{"id":"W4416580114","doi":"10.1061/jccee5.cpeng-6699","title":"Machine Learning Surrogates for Unreinforced Masonry Tensile-Strength Prediction","year":2025,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Masonry and Concrete Structural Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Social Fund; Agencia Estatal de Investigación; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Surrogate model; Computational model; Parametric statistics; Masonry; Unreinforced masonry building; Context (archaeology); Predictive modelling; Sensitivity (control systems); Uncertainty quantification","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001246801,0.0004981618,0.0006886399,0.0005112333,0.0002281218,0.0008176635,0.0007623767,0.000903622,0.001015757],"category_scores_gemma":[0.003565688,0.0003033784,0.0006257941,0.0004899414,0.0005546943,0.000714432,0.0006944647,0.0008300972,0.0002693538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004623005,"about_ca_system_score_gemma":0.0006520809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001442904,"about_ca_topic_score_gemma":0.001034623,"domain_scores_codex":[0.9996074,0.0001480856,0.00002539536,0.00004961977,0.0001365551,0.00003298468],"domain_scores_gemma":[0.9984865,0.0008741842,0.0001832358,0.000148764,0.0002663293,0.00004091719],"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":[0.00001588886,0.00001541081,0.0003328606,0.00001663979,0.000004828345,0.00001718349,0.000007467916,0.9914461,0.0008037382,0.002522395,0.0001029925,0.004714313],"study_design_scores_gemma":[6.561839e-7,0.000006176243,0.00004253596,0.00000189661,5.980699e-7,0.00000265771,0.000001125941,0.9991128,0.0002796208,0.0004714833,0.00007921341,0.000001160761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0949537,0.0004334165,0.8996291,0.0002471872,0.0000618933,0.00004453074,0.0001871659,0.0004057125,0.004037217],"genre_scores_gemma":[0.9257778,0.0002941318,0.07189307,0.00006506221,0.00001962762,0.0001021248,0.0003383561,0.00004024124,0.001469501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001442904,"threshold_uncertainty_score":0.006593764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004636825391022684,"score_gpt":0.1982369616949456,"score_spread":0.1936001363039229,"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."}}