{"id":"W573080288","doi":"10.6135/ijprt.org.tw/2014.7(2).101","title":"Canadian calibration on Mechanistic - Empirical Pavement Design Guide to estimate International Roughness Index (IRI) using MTO data","year":2014,"lang":"en","type":"article","venue":"International Journal of Pavement Research and Technology","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Pavement management; International Roughness Index; Pavement engineering; Engineering; Civil engineering; Data collection; Computer science; Transport engineering; Statistics; Mathematics; Surface finish; Mechanical engineering; Asphalt","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003186458,0.001677905,0.0008530238,0.004363588,0.002019012,0.00171117,0.003503367,0.001163874,0.01329084],"category_scores_gemma":[0.006702184,0.001276184,0.001477071,0.00429932,0.0005425424,0.00135761,0.001295733,0.001749992,0.007543199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01066111,"about_ca_system_score_gemma":0.02223478,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.813263,"about_ca_topic_score_gemma":0.8803686,"domain_scores_codex":[0.9967673,0.0002210946,0.0001035022,0.0002797465,0.002409914,0.0002185215],"domain_scores_gemma":[0.9950672,0.0002191797,0.0001052499,0.0003707026,0.004167335,0.00007041978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002552678,0.0003723474,0.03425757,0.001104804,0.0002050732,0.0002511323,0.000413469,0.3133379,0.02110491,0.02451388,0.221482,0.3827016],"study_design_scores_gemma":[0.0002163173,0.0001776195,0.1206286,0.0008671096,0.0002610041,0.0003119937,0.0005059543,0.4941328,0.03778482,0.01020345,0.3343314,0.0005788658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05019307,0.002634372,0.7194176,0.000986462,0.000575126,0.001421313,0.06052689,0.01528611,0.148959],"genre_scores_gemma":[0.2451242,0.002951481,0.6563019,0.0003626751,0.00004472372,0.00129743,0.03978517,0.004285163,0.0498474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.186737,"threshold_uncertainty_score":0.3756734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07048645757682756,"score_gpt":0.4083744853791064,"score_spread":0.3378880278022789,"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."}}