{"id":"W2929133625","doi":"10.1139/cjce-2018-0697","title":"Roughness prediction models using pavement surface distresses in different Canadian climatic regions","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"International Roughness Index; Elevation (ballistics); Environmental science; Distress; Surface roughness; Surface finish; Engineering; Materials science; Structural 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":[0.0004997751,0.0005744428,0.0002759997,0.0007605905,0.0005157145,0.0008112415,0.0009414514,0.0003750346,0.000802123],"category_scores_gemma":[0.001430697,0.0002743678,0.000631349,0.0008971312,0.0002876324,0.0003887292,0.00030375,0.0004137081,0.0001589213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006854666,"about_ca_system_score_gemma":0.00453326,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9361497,"about_ca_topic_score_gemma":0.9095046,"domain_scores_codex":[0.9997665,0.00002082034,0.00001207644,0.00007275716,0.00005392661,0.00007396786],"domain_scores_gemma":[0.9994969,0.0001222328,0.00005425397,0.00002221931,0.0002700925,0.00003425964],"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":[0.00005929545,0.00003412658,0.04925198,0.00001724204,0.00003969259,0.00004644346,0.00005183752,0.9390324,0.0008817413,0.0002934074,0.0003976715,0.00989417],"study_design_scores_gemma":[0.000008692838,0.00001952953,0.04864709,0.000004777166,0.00002151145,0.000009736167,0.00005811505,0.9503074,0.0005048517,0.00008236727,0.0003196071,0.00001634509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903333,0.0001038814,0.006200639,0.00006358388,0.000007938747,0.0000357157,0.001257999,0.0002296508,0.001767462],"genre_scores_gemma":[0.9959437,0.00007538287,0.002177897,0.000005197639,0.000001857593,0.00001510995,0.000870352,0.00001346799,0.000896949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06385028,"threshold_uncertainty_score":0.1284526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009689654845468512,"score_gpt":0.1795770226018765,"score_spread":0.169887367756408,"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."}}