{"id":"W3006365049","doi":"10.1155/2020/7534970","title":"A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Department of Transportation","keywords":"Overfitting; Support vector machine; Operability; Relation (database); Performance prediction; Grey relational analysis; Computer science; Data mining; Predictive modelling; Artificial intelligence; Genetic algorithm; Machine learning; Engineering; Artificial neural network; Reliability engineering; Statistics; Simulation; 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":[0.0000963518,0.0000883614,0.000164606,0.0001581233,0.00002321473,0.000006807773,0.00002933048,0.00002619664,0.000004113078],"category_scores_gemma":[0.00000873253,0.00008302102,0.00006432346,0.000146799,0.00000481601,0.0003066882,3.528284e-7,0.0001285806,1.296661e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006068656,"about_ca_system_score_gemma":0.00001550161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.42529e-7,"about_ca_topic_score_gemma":0.000008298424,"domain_scores_codex":[0.9993084,0.000004066383,0.0003653041,0.00008271671,0.000141252,0.00009825076],"domain_scores_gemma":[0.9997162,0.00001517063,0.0001111337,0.00003744523,0.00006992603,0.00005014741],"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":[0.0002232015,0.000007629379,0.01566401,0.00008792717,0.00003988552,0.000003515605,0.0006420826,0.976813,0.003094689,0.00001226782,0.000007199822,0.003404637],"study_design_scores_gemma":[0.0008963861,0.0002141457,0.2607707,0.00003503558,0.0001053453,4.523612e-7,0.00002081275,0.7349123,0.002920998,0.00003331465,0.00003735252,0.00005314513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.675343,0.00002482138,0.3242922,0.00005301089,0.0001164263,0.0001067004,0.00004054329,0.00001463945,0.000008572359],"genre_scores_gemma":[0.9911293,0.00005831778,0.008577933,0.00005070028,0.00007074368,0.000008797049,0.0000892721,0.00001255056,0.000002404956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3157862,"threshold_uncertainty_score":0.3385499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007063309581817917,"score_gpt":0.2099589060826697,"score_spread":0.2028955965008518,"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."}}