{"id":"W4391264827","doi":"10.1016/j.jobe.2024.108650","title":"Data-driven model to predict the residual drift of precast concrete columns","year":2024,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Precast concrete; Residual; Hyperparameter; Computer science; Feature (linguistics); Variance (accounting); Selection (genetic algorithm); Process (computing); Feature selection; Data mining; Artificial intelligence; Engineering; Structural engineering; Algorithm","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":[0.0004116374,0.0001537057,0.0002501326,0.0002077728,0.00002919411,0.00007741206,0.0005527189,0.00006133347,0.000003816744],"category_scores_gemma":[0.0001128563,0.0001169124,0.00007444289,0.0002305396,0.00001557841,0.0003189117,0.0001160924,0.0004491211,9.409566e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008319041,"about_ca_system_score_gemma":0.00005296335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002645398,"about_ca_topic_score_gemma":0.00000105642,"domain_scores_codex":[0.9988838,0.000007348116,0.0004502745,0.0001170929,0.0002850322,0.0002564809],"domain_scores_gemma":[0.9993666,0.0001094431,0.00005341659,0.0003063999,0.00007631625,0.00008776039],"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":[0.000005890025,6.008243e-7,0.00002562217,0.0001409068,0.0001203401,0.00002315174,0.0003853815,0.9057987,0.08685289,0.0002667699,0.005571268,0.0008085179],"study_design_scores_gemma":[0.0001107572,0.00004533458,0.0001351531,0.0008062473,0.00006648606,0.0001087979,0.00004644768,0.9761027,0.01308417,0.00002915062,0.009335864,0.0001288773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5850537,0.00146305,0.4097623,0.0001326619,0.002979168,0.0001496184,0.00009539029,0.0001785926,0.0001854685],"genre_scores_gemma":[0.9665923,0.00007027433,0.03222442,0.000009251143,0.001021828,0.00000265673,0.000001729916,0.00005433257,0.00002318166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3815386,"threshold_uncertainty_score":0.4767548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701999629880408,"score_gpt":0.2490728049556566,"score_spread":0.2320528086568526,"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."}}