{"id":"W3184100932","doi":"10.1061/jpeodx.0000305","title":"Modeling Pavement Performance Indices in Harsh Climate Regions","year":2021,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part B Pavements","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"International Roughness Index; Rut; Serviceability (structure); Environmental science; Forensic engineering; Cracking; Durability; Geotechnical engineering; Engineering; Civil engineering; Surface finish; Asphalt; Geography; Computer science; Cartography; Materials science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0002739243,0.0006084638,0.0003516486,0.0005814911,0.0003479717,0.0008519661,0.0006225974,0.0006587756,0.0007880371],"category_scores_gemma":[0.0006210127,0.0002890288,0.0005374861,0.0005747785,0.0002659198,0.0003639443,0.0003145399,0.0003420612,0.0001635671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001694958,"about_ca_system_score_gemma":0.0008697013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3667085,"about_ca_topic_score_gemma":0.3515734,"domain_scores_codex":[0.9998418,0.00002202763,0.00000476349,0.00004829945,0.00001583914,0.00006716064],"domain_scores_gemma":[0.9997706,0.00008329569,0.00004110867,0.00001872096,0.00005809655,0.00002830852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000263378,0.0000548136,0.0258554,0.0000136628,0.00002846796,0.00007122175,0.00002988238,0.9680701,0.001356261,0.0002099087,0.0002773462,0.004006624],"study_design_scores_gemma":[0.000004549147,0.00002013701,0.02772749,0.000003921438,0.00001406283,0.0000184698,0.00004914607,0.9713475,0.0003820975,0.00008622714,0.0003361732,0.00001035465],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846144,0.0001834689,0.01132185,0.00006470339,0.00001448055,0.00003197986,0.0008854534,0.000190439,0.002693271],"genre_scores_gemma":[0.9956474,0.00007582116,0.002836965,0.000007788448,0.000004647414,0.00001737714,0.0004462251,0.00001817193,0.0009455892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3667085,"threshold_uncertainty_score":0.729148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063395415082657,"score_gpt":0.210106394311709,"score_spread":0.1994724401608825,"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."}}