{"id":"W2252812103","doi":"10.1007/s11431-011-4603-x","title":"A numerical method for structural uncertainty response computation","year":2011,"lang":"en","type":"article","venue":"Science in China. Series E, Technological sciences/Science in China. Series E, Technological Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Finite element method; Cumulative distribution function; Probability density function; Applied mathematics; Mathematics; Random variable; Probability distribution; Numerical integration; Monte Carlo method; Hessian matrix; Mathematical optimization; Algorithm; Mathematical analysis; Structural engineering; Statistics; Engineering","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":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.06844983,0.001242631,0.001741837,0.005363324,0.005791975,0.002254626,0.02005642,0.00101385,0.0002777469],"category_scores_gemma":[0.07284761,0.0007764077,0.0003608307,0.05721559,0.09044141,0.008054717,0.003465389,0.001865071,0.00004609152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232078,"about_ca_system_score_gemma":0.002560148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005769467,"about_ca_topic_score_gemma":0.0002909789,"domain_scores_codex":[0.9784133,0.0008528553,0.002936231,0.006115328,0.007099659,0.004582624],"domain_scores_gemma":[0.9921837,0.003344129,0.001150623,0.001816538,0.0007400124,0.0007649286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001674105,0.001037735,0.05429372,0.00005319495,0.000009550662,0.0002597597,0.00305796,0.1186111,0.03015506,0.6243756,0.0001985772,0.1662735],"study_design_scores_gemma":[0.000613094,0.003622527,0.1564731,0.0001236379,0.000009757824,0.0003963869,0.006009966,0.155219,0.008205654,0.6673538,0.0005367345,0.001436374],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.877643,0.000190783,0.1041517,0.008432301,0.001404874,0.0022955,0.00004436334,0.001516699,0.004320743],"genre_scores_gemma":[0.768955,0.00005178114,0.2303431,0.0001449538,0.00003511549,0.0003192594,0.000001538924,0.00001937804,0.0001298429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1648372,"threshold_uncertainty_score":0.9994687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09859402596473928,"score_gpt":0.3840179884844305,"score_spread":0.2854239625196913,"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."}}