{"id":"W4409799819","doi":"10.11159/icgre25.175","title":"Integrating Random FEM and CNN for Efficient Slope Stability Analysis with Spatially Variable Soil Properties","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stability (learning theory); Variable (mathematics); Finite element method; Random variable; Computer science; Statistics; Mathematics; Structural engineering; Engineering; Machine learning; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001522469,0.0002839711,0.0004333202,0.0002099454,0.0001276098,0.00007797076,0.0001766355,0.00005550059,0.000008015212],"category_scores_gemma":[0.00004724864,0.0001894507,0.0001060533,0.0004776049,0.0001068514,0.00008280739,0.00008917214,0.0002099612,7.725555e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006825448,"about_ca_system_score_gemma":0.000004719959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001846293,"about_ca_topic_score_gemma":0.00002334239,"domain_scores_codex":[0.9989921,0.000003045543,0.0002924649,0.0002885865,0.0001673246,0.0002565122],"domain_scores_gemma":[0.9996483,0.00007809589,0.0000591642,0.0001314374,0.00001982675,0.00006316027],"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":[0.00005993082,0.00001230442,0.001460969,0.0005399527,0.000539991,1.115179e-7,0.00007448251,0.9515349,0.04414413,0.0008373697,0.00001094022,0.0007849548],"study_design_scores_gemma":[0.0007301518,0.00003299905,0.00257932,0.0002974549,0.000481642,0.000001353054,0.0001296951,0.9510252,0.04432032,0.0000498875,0.0001099802,0.0002420171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900684,0.0004367534,0.008405752,0.0000682263,0.000150722,0.0003727292,0.00002769109,0.0001383278,0.0003313784],"genre_scores_gemma":[0.997875,0.00003539734,0.001814818,0.00001054415,0.00001499211,0.00005575754,0.000003844653,0.00002450472,0.0001650958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007806627,"threshold_uncertainty_score":0.7725577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003623184934336566,"score_gpt":0.1616225378265164,"score_spread":0.1579993528921799,"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."}}