{"id":"W2877075567","doi":"10.1520/acem20170115","title":"Bayes Linear Regression Performance Model Depending on Experts’ Knowledge and Current Road Condition","year":2018,"lang":"en","type":"article","venue":"Advances in Civil Engineering Materials","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Pavement management; Bayesian network; Bayes' theorem; Regression analysis; Protocol (science); Function (biology); Set (abstract data type); Regression; Linear regression; Predictive modelling; Bayesian probability; Transport engineering; Operations research; Engineering; Machine learning; Artificial intelligence; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001418425,0.0002521499,0.0002461488,0.000176224,0.00006428227,0.00003215407,0.000106752,0.00007811707,0.00002517954],"category_scores_gemma":[0.0000343844,0.0002247633,0.0000182495,0.00009998099,0.00003332081,0.0004922063,0.00004360737,0.0001198341,0.00001165704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000978462,"about_ca_system_score_gemma":0.000006882724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.894287e-7,"about_ca_topic_score_gemma":0.000006312662,"domain_scores_codex":[0.9990302,0.000009757954,0.0002738597,0.0002324187,0.0001046894,0.0003490762],"domain_scores_gemma":[0.9996841,0.0000288147,0.0000358004,0.0001683061,0.00002960386,0.00005338583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005507303,0.00002322537,0.0004133546,0.001027156,0.00001332308,0.000005450855,0.001159982,0.5935462,0.3410597,0.000911386,0.0001885394,0.06159664],"study_design_scores_gemma":[0.0004697181,0.00008653157,0.001548639,0.002172066,0.000006961404,0.00001028197,0.00002723777,0.4574132,0.5313823,0.0001681172,0.00623188,0.0004830418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809144,0.00345433,0.009983204,0.000002555593,0.004231173,0.0001485203,0.00001236704,0.0002899471,0.0009634941],"genre_scores_gemma":[0.9943481,0.003274155,0.001450981,0.000004405167,0.0007971504,0.00005285793,0.00001059922,0.00005024288,0.00001150373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1903226,"threshold_uncertainty_score":0.9165581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008371177118830491,"score_gpt":0.2687092409093353,"score_spread":0.2603380637905048,"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."}}