{"id":"W4411167645","doi":"10.1016/j.compgeo.2025.107361","title":"Probabilistic characterization of inherent and epistemic geotechnical uncertainty in soil constitutive models using polynomial chaos expansion and monotonic drained triaxial tests","year":2025,"lang":"en","type":"article","venue":"Computers and Geotechnics","topic":"Geotechnical Engineering and Soil Mechanics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Vicerrectoría de Investigación, Creación e Innovación; Fondo Nacional de Desarrollo Científico y Tecnológico; Universidad de Chile; Agencia Nacional de Investigación y Desarrollo; Agenția Națională pentru Cercetare și Dezvoltare","keywords":"Polynomial chaos; Monotonic function; Probabilistic logic; Geotechnical engineering; Uncertainty quantification; Characterization (materials science); Polynomial; Constitutive equation; Mathematics; Applied mathematics; Geology; Structural engineering; Engineering; Monte Carlo method; Mathematical analysis; Materials science; Finite element method; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002358646,0.0002376059,0.0004383971,0.0002373977,0.00005618076,0.00003157773,0.0001007647,0.0002975564,3.236489e-7],"category_scores_gemma":[0.00005287425,0.0002461239,0.00003720884,0.0002733933,0.0001386339,0.0001018342,0.000163171,0.0003218676,6.077948e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009835863,"about_ca_system_score_gemma":0.00005791715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004770871,"about_ca_topic_score_gemma":0.000009397544,"domain_scores_codex":[0.9988669,0.00002856328,0.0004466696,0.0003127548,0.00009770306,0.0002474256],"domain_scores_gemma":[0.9995182,0.0001027201,0.00005969578,0.0002008346,0.00004051545,0.00007799844],"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":[0.00004707364,0.00003666139,0.000005934934,0.000394524,0.00002008899,0.000004577475,0.0001189342,0.9566485,0.02123824,0.004184944,0.000003627209,0.01729691],"study_design_scores_gemma":[0.0009267711,0.00007459817,0.0003385954,0.0005112583,0.0000276941,0.00001626416,0.00001701893,0.9947301,0.0005128462,0.002594946,0.00003421182,0.0002157556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.821241,0.0003372761,0.1775194,0.0000614891,0.0001781896,0.0004617645,0.0000153334,0.0001723016,0.00001326843],"genre_scores_gemma":[0.9988838,0.0004632787,0.000556915,0.00002031096,0.00001881471,0.00002487107,0.00001141071,0.00001707765,0.000003502555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1776428,"threshold_uncertainty_score":0.9999991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173452001102936,"score_gpt":0.2140315654564917,"score_spread":0.2022970454454623,"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."}}