{"id":"W4412710320","doi":"10.1016/j.ress.2025.111467","title":"Multi-fidelity modelling for uncertainty quantification of timber beam-column connections exposed to standard fire","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column (typography); Fidelity; High fidelity; Uncertainty quantification; Engineering; Environmental science; Computer science; Forensic engineering; Structural engineering; Machine learning; Connection (principal bundle); Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006567146,0.0002914575,0.0007973918,0.0002699895,0.0002257919,0.00008461664,0.0006117629,0.0002140731,0.00001165864],"category_scores_gemma":[0.007074789,0.0002575547,0.000308818,0.001245891,0.00005489277,0.0001487151,0.00007581808,0.0001815797,0.00001573789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00072641,"about_ca_system_score_gemma":0.0002511909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001260767,"about_ca_topic_score_gemma":0.00001459684,"domain_scores_codex":[0.9961057,0.0001243107,0.001836184,0.0008602412,0.0006705927,0.0004029179],"domain_scores_gemma":[0.9934859,0.003274028,0.0002254418,0.001400647,0.001439294,0.0001747192],"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":[0.0001606012,0.00005555201,0.0001335633,0.0005243451,0.00003494079,2.54251e-7,0.0001679305,0.988183,0.002230548,0.007638032,0.0003646782,0.0005065997],"study_design_scores_gemma":[0.0005433632,0.00006595186,0.0008267863,0.0003602245,0.00004348917,0.000001263266,0.0003041508,0.9897772,0.001265705,0.0002824145,0.006285402,0.0002440195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03744798,0.0001128392,0.9585178,0.000287057,0.001214105,0.001712843,0.0003282125,0.0003090441,0.00007011533],"genre_scores_gemma":[0.8888346,0.000004863457,0.110495,0.00001099718,0.00004511393,0.0002011382,0.00001693563,0.00002355821,0.0003678479],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8513866,"threshold_uncertainty_score":0.9999877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06253877276431374,"score_gpt":0.3162123995427192,"score_spread":0.2536736267784055,"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."}}