{"id":"W4401839013","doi":"10.1016/j.engstruct.2024.118725","title":"An improved method for statistics estimation of out-of-plane load resistance of masonry walls using design-code models and mechanics-based finite element models","year":2024,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Masonry and Concrete Structural Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Masonry; Finite element method; Structural engineering; Code (set theory); Engineering; Structural mechanics; Computer science; Mathematics; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0002459762,0.0002244797,0.0003869392,0.0001462912,0.00002226368,0.00002508421,0.0001237465,0.00009235989,0.0000102842],"category_scores_gemma":[0.00004211378,0.0002142825,0.00006778539,0.000139296,0.00001403529,0.0001693914,0.00001229359,0.0001017259,2.437408e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004906464,"about_ca_system_score_gemma":0.0000409844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002347429,"about_ca_topic_score_gemma":0.000005784359,"domain_scores_codex":[0.9989345,0.00002443605,0.0004492905,0.0002133891,0.0001891406,0.0001892576],"domain_scores_gemma":[0.9992083,0.0003571315,0.00008310159,0.0002122975,0.00008637024,0.00005281159],"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":[0.00003775138,0.000001365181,9.193165e-8,0.001624343,0.00015018,9.055845e-7,0.0003520097,0.8780572,0.1048959,0.01205541,0.000005401118,0.002819475],"study_design_scores_gemma":[0.0001758341,0.000053883,0.000001230464,0.0001174436,0.0001840151,7.965634e-7,0.00001706624,0.8853059,0.09687395,0.01708353,0.000005887692,0.00018045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01116902,0.0007928435,0.986781,0.000004103975,0.0001681343,0.0002419408,0.0007495235,0.00009076826,0.000002658287],"genre_scores_gemma":[0.5041328,0.00001150872,0.4957747,0.000001457928,0.00001504425,0.000006664468,0.00003199935,0.00002323939,0.000002639855],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4929638,"threshold_uncertainty_score":0.8738188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02098683889562519,"score_gpt":0.268436597804058,"score_spread":0.2474497589084328,"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."}}