{"id":"W633619851","doi":"","title":"CALIBRATING MECHANISTIC-EMPIRICAL PAVEMENT PERFORMANCE MODELS WITH AN EXPERT MATRIX","year":2001,"lang":"en","type":"article","venue":"","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subgrade; Pavement management; International Roughness Index; Pavement engineering; Empirical modelling; Computer science; Truck; Performance prediction; Range (aeronautics); Calibration; Probabilistic logic; Reliability engineering; Engineering; Environmental science; Structural engineering; Civil engineering; Surface finish; Simulation; Asphalt; Automotive engineering; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002037814,0.0002001204,0.0001431981,0.00007237406,0.0001121929,0.00005602707,0.0001388069,0.00005849907,0.000552381],"category_scores_gemma":[0.000001866532,0.0001613695,0.00001947358,0.0001880189,0.00001531276,0.001128064,0.00002346413,0.0001095443,0.00006190742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001222654,"about_ca_system_score_gemma":0.0000249716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000107829,"about_ca_topic_score_gemma":0.00001906767,"domain_scores_codex":[0.9987472,0.00001738329,0.0002668467,0.0002106285,0.0004058481,0.0003521274],"domain_scores_gemma":[0.9995266,0.00001302709,0.00002913869,0.0002699192,0.00004457875,0.000116742],"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":[0.00003591675,0.00006925753,0.002899946,0.00005226173,0.00003568496,0.000007570209,0.0009672082,0.98136,0.004376012,0.001652178,0.0005509266,0.007993019],"study_design_scores_gemma":[0.0004059496,0.0002709561,0.000221224,0.00002554604,0.000009598264,0.00001070714,0.0002756854,0.9878723,0.009968748,0.0001438843,0.0005382306,0.0002571764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6006747,0.00004302063,0.3919497,0.00004543908,0.0000856378,0.0002168239,9.646647e-7,0.0004873321,0.006496388],"genre_scores_gemma":[0.9664117,0.0001063299,0.03257766,0.0001996978,0.0001422099,0.00008252623,0.00002797931,0.00004719061,0.0004047685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3657369,"threshold_uncertainty_score":0.6580456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04804434688917752,"score_gpt":0.2979164207821793,"score_spread":0.2498720738930018,"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."}}