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009091199,"about_ca_system_score_gemma":0.006621209,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.924211,"about_ca_topic_score_gemma":0.9258748,"domain_scores_codex":[0.9996141,0.0000458718,0.00002471121,0.0001298514,0.00009538572,0.00009027959],"domain_scores_gemma":[0.9993425,0.0001912063,0.00004685735,0.00004682912,0.0003275274,0.00004519208],"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":[0.0002844618,0.0005504419,0.1096562,0.0002737488,0.0002474794,0.0007557015,0.0004094132,0.7171245,0.004281447,0.001060999,0.009662809,0.1556927],"study_design_scores_gemma":[0.0000201399,0.00002832755,0.02145766,0.00001664575,0.00002872994,0.00003203883,0.0003863597,0.9752725,0.001337438,0.0002193405,0.001178981,0.00002169041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9475958,0.000591773,0.03984867,0.0007314341,0.00004438513,0.0003965101,0.004898729,0.001361125,0.004531614],"genre_scores_gemma":[0.9358031,0.0003043517,0.05197299,0.00009237245,0.00001535875,0.0001256909,0.007658257,0.00005579201,0.003972102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07578903,"threshold_uncertainty_score":0.1524707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488540920599079,"score_gpt":0.2148157148314206,"score_spread":0.1999303056254298,"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."}}