{"id":"W4400521468","doi":"10.1139/cjce-2023-0568","title":"Local calibration of flexible performance models using maximum likelihood estimation approach","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Control Systems and Identification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Michigan Department of Transportation; U.S. Department of Transportation","keywords":"Calibration; Maximum likelihood; Computer science; Estimation; Statistics; Mathematics; Engineering","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":[0.0002167486,0.0001003725,0.0001625404,0.0004404054,0.00002571942,0.0000832061,0.00008939362,0.00006255422,0.00001838933],"category_scores_gemma":[0.000007975287,0.0001066212,0.00006378139,0.0002559402,0.00001065966,0.0006975327,0.000002653454,0.0001493286,9.226752e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002276018,"about_ca_system_score_gemma":0.00021196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004814201,"about_ca_topic_score_gemma":0.001283487,"domain_scores_codex":[0.9992322,0.000005943603,0.000384125,0.00006751253,0.000125531,0.0001847183],"domain_scores_gemma":[0.9996098,0.00001154677,0.00004413505,0.00009303429,0.00006804058,0.0001734057],"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":[9.34088e-7,0.000001522432,0.00001421846,0.00036559,0.00003938987,0.000004497815,0.000324326,0.9911147,0.00204032,0.0004302827,0.0001297653,0.005534505],"study_design_scores_gemma":[0.00008244926,0.00001418214,0.0001025231,0.000345617,0.00002869941,0.00008409693,0.00004438109,0.9979794,0.0008515472,0.0002139592,0.000156347,0.00009681623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04365795,0.003875514,0.9509082,0.000008263241,0.0008714191,0.00006909251,0.000005755158,0.00004159258,0.0005621709],"genre_scores_gemma":[0.9982772,0.00001582477,0.00152813,0.000001241998,0.0001262704,0.000002543851,0.000004024032,0.00003122088,0.00001349035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9546193,"threshold_uncertainty_score":0.4347888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252985648897544,"score_gpt":0.1838547030218347,"score_spread":0.1713248465328593,"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."}}