{"id":"W4365452865","doi":"10.1016/j.ijfatigue.2023.107680","title":"Probabilistic fatigue life prediction for RC beams under chloride environment considering the statistical uncertainty by Bayesian updating","year":2023,"lang":"en","type":"article","venue":"International Journal of Fatigue","topic":"Concrete Corrosion and Durability","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Markov chain Monte Carlo; Probabilistic logic; Bayesian inference; Bayesian probability; Statistical inference; Statistical model; Posterior probability; Monte Carlo method; Computer science; Mathematics; Statistics; Machine learning; Artificial intelligence","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":[0.0005913286,0.0001446811,0.0001812753,0.00008685912,0.00008705661,0.00008672332,0.0002812185,0.00005742152,0.0001731644],"category_scores_gemma":[0.0008239444,0.0001118942,0.0001031728,0.00007714534,0.00008170687,0.0001475313,0.00004638378,0.0002391644,0.000007341433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002568306,"about_ca_system_score_gemma":0.00007727197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001394042,"about_ca_topic_score_gemma":0.00000466315,"domain_scores_codex":[0.9984653,0.00004594485,0.0006474834,0.0001412058,0.0005044821,0.0001956132],"domain_scores_gemma":[0.9984983,0.000926872,0.0001558862,0.0001306012,0.000154593,0.0001337235],"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":[0.0001501514,0.00003747995,0.001542234,0.00006372225,0.0002423083,0.00001331612,0.0004425589,0.9611571,0.006112518,0.004407204,0.01725512,0.008576328],"study_design_scores_gemma":[0.001787143,0.0001837514,0.005758469,0.0002010297,0.00008537131,0.00006476812,0.001818374,0.9628041,0.002820798,0.008135117,0.01599949,0.0003415975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1889832,0.00009075723,0.8047065,0.002959603,0.002098938,0.0003785119,0.000419869,0.0001219203,0.0002407214],"genre_scores_gemma":[0.9981508,0.00007293716,0.001154318,0.0001389067,0.0003109116,0.00003325684,0.0000909091,0.00002360101,0.00002437056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8091676,"threshold_uncertainty_score":0.4562915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03519897960844042,"score_gpt":0.2777259812298654,"score_spread":0.242527001621425,"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."}}