{"id":"W2525532055","doi":"10.1139/cjce-2015-0556","title":"Local calibration of flexible pavement performance models in Michigan","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Michigan Department of Transportation; U.S. Department of Transportation","keywords":"Resampling; Bootstrapping (finance); Calibration; Computer science; Sampling (signal processing); Statistic; Nonparametric statistics; Field (mathematics); Statistics; Econometrics; Algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001158962,0.0000972392,0.0001544716,0.0003276846,0.00001164903,0.000008521728,0.0001112786,0.00004838536,0.0000332598],"category_scores_gemma":[0.000008675446,0.00007903923,0.00003793952,0.0001248858,0.00001635565,0.0003249885,0.000003872729,0.0001155291,4.927149e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001696477,"about_ca_system_score_gemma":0.0001195992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001616369,"about_ca_topic_score_gemma":0.01143098,"domain_scores_codex":[0.9992855,0.000003437968,0.0003114646,0.00004817438,0.00009079313,0.0002606964],"domain_scores_gemma":[0.9996488,0.00001356533,0.00004094106,0.00008256685,0.00004558463,0.0001685037],"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":[0.000002088028,0.000001043691,0.001315582,0.00005009556,0.00001657508,0.0000175015,0.0003990226,0.98758,0.00717247,0.0005829257,0.00007050118,0.002792209],"study_design_scores_gemma":[0.001484324,0.0001843803,0.01752643,0.002783999,0.00002695489,0.0001320176,0.000355512,0.794417,0.1793322,0.0007807252,0.002379618,0.0005967843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5343958,0.0005179208,0.4633171,0.0000262604,0.001006306,0.00004713995,0.000004346301,0.00001630022,0.0006687992],"genre_scores_gemma":[0.9994644,0.00004011501,0.0003329292,0.000005315498,0.0001143033,0.000001642087,2.923873e-7,0.00002092726,0.00002007652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4650686,"threshold_uncertainty_score":0.6378757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006100100779880863,"score_gpt":0.1651651625380411,"score_spread":0.1590650617581602,"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."}}