{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002451299,0.0007573719,0.0004069754,0.0009991975,0.0004225272,0.0009036353,0.001179198,0.0008133798,0.002703142],"category_scores_gemma":[0.006860931,0.0005902076,0.0004735164,0.0005453377,0.0003650958,0.0008171296,0.0007077078,0.0006749913,0.000708127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001448563,"about_ca_system_score_gemma":0.001130916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01262647,"about_ca_topic_score_gemma":0.01196334,"domain_scores_codex":[0.999118,0.0002956065,0.00005461041,0.0001690072,0.0002862413,0.00007646547],"domain_scores_gemma":[0.9968636,0.001609956,0.0003007859,0.0004703973,0.0007129621,0.00004229616],"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":[0.00002034857,0.00004817553,0.001315093,0.00002552909,0.00001401286,0.00001422004,0.00004136582,0.9769377,0.001493759,0.001283016,0.0001732158,0.01863357],"study_design_scores_gemma":[0.000006127741,0.00002557305,0.0008113752,0.000007336877,0.00000484601,0.000008904663,0.00001679591,0.9963858,0.001238174,0.0007667099,0.0007187665,0.000009660372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2219483,0.0000558012,0.7645044,0.00009148854,0.00001985792,0.0003209575,0.0005242528,0.001390079,0.01114489],"genre_scores_gemma":[0.8292103,0.00007855942,0.1675844,0.00003371363,0.000006743574,0.0003112067,0.0005497637,0.0001215903,0.002103817],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01262647,"threshold_uncertainty_score":0.02510595,"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."}}